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Characterization of fatigue damage mechanism of asphalt mixtures with acoustic emission
Construction and Building Materials 240 (2020) 117961
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Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
Characterization of fatigue damage mechanism of asphalt mixtures with acoustic emission Xin Qiu a,⇑, Jingxian Xu a, Wenyi Xu a,⇑, Shanglin Xiao a, Feng Wang b, Jie Yuan c a
College of Engineering, Road and Traffic Engineering Institute, Zhejiang Normal University, 688 Yingbin Road, Jinhua, Zhejiang 321004, China Ingram School of Engineering, Texas State University, San Marcos, Texas, United State c Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, China b
h i g h l i g h t s Acoustic emission could characterize the fatigue damage behavior of asphalt mixtures. AE energy and amplitude b-value could reflect the stage features of damage evolution. The distribution of AE event is related to the microstructure of mix and loading mode. The Felicity ratio could measure the irreversibility of damage evolution. The wavelet scalogram of AE waveforms could identify the damage mechanism.
a r t i c l e
i n f o
Article history: Received 6 September 2019 Received in revised form 21 December 2019 Accepted 24 December 2019
Keywords: Asphalt mixture Acoustic emission Damage stage characteristic Kaiser effect Wavelet scalogram
a b s t r a c t Understanding the fatigue damage mechanism of asphalt mixtures is essential to prolong the service life of asphalt pavements. Acoustic emission (AE) technique can effectively detect minor damage and evaluate material mechanical properties. The objective of this paper was to investigate the temporal-spatial variation of AE parameters associated with fatigue damage behavior of asphalt mixtures and characterize the irreversibility of damage evolution utilizing the Kaiser effect. Firstly, the semi-circular bending (SCB) tests with different rest time and AE tests were carried out simultaneously to discuss the AE characteristics of fatigue damage behavior of asphalt mixtures. Secondly, the spatial evolution of AE events was analyzed to correlate with the crack propagation path of asphalt mixtures. Finally, the Felicity ratio (RF) and wavelet scalogram were utilized to reveal the irreversible characteristics of the AE process of asphalt mixtures. The results show that the ‘‘blank area” in AE energy temporal, representing the intensity of AE activities in asphalt mixtures during the process of microcracks concentration, gradually disappears with the increase of rest time. The inflection point of the decline of AE amplitude b-value could be considered as a precursor to the rupture of asphalt mixtures. The spatial distribution of AE events from disorder to order reflects the influence of the microstructures of asphalt mixtures and loading mode on crack propagation. The change of RF could be divided into the three stages, corresponding to the deformation and compaction stage where 1 RF, microcracks initiation and concentration stage where 0.4 RF < 1, and macrocracks extension stage where RF < 0.4. The Kaiser effect mainly exists at the stage of deformation and compaction of asphalt mixtures. After the occurrence of macrocracks, the AE process would lose irreversible characteristics. The damage mechanisms related to the Kaiser effect and the Felicity effect could be identified by the energy distribution of AE waveforms. Ó 2020 Elsevier Ltd. All rights reserved.
1. Introduction Fatigue cracking is one of the major distresses in asphalt pavements. To delay the deterioration of pavement performance, pre⇑ Corresponding authors. E-mail addresses: [email protected] (X. Qiu), [email protected] (W. Xu), [email protected] (F. Wang), [email protected] (J. Yuan). https://doi.org/10.1016/j.conbuildmat.2019.117961 0950-0618/Ó 2020 Elsevier Ltd. All rights reserved.
ventive maintenance is considered effective in dealing with the premature damage hidden in asphalt pavements, depending on the understanding of fatigue damage evolution process and fracture mechanism of asphalt mixtures, as well as the detection and diagnosis of cracking position and severity. Generally, the fatigue damage of asphalt mixtures originates from the initiation of microcracks, caused by the movement of primary pores and micro defects distributed in asphalt mixtures under the external forces.
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The concentration and development of microcracks seriously affects the pavement performance of asphalt mixtures, promotes the formation of macrocracks and eventually leads to the deterioration of material properties. In addition, due to the viscoelastic properties of asphalt mixture, its mechanical behavior is variable with external environment and vehicle loading. Consequently, it is of great necessity to monitor the crack growth and measure the damage accumulation in asphalt pavements by modern technologies. In laboratory tests, the relationship between structural changes and mechanical properties of asphalt mixtures has been extensively studied in various approaches. For instance, the simulation methods represented by finite element and boundary element have been applied to the fracture modeling of asphalt mixtures through numerical calculation and test [1,2]. CT and scanning laser have been widely used to capture various cracks generating in asphalt mixtures [3,4,5]. Recently, Acoustic Emission (AE) technique, as a dynamic and flexible non-destructive method, has attracted much attention in investigating the failure mechanism of asphalt mixtures [6,7]. Actually, AE is transient elastic waves emitted by a deformed or cracked material, which could be detected and converted into electrical signals by piezoelectric sensors. The generation and transmission of AE signals are influenced by material properties, mechanical process, damage evolution and other factors in the process of testing and analysis. Therefore, the variation of AE signals has a close connection with the internal structural behaviors of materials which could be utilized to locate damage sources, distinguish damage patterns and diagnose material properties. The AE parameters, extracted from AE signals, are of great significance to characterize the damage behavior of materials by quantifying the intensity of AE activity. There are some traditional parameters of an AE signal, such as amplitude, energy, duration, etc., as exhibited in Fig. 1. To clarify the fracture mechanisms of concrete, Ohno et.al applied AE parameters and SIGMA analysis to classify the crack modes and evaluate the crack motion [8]. With the purpose of evaluating the damage degree of concrete, Suzuki et.al established the damage parameter correlated with AE rate [9]. Considering the distance between two sensors and the size of specimen, Carpinteri et al. investigated the propagation, attenuation and distortion of AE signal in concrete by comparing AE parameters and waveforms characteristics [10]. Diakhate et al. applied AE energy and AE event to study the fatigue mechanism of asphalt concrete and discussed the influence of energy attenuation and velocity variation on wave propagation [11]. McGovern et al. determined the embrittlement temperature of oxidized aging
Fig. 1. A typical AE signal.
asphalt mixtures through AE count and AE energy, and concluded that the embrittlement temperature of asphalt mixtures increased with the increase of aging time [12]. By means of seismic moments, AE event densities and b-value analysis, Goebel et al. explored the development of stick–slip in granite subjected to triaxial compression forces and identified the nucleation and the abnormal state of its rough surface [13]. In addition to the common AE parameters, the time–frequency information of AE signals is also applied to essentially understand the generation mechanism of AEs and effectively link it with the damage mechanism of materials. Zitto et al. investigated the frequency distribution features of AE corresponding to the fracture of reinforced concrete slabs by applying the Morlet wavelet [14]. To identify the damage types of thermal barrier coatings subjected to unidirectional tension, Yang et al. calculated the energy ratio of AE signals decomposed by discrete wavelet transform (DWT) [15]. Kang et al. characterized the fault features of bearing defects by wavelet packet energy and wavelet packet node entropy and established a support vector machine (SVM) model for fault diagnosis of low-speed bearing [16]. To study impact localization and damage evaluation of composite structures, Jang et al. established the numerical relationship between the AE signals decomposed by wavelet transform (WT) and the delamination areas [17]. Qiu et al. investigated the time–frequency characteristic of AE signals at different damage stages of asphalt mixtures under different loading rates [18]. When discussing the fracture micromechanisms in various composites, Bussiba. A et al. acquired the time–frequency information of AE signals corresponding to the main fracture patterns of materials by short-time fast fourier transform (STFT) [19]. In a word, the parameter and waveform characteristics of AE signals have a complex relation with the internal changes of materials, which provides a basis for tracking damage evolution and evaluating material properties. To further reveal the fracture mechanism of materials, domestic and foreign scholars have made great efforts to establish the relationship between the spatial–temporal evolution of AE events and the crack propagation process. Fatih E et.al correlated AE event with different damage modes of polymer-matrix composites and identified the AE characteristics of transverse crack tension specimens [20]. Lu et al. adopted AE technique to synchronously monitor the deformation and formation of microcracks in a three-point bending sandstone [21]. Li et al. calculated the spatial correlation length of AE events of rock under uniaxial compression tests and concluded that stress release and stress re-distribution were the two main factors affecting the correlation length [22]. Shi et al. studied the creep characteristics of fine sandstone specimens through the spatial evolution of AE events [23]. Li et al. estimated the size of fracture process zone (FPZ) in asphalt mixtures by evaluating the distribution of AE events and discussed the influence of low temperature, loading rates, aggregate type and initial notch lengths on the development scale of the FPZ [24]. By monitoring the location of AE events, the crack length and propagation direction can be effectively observed. Actually, the accumulation of fatigue microcracks in asphalt mixtures is related to material property, structural composition, loading mode and other factors [25,26]. Therefore, the application of real-time and sensitive AE technique will promote the diagnosis and characterization of fatigue damage of asphalt mixtures, and supplement existing detection and evaluation methods for asphalt pavements. The main purpose of this study is to understand the AE characteristics associated with the fatigue damage behavior and investigate the irreversible damage evolution characterized by Kaiser memory effect in asphalt mixtures. The paper aims to: 1. Study the temporal distribution features of AE energy and the variation trend of AE amplitude b-value of asphalt mixtures under cyclic test conditions with different rest time.
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2. Investigate the spatial distribution of AE events and discuss the relationship between the dynamic evolution mode of AE events and the crack propagation path of asphalt mixtures. 3. Observe the Kaiser phenomenon of AE process, explore the AE memory in the course of fatigue damage evolution of asphalt mixtures and identify the damage mechanism associated with the Kaiser effect and the Felicity effect. As a consequence, the research would expectantly give a deeper insight into the AE behaviors and characteristics of asphalt mixtures under cyclic load, and provide a reference for diagnosing and evaluating the performance of asphalt pavements. 2. Materials and methods 2.1. Sample preparation and testing systems The AC-16 (16 mm is the nominal maximum aggregate size of the mixture) asphalt mixtures with a target void ratio of 4% was utilized. Referred to MS-2 Asphalt Mix Design Methods (7th edition) and AASHTO TP312, the aggregate gradation curve of AC-16 asphalt mixture was determined in accordance with the Marshall method of mix design, as shown in Fig. 2. Meanwhile, the optimum content of 70 pen bitumen was 4.7 percent of the total weight of the mix. The aggregate and mineral filler were limestone. After mixing at 150 °C, the AC-16 was compacted by a gyratory compactor at 147 °C to form a cylindrical specimen with a diameter of 150 mm and a height of 160 mm. By utilizing Autosaw II,
Fig. 2. Aggregate gradation for AC-16.
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firstly, two equal circular samples with a thickness of 50 mm were sliced symmetrically from the middle of the cylindrical specimen. Then, four semi-circular bending (SCB) specimens were obtained by cutting along the central axis of the circular samples. Furthermore, a pre-cut notch of 5 mm in length and 1 mm in width was obtained at the bottom of the SCB specimen by using a small cutting saw. The dynamic SCB tests of asphalt mixtures were carried out in UTM-30 controlled by servo hydraulic pressure. The ambient temperature was 20 °C with a tolerance of 0.5 °C. As illustrated in Fig. 3, the load cell was connected with a force and displacement sensor, which could sense the magnitude of the load applied on the surface of the specimen and the amount of deformation in vertical direction. The AE tests were simultaneously performed, using the MISTRAS Micro-II Express AE system manufactured by PAC in the United States. Four AE sensors (R3a) were installed on the surface of the specimen, as shown in Fig. 4. The sensor had a central frequency of 30 kHz and a frequency range from 20 kHz to 180 kHz. The pre-amplifier gain was 40 dB. A fixed threshold value of 30 dB was assigned for the tests. The sampling frequency of the AE signal was set to 1 MHz, the pre-trigger was 256 ls, and the length of the waveform was 1 k. 2.2. Experimental scheme To investigate the AE characteristics of fatigue damage process of asphalt mixtures subjected to cyclic loading, the loading schemes of dynamic SCB tests were designed [27,28]. The load level (FA) of 0.5 kN was initially applied to the SCB specimen. One hundred semi-sinusoidal loads were adopted in each loading cycle with a frequency of 10 Hz, as shown in Fig. 5. When the first
Fig. 4. Layout of AE sensors (dimensions in mm).
Fig. 3. Schematic diagram of experimental setup.
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scale and move the window. The continuous wavelet transform (CWT) is applied to the original signal x(t), defined as,
pffiffi CWT ðs; sÞ ¼ 1= s
þ1 Z
xðtÞw ½ðt sÞ=sdt
ð2Þ
1
The result of CWT is called the wavelet coefficient. The square of wavelet coefficient and its distribution on scale constitute the wavelet scalogram. The wavelet scalogram visually reflects the energy component and the intensity distribution of AE signal pulse in time-scale plane, which is conductive to determine the frequency component of AE signal and the time when it arrives at the AE sensor. 2.5. AE source location
Fig. 5. Test program for dynamic SCB test of AC-16.
loading cycle was finished, the FA of the next loading cycle would increase by 0.5 kN (DA = 0.5 kN), until the SCB specimen completely ruptured, as listed in Table 1. There was no rest time in test 1, while test 2 had a rest time of 10 s and test 3 had a rest time of 20 s between each loading cycle. Three replicates were tested under each condition to ensure the reliability of the experimental results. 2.3. Kaiser effect and Felicity effect In some cases, when the reapplied load on the material exceeds its previous maximum load level, the new AE would be detected. Such phenomenon is called the Kaiser effect, reflecting the stress memory characteristics of materials under repeated loading. The existence of Kaiser effect provides an effective support for assessing the damage severity and estimating the residual life of materials. However, due to the complexity of material properties and damage mechanisms, some materials generate obvious AE phenomenon when the reapplied load has not reached the maximum load level, which is considered to be the appearance of Felicity effect. For the sake of quantifying the irreversibility of damage evolution, the Felicity Ratio is established to evaluate the severity of material damage at a certain load level. The value greater than or equal to 1 indicates that the AE process of materials has Kaiser effect, otherwise, it presents Felicity effect [29].
RF ðiÞ ¼ rAE =ri1
ð1Þ
where, RF(i) is the Felicity ratio, rAE is the load level corresponding to the ith loading cycle at which obviously active AE signals occur, ri-1 is the maximum load level of the (i-1)th loading cycle. 2.4. Wavelet scalogram The result of wavelet transform (WT) not only shows the frequency components of the signal, but also reflects their specific distribution in time domain. WT is adaptable to distinguish the time–frequency characteristics of complex and random AE signals [30]. Given the mother wavelet W(t) as the prototype of the window, the scaling factor s and translation factor s are applied to
Deducing the location of AE source where an AE event occurs is of great importance to determine the propagation direction of microcracks and judge the structural health state of asphalt mixtures. The three-dimensional (3D) location algorithm was applied to calculate the location of AE event detected by the four AE sensors arranged on the surface of the specimen. In this paper, four AE sensors were applied. CWT was utilized to obtain the energy content of AE events, thus the time of arrival (TOA) was determined by the projection of the highest energy of the waveform on time domain [31]. The distance between the AE source and the AE sensor can be expressed as equation set (3).
8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 > > > ðx1 xÞ þ ðy1 yÞ þ ðz1 zÞ > > ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi q > > > > > ðx2 xÞ2 þ ðy2 yÞ2 þ ðz2 zÞ2 ¼ v p jt1 t 2 j > > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > > < ðx2 xÞ2 þ ðy2 yÞ2 þ ðz2 zÞ2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > 2 2 2 > > > ðx3 xÞ þ ðy3 yÞ þ ðz3 zÞ ¼ v p jt2 t 3 j > > ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi q > > > > ðx3 xÞ2 þ ðy3 yÞ2 þ ðz3 zÞ2 > > > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > : ðx xÞ2 þ ðy yÞ2 þ ðz zÞ2 ¼ v jt t j 4 4 p 3 4 4
ð3Þ
where, (xi,yi,zi) is the spatial coordinate of the ith sensor, (x,y,z) is the spatial coordinate of the AE source. vp is the P-wave velocity of asphalt mixtures, and | ti - tj | is the time difference of arrival (TDOA) between two different sensors for the same AE event. Because of the inhomogeneity of the medium, the wave propagation in asphalt mixtures is easy to change. To reduce the influence of wave velocity variability on the accuracy of AE source location, the pencil-leadbreak (PLB) test (20 ± 0.5 °C) was carried out on the surface of the SCB specimens before the start of test. With the location algorithm mentioned above, the average wave velocity was calculated by the detected AE event location corresponding to the true PLB points. The result was applied to the study on the spatial evolution of microcracks in SCB specimens under loading. 3. Results and discussion 3.1. Diagnosis of fatigue damage evolution with AE parameters The various AE parameters could reflect the instantaneous or cumulative characteristics of damage evolution in materials. For
Table 1 Load scheme. Loading cycle
1st
2nd
3rd
4th
5th
6th
7th
8th
9th
10th
FA (kN)
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
X. Qiu et al. / Construction and Building Materials 240 (2020) 117961
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instance, AE energy is suitable for evaluating the AE activity of material under different working conditions. The abrupt change of AE energy sequence denotes the transformation of material damage state [19,32]. AE amplitude b-value, which reflects the intensity of AE activity with different magnitudes in any region or time period, is often used to distinguish damage types, damage degree and signal attenuation. The equation is presented as follows [33].
logN ¼ a bm
ð4Þ
where, N is the cumulative AE activity whose amplitude exceed the value of m, a and b are constant, and b represents the slope of the curve that is defined as the AE b-value. Generally, a larger b-value is associated with the initiation and slow propagation of microcracks, since a large quantity of small amplitude AE hits are generated. A smaller b-value indicates that the rapid and unsteady expansion of cracks [14]. To investigate the variation of AE behavior during fatigue damage process of asphalt mixtures, the stage characteristics of AE energy and AE amplitude b-value were analyzed. The results show that the AE sequences of three replicates under each test condition present a similar variation trend, exhibiting a good repeatability. Therefore, a representative result is selected for detailed description and analysis. The AE energy temporal distribution of dynamic SCB tests with different rest time are compared in Fig. 6. With respect to test 1, there is no obvious AE energy generated in the SCB specimen at the initial loading stage, which indicates that the non-uniformity of the structure dominates the AE activities in asphalt mixture. The AE energy starts to increase during the second loading cycle. At this time, microcracks randomly originate from the tip of pre-cut notch and minor void where the high stress region forms, accompanied by lowintensity AE activity. As the level of cyclic load increases to 1.5 kN, the AE behaviors of asphalt mixtures becomes more active and AE energy continuously increases. Under the action of the fourth loading cycle, AE energy exhibits a short pulse-like increase without a gradual attenuation trend, resulting in the occurrence of ‘‘blank area” and ‘‘hole” with different sizes in its temporal distribution, which implies that the AE activities generated by the concentration and propagation of microcracks in the SCB specimen are intense. In the fifth loading cycle, AE energy becomes much larger and presents a more prominent feature of ‘‘blank area”, which portends that the microcracks constantly grow into macrocracks with the increase of load. When the sixth loading cycle is applied, the asphalt mixture enters the relative quiet period during which the development of AE energy is relatively stable, which indicates that the expansion of macrocracks is steady. When the load level reaches to 3.5 kN, AE energy dramatically rises until the specimen totally breaks. In contrast, the AE energy of test 2 and test 3 is greatly reduced in magnitude. For test 2, there is little change in AE energy when the first and the second loading cycles are applied on the SCB specimen. When the level of cyclic load increases to 1.5 kN, AE energy substantially increases, indicating that the cracking behavior of asphalt mixtures is enhanced. When the fourth loading cycle is applied, the AE energy keeps rising, which means that the size of microcracks gradually expands. During the fifth and the sixth loading cycles, the feature of ‘‘blank area” in temporal distribution of AE energy still exist, indicating that the formation of macrocracks generate strong AE activities. Notably, the magnitude of AE energy and the scale of ‘‘blank area” obviously reduce compared with test 1. Such phenomenon could be attributed to the healing behaviors of AC-16 in the rest time, reducing the intensity of AE response after reloading. When the cyclic load level is 3.5 kN, AE energy obviously decreases and the ‘‘blank area” in its temporal distribution disappears. At this time, the magnitude of AE energy is in the range of 0 to 600, which is similar to the AE energy in the sixth
(a) AE energy and load versus time in test 1
(b) AE energy and load versus time in test 2
(c) AE energy and load versus time in test 3 Fig. 6. Variation of AE energy during fatigue damage process of asphalt mixtures.
loading cycle in test 1, indicating that asphalt mixture has entered the stage of macrocracks propagation. Finally, the abrupt increase of AE energy indicates the unstable propagation of macrocracks. In test 3, the growth trend of AE energy during fatigue damage evolution of asphalt mixtures is relatively smooth. However, compared with test 1 and test 2, the feature of ‘‘blank area” of AE
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energy temporal distribution completely disappears at the stage of microcracks initiation and concentration, which implies that the longer rest time makes the viscoelasticity of asphalt mixtures more obvious. The SCB specimen has a better resistance to deformation, which could reduce the degree of damage. Under the action of the sixth loading cycle, the sudden increase of AE energy means the intensification of fatigue damage. Since then, AE energy maintains at a high level, which represents the sustainable development of the fracture zone. Fig. 7 shows the variation of AE amplitude b-value of test 1, test 2 and test 3. From the first loading cycle to the fifth loading cycle, the AE amplitude b-value of test 1 directly decreases from 1.71 to 0.31, which reflects the progressive growth of microcracks and the formation of macrocracks in the SCB specimen. Under the action of the sixth and the seventh loading cycle, the AE amplitude b-value significantly increases, indicating the accelerating development and coalescence of internal cracks with the formation of macrocracks. In test 2, the AE amplitude b-value increases from 1.60 to 1.92 in the first two loading cycles, demonstrating that more minor damages generate at the initial loading stage. Then it decreases to 0.77 and levels off, which indicate that the constant expansion and connection of cracks leads to severe damage of SCB specimen. By comparison, from the first loading cycle to the sixth loading cycle, the AE amplitude b-value of test 3 directly decreases from 2.76 to 1.10, illustrating that the AE activities with low amplitude decreases due to the concentration of microcracks. Subsequently, it increases to 1.40 and finally drops to 0.94, revealing that the dominant macrocracks still elongate and branch through the whole SCB specimen. It shows that the inflection point of the decline of AE amplitude b-value could be considered as precursor information to indicate the rupture of asphalt mixtures. In a word, the variation of AE energy and AE amplitude b-value could effectively describe the stage characteristics of fatigue damage process of asphalt mixtures with different rest time. It is notable that in test 3, however, the longest rest time does not significantly improve the resistance of AC-16 to fatigue damage. Therefore, it is necessary to explore the evolution process and distribution form of microcracks in SCB specimens and reveal the fatigue damage mechanism of asphalt mixtures.
[7]. To describe the fatigue crack growth and understand the fracture mechanism of asphalt mixtures, the spatial distribution of AE events was tracked and exhibited on the surface of the SCB specimen. Based on the proposed location method, the Morlet wavelet was employed to conduct CWT with a scale range from 1 to 64 for each AE event. The TOA was obtained by the highest energy of the waveform projected in time domain. The results of PLB test show that the average AE wave velocity of asphalt mixtures in test 1, test 2 and test 3 are 3.66 km/s, 3.74 km/s and 3.68 km/s, respectively.
(a) Location results and crack propagation path of test 1
3.2. Correlation between AE location and crack propagation The AE events are mainly caused by the formation and propagation of microcracks. The crack propagation path of asphalt mixtures could be located by the spatial distribution of AE events
(b) Location results and crack propagation path of test 2
(c) Location results and crack propagation path of test 3 Fig. 7. Curves of AE amplitude b-value versus cycles of asphalt mixtures.
Fig. 8. Spatial locations of AE events plotted on SCB specimens.
X. Qiu et al. / Construction and Building Materials 240 (2020) 117961
Through the above analysis of AE characteristics and mechanical behaviors of SCB specimens, it shows that macrocracks occur during the fifth loading cycle in test 1, while develop during the sixth loading cycle in test 2 and test 3. Fig. 8 shows the location result of AE events plotted on the SCB specimens. The star shape and square shape denote the locations of AE events before and after the formation of macrocracks, and the round shape indicates the locations of the four AE sensors plotted on the same surface. With respect to test 1, a small number of AE events are detected at the stage of initiation and concentration of microcracks. The located AE events are mainly distributed near the coarse aggregate along the center axis of the specimen. With the increase of load level, the number of AE events significantly increases and distributes along the vertical direction of the SCB specimen. In test 2, however, the development of microcracks produces more AE events, which obviously concentrate on the left of the center axis of the SCB specimens. After the formation of macrocracks, the quantity of AE events is still increasing and the distribution of AE events is denser along the fracture surface. Similar to test 1, the early AE behavior of asphalt mixtures is not obvious in test 3. Whereas, the distribution of AE events is scattered and disordered. In the macrocracks propagation stage, the AE events suddenly increase, and the distribution scales of both horizontal and vertical directions distinctly expand. Furthermore, by comparing the morphology of the fracture surface, it is found that circumferential alignment of large-size aggregate is obvious in the AC-16 specimen of test 2. The random distribution of small-size aggregate in the center of the specimen is easier to generate microcracks, accompanied by active AE events. However, in test 2 and test 3, AE events are mainly observed near the interface between asphalt binder and coarse aggregate, implying that the large-size aggregate distributed near the center axis of SCB specimens inhibit the formation of microcracks to a certain extent. Notably, for the location results of test 2 and test 3, AE events are not only large in quantity, but extensive in distribution, which reflects the influence of rest time on the complex fracture behavior of asphalt mixtures. The crack propagation path of SCB specimens could be identified through the spatial locations of accumulated AE events. The distribution of AE events develops from a relatively localized area to a larger area and is affected by the microstructures of asphalt mixtures and loading modes. Therefore, it is of great significance to understand the fatigue damage mechanism of asphalt mixtures under different working conditions by monitoring AE events.
The results show that in the second loading cycle, the RF of test 1, test 2 and test 3 are all greater than 1. Subsequently, the RF decreases to <1. In the fifth loading cycle, the RF of test 1 declines to 0.41. While the RF values of test 2 and test 3 reduce to 0.42 and 0.47, respectively, in the sixth loading cycle. After that, the RF values of all the tests vary from 0 to 0.33. In terms of the above study on the AE characteristics associated with the fatigue damage state, the variation of RF during the fatigue damage evolution of asphalt mixtures can be divided into three stages. As presented in Fig. 9, in the initial loading cycle, 1 RF, the Kaiser effect is clear, resulting from discontinuities generated by deformation and friction of primary pores and micro defects in asphalt mixtures. The memory effect of asphalt mixtures is significant since they are not damaged at lower stress. During the initiation and concentration of microcracks, 0.4 RF < 1, the Felicity effect occurs. The RF values of test 1 and test 3 are closer to 1, illustrating that the integrity of asphalt mixtures is affected. The RF of test 2 reduces to about 0.8, however, which suggests that the movement and expansion of discontinuities in asphalt mixtures are more active that inhibit the Kaiser effect. As the level of cyclic load increases, RF values substantially decrease for the reason that the accumulation of numerous microcracks make the AE expression of asphalt mixtures more sensitive. When the SCB specimens enter the stage of macrocracks propagation, RF < 0.4, the AE process of asphalt mixture has completely lost irreversible characteristic. The Kaiser effect dramatically decays, since the integrity of SCB specimens is seriously damaged. The macro fracture behavior of asphalt mixtures is intensified, generating stronger AE activities. Therefore, in the stage of deformation and compaction, an obvious Kaiser effect occurs in asphalt mixtures when exceeding the maximum ‘‘memorized” stress value. Once entering the stage of microcracks initiation and concentration, the Felicity effect of AE process gradually appears, which reveals that the integrity of the SCB specimen has a significant impact on its stress memory. To further distinguish the influence of loading mode on irreversible evolution of early fatigue damage in asphalt mixtures, the AE signal corresponding to the occurrence of the Kaiser effect (RF 1) and the Felicity effect (once RF < 1) was defined as the Kaiser effect point and the Felicity effect point, respectively. During the process of the test, the Kaiser effect point occurred at the 2nd load cycle, which reflected the AE memory of asphalt mixtures in the stage of deformation and compaction. And the Felicity effect point occurred at the 3rd load cycle, indicating the AE response affected by the development of microcracks in asphalt mixtures. The energy distribution characteristics of related signal waveforms were obtained by using CWT. The Morlet wavelet was applied and
3.3. Irreversible characteristics of the AE process The Kaiser effect is a phenomenon commonly observed in materials subjected to repeated cycles of external forces, which describes the AE memory effect of materials on loading history. In materials presenting AE response under a certain load, the Kaiser effect could be characterized by the absence of AE until that load is exceeded; when that load has not reached, the significant increase of AE activities is regarded as the Felicity effect. It shows that the Felicity effect, rather than the Kaiser effect, would exist in asphalt mixtures in certain cases. [32,34]. To investigate the AE memory formation and characterize the damage evolution of asphalt mixtures under cyclic loading with different rest time, the variations of Felicity ratio (RF) were analyzed. Generally, for different materials, the threshold for rAE can be set at AE count exceeds a certain value [29]. In this paper, AE counts greater than 20 was utilized as the stress threshold for rAE. Besides, the occurrence of AE activities after re-loading was considered effective when at least four AE signals that met the threshold were continuously acquired.
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Fig. 9. RF value for asphalt mixtures in each loading cycle.
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Fig. 10. Time-scale distribution for the Kaiser effect points and the Felicity effect points.
the scale range was set from1 to 64. The horizontal coordinate denotes the sampling points of the waveform, and the longitudinal coordinate represents scales. In the legend on the right, the high energy density is shown in yellow and the low energy density in blue. The energy variation of the waveforms of Kaiser effect and Felicity effect points with time and scale are compared, as presented in Fig. 10. With regard to test 1, test 2 and test 3, it can be seen that the main impulse component of Kaiser effect point appears at 977 ls, 794 ls and 421 ls, corresponding to the characteristic frequency of 26.2 kHz, 26.2 kHz and 25.4 kHz. In addition, the bandwidth of high energy area in the time domain is 62 ls, 256 ls and 220 ls and in the frequency domain is 3.4 kHz, 19.8 kHz and 8.9 kHz. Compared with test 1, the characteristic frequency of point in test 2 and 3 appears earlier, and the distribution range of high energy are is obviously larger. It demonstrates that the deformation of asphalt mixtures is recovered during the rest time, leading to little changes in the inhomogeneity of specimens. Hence, the propagation of AE signal after reloading is faster with less interference. Comparatively, under the three different cyclic test conditions, the main impulse components of Felicity effect points appear at 630 ls, 786 ls and 884 ls, corresponding to the characteristic frequency of 25.39 kHz, 25.39 kHz and 23.90 kHz, respectively. Obviously, the projection length of high energy area in the time domain is 156 ls, 224 ls and 245 ls, and the projection length in the frequency domain is 6.5 kHz, 21.9 kHz and 10.3 kHz. Compared with test 1, the occurrence of characteristic frequency in test 2 and test 3 is delayed and the high energy area is still large. Such phenomenon is caused by the self-adjustment of asphalt mixtures during the rest time, which becomes more active due to the increase and connection of minor defects. After reloading, the energy com-
ponents of AE signals related to different damage mechanisms change, and its concentration in time–frequency domain becomes poor. Notably, the characteristic frequency of Felicity effect points is lower than that of Kaiser effect points, and the energy distribution of the corresponding waveform is more complex. Evidently, the wavelet scalogram could effectively identify the micro damage mechanism associated with the Kaiser effect and the Felicity effect, which could provide a reference for the early damage detection of asphalt pavements. 4. Conclusions Based on the dynamic SCB tests and AE tests of AC-16, the changes of different AE parameters, such as energy, amplitude bvalue and event, and the existence of the Kaiser effect phenomenon were explored to characterize the fatigue behavior at various stages of damage evolution of asphalt mixtures. The main conclusions are presented as follows. (1) The temporal variation of AE energy and AE amplitude bvalue are related to the mechanical behaviors of asphalt mixtures during the fatigue damage process. The rest time between each loading cycle would affect the intensity of AE activities, which could be reflected by the ‘‘blank area” in the temporal distribution of AE energy. With the increase of rest time, the magnitude of AE energy decreases, and the feature of ‘‘blank area” gradually disappears at the stage of microcracks initiation and concentration, demonstrating that asphalt mixture has a better resistance to deformation. Additionally, the AE amplitude b-value could effectively determine the transformation of fatigue damage condition
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of asphalt mixtures, and the inflection point of the decline of AE amplitude b-value curve could be considered as a precursor to the rupture of asphalt mixtures. (2) The spatial distribution of AE events could identify the crack propagation path of asphalt mixtures, and their dynamic development reveals the fatigue damage in asphalt mixtures develops from a relatively localized area to a larger area. The locations of AE events also reflect the influence of the microstructures of asphalt mixtures and loading modes on the fatigue damage process. The large-size aggregate could inhibit the formation of microcracks to some extent, generating less AE events. Affected by the rest time probably, AE events are more generated and widely distributed in asphalt mixtures. (3) The change of Felicity ratio could be divided into three stages, corresponding to the deformation and compaction stage where 1 RF, microcracks initiation and concentration stage where 0.4 RF < 1, and macrocracks extension stage where RF < 0.4. The Kaiser effect of AE process mainly occurs in deformation and compaction stage of asphalt mixtures. Once entering the stage of microcracks initiation and concentration, Kaiser effect gradually disappears and Felicity effect begins to play a role. The wavelet scalogram of AE waveform could identify the micro damage mechanism associated with the Kaiser effect and the Felicity effect. It shows that the Kaiser effect point propagates faster with the increase of rest time, while the time–frequency concentration of Felicity effect point become worse in general. In conclusion, AE technique is effective in describing and evaluating the fatigue damage behaviors and characteristics of asphalt mixtures. Meanwhile, due to the sensitivity of Kaiser effect, it is significant to determine the formation and propagation of irreversible damage in asphalt pavements. 5. Compliance with ethical Standards: Funding: This work was supported by the Natural Science Foundation of Zhejiang Province [grant numbers LY18E080020, LQ20E080009]; and Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC [grant number MK201901]. CRediT authorship contribution statement Xin Qiu: Conceptualization, Writing - review & editing, Supervision, Project administration. Jingxian Xu: Formal analysis, Investigation, Validation, Writing - original draft. Wenyi Xu: Investigation, Data curation, Validation, Writing - original draft. Shanglin Xiao: Data curation, Validation. Feng Wang: Writing review & editing. Jie Yuan: Writing - review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement The authors are great grateful for the assistance from Yujie Wang, Weihao Cheng, Ningning Li and Ganghua Hu in conducting experiments for this paper. The authors would like to thank Xiaoxiao Tang for commendable opinions and revising the English style of the manuscript, which improved its legibility.
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Contents lists available at ScienceDirect
Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
Characterization of fatigue damage mechanism of asphalt mixtures with acoustic emission Xin Qiu a,⇑, Jingxian Xu a, Wenyi Xu a,⇑, Shanglin Xiao a, Feng Wang b, Jie Yuan c a
College of Engineering, Road and Traffic Engineering Institute, Zhejiang Normal University, 688 Yingbin Road, Jinhua, Zhejiang 321004, China Ingram School of Engineering, Texas State University, San Marcos, Texas, United State c Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, China b
h i g h l i g h t s Acoustic emission could characterize the fatigue damage behavior of asphalt mixtures. AE energy and amplitude b-value could reflect the stage features of damage evolution. The distribution of AE event is related to the microstructure of mix and loading mode. The Felicity ratio could measure the irreversibility of damage evolution. The wavelet scalogram of AE waveforms could identify the damage mechanism.
a r t i c l e
i n f o
Article history: Received 6 September 2019 Received in revised form 21 December 2019 Accepted 24 December 2019
Keywords: Asphalt mixture Acoustic emission Damage stage characteristic Kaiser effect Wavelet scalogram
a b s t r a c t Understanding the fatigue damage mechanism of asphalt mixtures is essential to prolong the service life of asphalt pavements. Acoustic emission (AE) technique can effectively detect minor damage and evaluate material mechanical properties. The objective of this paper was to investigate the temporal-spatial variation of AE parameters associated with fatigue damage behavior of asphalt mixtures and characterize the irreversibility of damage evolution utilizing the Kaiser effect. Firstly, the semi-circular bending (SCB) tests with different rest time and AE tests were carried out simultaneously to discuss the AE characteristics of fatigue damage behavior of asphalt mixtures. Secondly, the spatial evolution of AE events was analyzed to correlate with the crack propagation path of asphalt mixtures. Finally, the Felicity ratio (RF) and wavelet scalogram were utilized to reveal the irreversible characteristics of the AE process of asphalt mixtures. The results show that the ‘‘blank area” in AE energy temporal, representing the intensity of AE activities in asphalt mixtures during the process of microcracks concentration, gradually disappears with the increase of rest time. The inflection point of the decline of AE amplitude b-value could be considered as a precursor to the rupture of asphalt mixtures. The spatial distribution of AE events from disorder to order reflects the influence of the microstructures of asphalt mixtures and loading mode on crack propagation. The change of RF could be divided into the three stages, corresponding to the deformation and compaction stage where 1 RF, microcracks initiation and concentration stage where 0.4 RF < 1, and macrocracks extension stage where RF < 0.4. The Kaiser effect mainly exists at the stage of deformation and compaction of asphalt mixtures. After the occurrence of macrocracks, the AE process would lose irreversible characteristics. The damage mechanisms related to the Kaiser effect and the Felicity effect could be identified by the energy distribution of AE waveforms. Ó 2020 Elsevier Ltd. All rights reserved.
1. Introduction Fatigue cracking is one of the major distresses in asphalt pavements. To delay the deterioration of pavement performance, pre⇑ Corresponding authors. E-mail addresses: [email protected] (X. Qiu), [email protected] (W. Xu), [email protected] (F. Wang), [email protected] (J. Yuan). https://doi.org/10.1016/j.conbuildmat.2019.117961 0950-0618/Ó 2020 Elsevier Ltd. All rights reserved.
ventive maintenance is considered effective in dealing with the premature damage hidden in asphalt pavements, depending on the understanding of fatigue damage evolution process and fracture mechanism of asphalt mixtures, as well as the detection and diagnosis of cracking position and severity. Generally, the fatigue damage of asphalt mixtures originates from the initiation of microcracks, caused by the movement of primary pores and micro defects distributed in asphalt mixtures under the external forces.
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The concentration and development of microcracks seriously affects the pavement performance of asphalt mixtures, promotes the formation of macrocracks and eventually leads to the deterioration of material properties. In addition, due to the viscoelastic properties of asphalt mixture, its mechanical behavior is variable with external environment and vehicle loading. Consequently, it is of great necessity to monitor the crack growth and measure the damage accumulation in asphalt pavements by modern technologies. In laboratory tests, the relationship between structural changes and mechanical properties of asphalt mixtures has been extensively studied in various approaches. For instance, the simulation methods represented by finite element and boundary element have been applied to the fracture modeling of asphalt mixtures through numerical calculation and test [1,2]. CT and scanning laser have been widely used to capture various cracks generating in asphalt mixtures [3,4,5]. Recently, Acoustic Emission (AE) technique, as a dynamic and flexible non-destructive method, has attracted much attention in investigating the failure mechanism of asphalt mixtures [6,7]. Actually, AE is transient elastic waves emitted by a deformed or cracked material, which could be detected and converted into electrical signals by piezoelectric sensors. The generation and transmission of AE signals are influenced by material properties, mechanical process, damage evolution and other factors in the process of testing and analysis. Therefore, the variation of AE signals has a close connection with the internal structural behaviors of materials which could be utilized to locate damage sources, distinguish damage patterns and diagnose material properties. The AE parameters, extracted from AE signals, are of great significance to characterize the damage behavior of materials by quantifying the intensity of AE activity. There are some traditional parameters of an AE signal, such as amplitude, energy, duration, etc., as exhibited in Fig. 1. To clarify the fracture mechanisms of concrete, Ohno et.al applied AE parameters and SIGMA analysis to classify the crack modes and evaluate the crack motion [8]. With the purpose of evaluating the damage degree of concrete, Suzuki et.al established the damage parameter correlated with AE rate [9]. Considering the distance between two sensors and the size of specimen, Carpinteri et al. investigated the propagation, attenuation and distortion of AE signal in concrete by comparing AE parameters and waveforms characteristics [10]. Diakhate et al. applied AE energy and AE event to study the fatigue mechanism of asphalt concrete and discussed the influence of energy attenuation and velocity variation on wave propagation [11]. McGovern et al. determined the embrittlement temperature of oxidized aging
Fig. 1. A typical AE signal.
asphalt mixtures through AE count and AE energy, and concluded that the embrittlement temperature of asphalt mixtures increased with the increase of aging time [12]. By means of seismic moments, AE event densities and b-value analysis, Goebel et al. explored the development of stick–slip in granite subjected to triaxial compression forces and identified the nucleation and the abnormal state of its rough surface [13]. In addition to the common AE parameters, the time–frequency information of AE signals is also applied to essentially understand the generation mechanism of AEs and effectively link it with the damage mechanism of materials. Zitto et al. investigated the frequency distribution features of AE corresponding to the fracture of reinforced concrete slabs by applying the Morlet wavelet [14]. To identify the damage types of thermal barrier coatings subjected to unidirectional tension, Yang et al. calculated the energy ratio of AE signals decomposed by discrete wavelet transform (DWT) [15]. Kang et al. characterized the fault features of bearing defects by wavelet packet energy and wavelet packet node entropy and established a support vector machine (SVM) model for fault diagnosis of low-speed bearing [16]. To study impact localization and damage evaluation of composite structures, Jang et al. established the numerical relationship between the AE signals decomposed by wavelet transform (WT) and the delamination areas [17]. Qiu et al. investigated the time–frequency characteristic of AE signals at different damage stages of asphalt mixtures under different loading rates [18]. When discussing the fracture micromechanisms in various composites, Bussiba. A et al. acquired the time–frequency information of AE signals corresponding to the main fracture patterns of materials by short-time fast fourier transform (STFT) [19]. In a word, the parameter and waveform characteristics of AE signals have a complex relation with the internal changes of materials, which provides a basis for tracking damage evolution and evaluating material properties. To further reveal the fracture mechanism of materials, domestic and foreign scholars have made great efforts to establish the relationship between the spatial–temporal evolution of AE events and the crack propagation process. Fatih E et.al correlated AE event with different damage modes of polymer-matrix composites and identified the AE characteristics of transverse crack tension specimens [20]. Lu et al. adopted AE technique to synchronously monitor the deformation and formation of microcracks in a three-point bending sandstone [21]. Li et al. calculated the spatial correlation length of AE events of rock under uniaxial compression tests and concluded that stress release and stress re-distribution were the two main factors affecting the correlation length [22]. Shi et al. studied the creep characteristics of fine sandstone specimens through the spatial evolution of AE events [23]. Li et al. estimated the size of fracture process zone (FPZ) in asphalt mixtures by evaluating the distribution of AE events and discussed the influence of low temperature, loading rates, aggregate type and initial notch lengths on the development scale of the FPZ [24]. By monitoring the location of AE events, the crack length and propagation direction can be effectively observed. Actually, the accumulation of fatigue microcracks in asphalt mixtures is related to material property, structural composition, loading mode and other factors [25,26]. Therefore, the application of real-time and sensitive AE technique will promote the diagnosis and characterization of fatigue damage of asphalt mixtures, and supplement existing detection and evaluation methods for asphalt pavements. The main purpose of this study is to understand the AE characteristics associated with the fatigue damage behavior and investigate the irreversible damage evolution characterized by Kaiser memory effect in asphalt mixtures. The paper aims to: 1. Study the temporal distribution features of AE energy and the variation trend of AE amplitude b-value of asphalt mixtures under cyclic test conditions with different rest time.
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2. Investigate the spatial distribution of AE events and discuss the relationship between the dynamic evolution mode of AE events and the crack propagation path of asphalt mixtures. 3. Observe the Kaiser phenomenon of AE process, explore the AE memory in the course of fatigue damage evolution of asphalt mixtures and identify the damage mechanism associated with the Kaiser effect and the Felicity effect. As a consequence, the research would expectantly give a deeper insight into the AE behaviors and characteristics of asphalt mixtures under cyclic load, and provide a reference for diagnosing and evaluating the performance of asphalt pavements. 2. Materials and methods 2.1. Sample preparation and testing systems The AC-16 (16 mm is the nominal maximum aggregate size of the mixture) asphalt mixtures with a target void ratio of 4% was utilized. Referred to MS-2 Asphalt Mix Design Methods (7th edition) and AASHTO TP312, the aggregate gradation curve of AC-16 asphalt mixture was determined in accordance with the Marshall method of mix design, as shown in Fig. 2. Meanwhile, the optimum content of 70 pen bitumen was 4.7 percent of the total weight of the mix. The aggregate and mineral filler were limestone. After mixing at 150 °C, the AC-16 was compacted by a gyratory compactor at 147 °C to form a cylindrical specimen with a diameter of 150 mm and a height of 160 mm. By utilizing Autosaw II,
Fig. 2. Aggregate gradation for AC-16.
3
firstly, two equal circular samples with a thickness of 50 mm were sliced symmetrically from the middle of the cylindrical specimen. Then, four semi-circular bending (SCB) specimens were obtained by cutting along the central axis of the circular samples. Furthermore, a pre-cut notch of 5 mm in length and 1 mm in width was obtained at the bottom of the SCB specimen by using a small cutting saw. The dynamic SCB tests of asphalt mixtures were carried out in UTM-30 controlled by servo hydraulic pressure. The ambient temperature was 20 °C with a tolerance of 0.5 °C. As illustrated in Fig. 3, the load cell was connected with a force and displacement sensor, which could sense the magnitude of the load applied on the surface of the specimen and the amount of deformation in vertical direction. The AE tests were simultaneously performed, using the MISTRAS Micro-II Express AE system manufactured by PAC in the United States. Four AE sensors (R3a) were installed on the surface of the specimen, as shown in Fig. 4. The sensor had a central frequency of 30 kHz and a frequency range from 20 kHz to 180 kHz. The pre-amplifier gain was 40 dB. A fixed threshold value of 30 dB was assigned for the tests. The sampling frequency of the AE signal was set to 1 MHz, the pre-trigger was 256 ls, and the length of the waveform was 1 k. 2.2. Experimental scheme To investigate the AE characteristics of fatigue damage process of asphalt mixtures subjected to cyclic loading, the loading schemes of dynamic SCB tests were designed [27,28]. The load level (FA) of 0.5 kN was initially applied to the SCB specimen. One hundred semi-sinusoidal loads were adopted in each loading cycle with a frequency of 10 Hz, as shown in Fig. 5. When the first
Fig. 4. Layout of AE sensors (dimensions in mm).
Fig. 3. Schematic diagram of experimental setup.
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scale and move the window. The continuous wavelet transform (CWT) is applied to the original signal x(t), defined as,
pffiffi CWT ðs; sÞ ¼ 1= s
þ1 Z
xðtÞw ½ðt sÞ=sdt
ð2Þ
1
The result of CWT is called the wavelet coefficient. The square of wavelet coefficient and its distribution on scale constitute the wavelet scalogram. The wavelet scalogram visually reflects the energy component and the intensity distribution of AE signal pulse in time-scale plane, which is conductive to determine the frequency component of AE signal and the time when it arrives at the AE sensor. 2.5. AE source location
Fig. 5. Test program for dynamic SCB test of AC-16.
loading cycle was finished, the FA of the next loading cycle would increase by 0.5 kN (DA = 0.5 kN), until the SCB specimen completely ruptured, as listed in Table 1. There was no rest time in test 1, while test 2 had a rest time of 10 s and test 3 had a rest time of 20 s between each loading cycle. Three replicates were tested under each condition to ensure the reliability of the experimental results. 2.3. Kaiser effect and Felicity effect In some cases, when the reapplied load on the material exceeds its previous maximum load level, the new AE would be detected. Such phenomenon is called the Kaiser effect, reflecting the stress memory characteristics of materials under repeated loading. The existence of Kaiser effect provides an effective support for assessing the damage severity and estimating the residual life of materials. However, due to the complexity of material properties and damage mechanisms, some materials generate obvious AE phenomenon when the reapplied load has not reached the maximum load level, which is considered to be the appearance of Felicity effect. For the sake of quantifying the irreversibility of damage evolution, the Felicity Ratio is established to evaluate the severity of material damage at a certain load level. The value greater than or equal to 1 indicates that the AE process of materials has Kaiser effect, otherwise, it presents Felicity effect [29].
RF ðiÞ ¼ rAE =ri1
ð1Þ
where, RF(i) is the Felicity ratio, rAE is the load level corresponding to the ith loading cycle at which obviously active AE signals occur, ri-1 is the maximum load level of the (i-1)th loading cycle. 2.4. Wavelet scalogram The result of wavelet transform (WT) not only shows the frequency components of the signal, but also reflects their specific distribution in time domain. WT is adaptable to distinguish the time–frequency characteristics of complex and random AE signals [30]. Given the mother wavelet W(t) as the prototype of the window, the scaling factor s and translation factor s are applied to
Deducing the location of AE source where an AE event occurs is of great importance to determine the propagation direction of microcracks and judge the structural health state of asphalt mixtures. The three-dimensional (3D) location algorithm was applied to calculate the location of AE event detected by the four AE sensors arranged on the surface of the specimen. In this paper, four AE sensors were applied. CWT was utilized to obtain the energy content of AE events, thus the time of arrival (TOA) was determined by the projection of the highest energy of the waveform on time domain [31]. The distance between the AE source and the AE sensor can be expressed as equation set (3).
8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 > > > ðx1 xÞ þ ðy1 yÞ þ ðz1 zÞ > > ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi q > > > > > ðx2 xÞ2 þ ðy2 yÞ2 þ ðz2 zÞ2 ¼ v p jt1 t 2 j > > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > > < ðx2 xÞ2 þ ðy2 yÞ2 þ ðz2 zÞ2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > 2 2 2 > > > ðx3 xÞ þ ðy3 yÞ þ ðz3 zÞ ¼ v p jt2 t 3 j > > ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi q > > > > ðx3 xÞ2 þ ðy3 yÞ2 þ ðz3 zÞ2 > > > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > : ðx xÞ2 þ ðy yÞ2 þ ðz zÞ2 ¼ v jt t j 4 4 p 3 4 4
ð3Þ
where, (xi,yi,zi) is the spatial coordinate of the ith sensor, (x,y,z) is the spatial coordinate of the AE source. vp is the P-wave velocity of asphalt mixtures, and | ti - tj | is the time difference of arrival (TDOA) between two different sensors for the same AE event. Because of the inhomogeneity of the medium, the wave propagation in asphalt mixtures is easy to change. To reduce the influence of wave velocity variability on the accuracy of AE source location, the pencil-leadbreak (PLB) test (20 ± 0.5 °C) was carried out on the surface of the SCB specimens before the start of test. With the location algorithm mentioned above, the average wave velocity was calculated by the detected AE event location corresponding to the true PLB points. The result was applied to the study on the spatial evolution of microcracks in SCB specimens under loading. 3. Results and discussion 3.1. Diagnosis of fatigue damage evolution with AE parameters The various AE parameters could reflect the instantaneous or cumulative characteristics of damage evolution in materials. For
Table 1 Load scheme. Loading cycle
1st
2nd
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instance, AE energy is suitable for evaluating the AE activity of material under different working conditions. The abrupt change of AE energy sequence denotes the transformation of material damage state [19,32]. AE amplitude b-value, which reflects the intensity of AE activity with different magnitudes in any region or time period, is often used to distinguish damage types, damage degree and signal attenuation. The equation is presented as follows [33].
logN ¼ a bm
ð4Þ
where, N is the cumulative AE activity whose amplitude exceed the value of m, a and b are constant, and b represents the slope of the curve that is defined as the AE b-value. Generally, a larger b-value is associated with the initiation and slow propagation of microcracks, since a large quantity of small amplitude AE hits are generated. A smaller b-value indicates that the rapid and unsteady expansion of cracks [14]. To investigate the variation of AE behavior during fatigue damage process of asphalt mixtures, the stage characteristics of AE energy and AE amplitude b-value were analyzed. The results show that the AE sequences of three replicates under each test condition present a similar variation trend, exhibiting a good repeatability. Therefore, a representative result is selected for detailed description and analysis. The AE energy temporal distribution of dynamic SCB tests with different rest time are compared in Fig. 6. With respect to test 1, there is no obvious AE energy generated in the SCB specimen at the initial loading stage, which indicates that the non-uniformity of the structure dominates the AE activities in asphalt mixture. The AE energy starts to increase during the second loading cycle. At this time, microcracks randomly originate from the tip of pre-cut notch and minor void where the high stress region forms, accompanied by lowintensity AE activity. As the level of cyclic load increases to 1.5 kN, the AE behaviors of asphalt mixtures becomes more active and AE energy continuously increases. Under the action of the fourth loading cycle, AE energy exhibits a short pulse-like increase without a gradual attenuation trend, resulting in the occurrence of ‘‘blank area” and ‘‘hole” with different sizes in its temporal distribution, which implies that the AE activities generated by the concentration and propagation of microcracks in the SCB specimen are intense. In the fifth loading cycle, AE energy becomes much larger and presents a more prominent feature of ‘‘blank area”, which portends that the microcracks constantly grow into macrocracks with the increase of load. When the sixth loading cycle is applied, the asphalt mixture enters the relative quiet period during which the development of AE energy is relatively stable, which indicates that the expansion of macrocracks is steady. When the load level reaches to 3.5 kN, AE energy dramatically rises until the specimen totally breaks. In contrast, the AE energy of test 2 and test 3 is greatly reduced in magnitude. For test 2, there is little change in AE energy when the first and the second loading cycles are applied on the SCB specimen. When the level of cyclic load increases to 1.5 kN, AE energy substantially increases, indicating that the cracking behavior of asphalt mixtures is enhanced. When the fourth loading cycle is applied, the AE energy keeps rising, which means that the size of microcracks gradually expands. During the fifth and the sixth loading cycles, the feature of ‘‘blank area” in temporal distribution of AE energy still exist, indicating that the formation of macrocracks generate strong AE activities. Notably, the magnitude of AE energy and the scale of ‘‘blank area” obviously reduce compared with test 1. Such phenomenon could be attributed to the healing behaviors of AC-16 in the rest time, reducing the intensity of AE response after reloading. When the cyclic load level is 3.5 kN, AE energy obviously decreases and the ‘‘blank area” in its temporal distribution disappears. At this time, the magnitude of AE energy is in the range of 0 to 600, which is similar to the AE energy in the sixth
(a) AE energy and load versus time in test 1
(b) AE energy and load versus time in test 2
(c) AE energy and load versus time in test 3 Fig. 6. Variation of AE energy during fatigue damage process of asphalt mixtures.
loading cycle in test 1, indicating that asphalt mixture has entered the stage of macrocracks propagation. Finally, the abrupt increase of AE energy indicates the unstable propagation of macrocracks. In test 3, the growth trend of AE energy during fatigue damage evolution of asphalt mixtures is relatively smooth. However, compared with test 1 and test 2, the feature of ‘‘blank area” of AE
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energy temporal distribution completely disappears at the stage of microcracks initiation and concentration, which implies that the longer rest time makes the viscoelasticity of asphalt mixtures more obvious. The SCB specimen has a better resistance to deformation, which could reduce the degree of damage. Under the action of the sixth loading cycle, the sudden increase of AE energy means the intensification of fatigue damage. Since then, AE energy maintains at a high level, which represents the sustainable development of the fracture zone. Fig. 7 shows the variation of AE amplitude b-value of test 1, test 2 and test 3. From the first loading cycle to the fifth loading cycle, the AE amplitude b-value of test 1 directly decreases from 1.71 to 0.31, which reflects the progressive growth of microcracks and the formation of macrocracks in the SCB specimen. Under the action of the sixth and the seventh loading cycle, the AE amplitude b-value significantly increases, indicating the accelerating development and coalescence of internal cracks with the formation of macrocracks. In test 2, the AE amplitude b-value increases from 1.60 to 1.92 in the first two loading cycles, demonstrating that more minor damages generate at the initial loading stage. Then it decreases to 0.77 and levels off, which indicate that the constant expansion and connection of cracks leads to severe damage of SCB specimen. By comparison, from the first loading cycle to the sixth loading cycle, the AE amplitude b-value of test 3 directly decreases from 2.76 to 1.10, illustrating that the AE activities with low amplitude decreases due to the concentration of microcracks. Subsequently, it increases to 1.40 and finally drops to 0.94, revealing that the dominant macrocracks still elongate and branch through the whole SCB specimen. It shows that the inflection point of the decline of AE amplitude b-value could be considered as precursor information to indicate the rupture of asphalt mixtures. In a word, the variation of AE energy and AE amplitude b-value could effectively describe the stage characteristics of fatigue damage process of asphalt mixtures with different rest time. It is notable that in test 3, however, the longest rest time does not significantly improve the resistance of AC-16 to fatigue damage. Therefore, it is necessary to explore the evolution process and distribution form of microcracks in SCB specimens and reveal the fatigue damage mechanism of asphalt mixtures.
[7]. To describe the fatigue crack growth and understand the fracture mechanism of asphalt mixtures, the spatial distribution of AE events was tracked and exhibited on the surface of the SCB specimen. Based on the proposed location method, the Morlet wavelet was employed to conduct CWT with a scale range from 1 to 64 for each AE event. The TOA was obtained by the highest energy of the waveform projected in time domain. The results of PLB test show that the average AE wave velocity of asphalt mixtures in test 1, test 2 and test 3 are 3.66 km/s, 3.74 km/s and 3.68 km/s, respectively.
(a) Location results and crack propagation path of test 1
3.2. Correlation between AE location and crack propagation The AE events are mainly caused by the formation and propagation of microcracks. The crack propagation path of asphalt mixtures could be located by the spatial distribution of AE events
(b) Location results and crack propagation path of test 2
(c) Location results and crack propagation path of test 3 Fig. 7. Curves of AE amplitude b-value versus cycles of asphalt mixtures.
Fig. 8. Spatial locations of AE events plotted on SCB specimens.
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Through the above analysis of AE characteristics and mechanical behaviors of SCB specimens, it shows that macrocracks occur during the fifth loading cycle in test 1, while develop during the sixth loading cycle in test 2 and test 3. Fig. 8 shows the location result of AE events plotted on the SCB specimens. The star shape and square shape denote the locations of AE events before and after the formation of macrocracks, and the round shape indicates the locations of the four AE sensors plotted on the same surface. With respect to test 1, a small number of AE events are detected at the stage of initiation and concentration of microcracks. The located AE events are mainly distributed near the coarse aggregate along the center axis of the specimen. With the increase of load level, the number of AE events significantly increases and distributes along the vertical direction of the SCB specimen. In test 2, however, the development of microcracks produces more AE events, which obviously concentrate on the left of the center axis of the SCB specimens. After the formation of macrocracks, the quantity of AE events is still increasing and the distribution of AE events is denser along the fracture surface. Similar to test 1, the early AE behavior of asphalt mixtures is not obvious in test 3. Whereas, the distribution of AE events is scattered and disordered. In the macrocracks propagation stage, the AE events suddenly increase, and the distribution scales of both horizontal and vertical directions distinctly expand. Furthermore, by comparing the morphology of the fracture surface, it is found that circumferential alignment of large-size aggregate is obvious in the AC-16 specimen of test 2. The random distribution of small-size aggregate in the center of the specimen is easier to generate microcracks, accompanied by active AE events. However, in test 2 and test 3, AE events are mainly observed near the interface between asphalt binder and coarse aggregate, implying that the large-size aggregate distributed near the center axis of SCB specimens inhibit the formation of microcracks to a certain extent. Notably, for the location results of test 2 and test 3, AE events are not only large in quantity, but extensive in distribution, which reflects the influence of rest time on the complex fracture behavior of asphalt mixtures. The crack propagation path of SCB specimens could be identified through the spatial locations of accumulated AE events. The distribution of AE events develops from a relatively localized area to a larger area and is affected by the microstructures of asphalt mixtures and loading modes. Therefore, it is of great significance to understand the fatigue damage mechanism of asphalt mixtures under different working conditions by monitoring AE events.
The results show that in the second loading cycle, the RF of test 1, test 2 and test 3 are all greater than 1. Subsequently, the RF decreases to <1. In the fifth loading cycle, the RF of test 1 declines to 0.41. While the RF values of test 2 and test 3 reduce to 0.42 and 0.47, respectively, in the sixth loading cycle. After that, the RF values of all the tests vary from 0 to 0.33. In terms of the above study on the AE characteristics associated with the fatigue damage state, the variation of RF during the fatigue damage evolution of asphalt mixtures can be divided into three stages. As presented in Fig. 9, in the initial loading cycle, 1 RF, the Kaiser effect is clear, resulting from discontinuities generated by deformation and friction of primary pores and micro defects in asphalt mixtures. The memory effect of asphalt mixtures is significant since they are not damaged at lower stress. During the initiation and concentration of microcracks, 0.4 RF < 1, the Felicity effect occurs. The RF values of test 1 and test 3 are closer to 1, illustrating that the integrity of asphalt mixtures is affected. The RF of test 2 reduces to about 0.8, however, which suggests that the movement and expansion of discontinuities in asphalt mixtures are more active that inhibit the Kaiser effect. As the level of cyclic load increases, RF values substantially decrease for the reason that the accumulation of numerous microcracks make the AE expression of asphalt mixtures more sensitive. When the SCB specimens enter the stage of macrocracks propagation, RF < 0.4, the AE process of asphalt mixture has completely lost irreversible characteristic. The Kaiser effect dramatically decays, since the integrity of SCB specimens is seriously damaged. The macro fracture behavior of asphalt mixtures is intensified, generating stronger AE activities. Therefore, in the stage of deformation and compaction, an obvious Kaiser effect occurs in asphalt mixtures when exceeding the maximum ‘‘memorized” stress value. Once entering the stage of microcracks initiation and concentration, the Felicity effect of AE process gradually appears, which reveals that the integrity of the SCB specimen has a significant impact on its stress memory. To further distinguish the influence of loading mode on irreversible evolution of early fatigue damage in asphalt mixtures, the AE signal corresponding to the occurrence of the Kaiser effect (RF 1) and the Felicity effect (once RF < 1) was defined as the Kaiser effect point and the Felicity effect point, respectively. During the process of the test, the Kaiser effect point occurred at the 2nd load cycle, which reflected the AE memory of asphalt mixtures in the stage of deformation and compaction. And the Felicity effect point occurred at the 3rd load cycle, indicating the AE response affected by the development of microcracks in asphalt mixtures. The energy distribution characteristics of related signal waveforms were obtained by using CWT. The Morlet wavelet was applied and
3.3. Irreversible characteristics of the AE process The Kaiser effect is a phenomenon commonly observed in materials subjected to repeated cycles of external forces, which describes the AE memory effect of materials on loading history. In materials presenting AE response under a certain load, the Kaiser effect could be characterized by the absence of AE until that load is exceeded; when that load has not reached, the significant increase of AE activities is regarded as the Felicity effect. It shows that the Felicity effect, rather than the Kaiser effect, would exist in asphalt mixtures in certain cases. [32,34]. To investigate the AE memory formation and characterize the damage evolution of asphalt mixtures under cyclic loading with different rest time, the variations of Felicity ratio (RF) were analyzed. Generally, for different materials, the threshold for rAE can be set at AE count exceeds a certain value [29]. In this paper, AE counts greater than 20 was utilized as the stress threshold for rAE. Besides, the occurrence of AE activities after re-loading was considered effective when at least four AE signals that met the threshold were continuously acquired.
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Fig. 9. RF value for asphalt mixtures in each loading cycle.
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Fig. 10. Time-scale distribution for the Kaiser effect points and the Felicity effect points.
the scale range was set from1 to 64. The horizontal coordinate denotes the sampling points of the waveform, and the longitudinal coordinate represents scales. In the legend on the right, the high energy density is shown in yellow and the low energy density in blue. The energy variation of the waveforms of Kaiser effect and Felicity effect points with time and scale are compared, as presented in Fig. 10. With regard to test 1, test 2 and test 3, it can be seen that the main impulse component of Kaiser effect point appears at 977 ls, 794 ls and 421 ls, corresponding to the characteristic frequency of 26.2 kHz, 26.2 kHz and 25.4 kHz. In addition, the bandwidth of high energy area in the time domain is 62 ls, 256 ls and 220 ls and in the frequency domain is 3.4 kHz, 19.8 kHz and 8.9 kHz. Compared with test 1, the characteristic frequency of point in test 2 and 3 appears earlier, and the distribution range of high energy are is obviously larger. It demonstrates that the deformation of asphalt mixtures is recovered during the rest time, leading to little changes in the inhomogeneity of specimens. Hence, the propagation of AE signal after reloading is faster with less interference. Comparatively, under the three different cyclic test conditions, the main impulse components of Felicity effect points appear at 630 ls, 786 ls and 884 ls, corresponding to the characteristic frequency of 25.39 kHz, 25.39 kHz and 23.90 kHz, respectively. Obviously, the projection length of high energy area in the time domain is 156 ls, 224 ls and 245 ls, and the projection length in the frequency domain is 6.5 kHz, 21.9 kHz and 10.3 kHz. Compared with test 1, the occurrence of characteristic frequency in test 2 and test 3 is delayed and the high energy area is still large. Such phenomenon is caused by the self-adjustment of asphalt mixtures during the rest time, which becomes more active due to the increase and connection of minor defects. After reloading, the energy com-
ponents of AE signals related to different damage mechanisms change, and its concentration in time–frequency domain becomes poor. Notably, the characteristic frequency of Felicity effect points is lower than that of Kaiser effect points, and the energy distribution of the corresponding waveform is more complex. Evidently, the wavelet scalogram could effectively identify the micro damage mechanism associated with the Kaiser effect and the Felicity effect, which could provide a reference for the early damage detection of asphalt pavements. 4. Conclusions Based on the dynamic SCB tests and AE tests of AC-16, the changes of different AE parameters, such as energy, amplitude bvalue and event, and the existence of the Kaiser effect phenomenon were explored to characterize the fatigue behavior at various stages of damage evolution of asphalt mixtures. The main conclusions are presented as follows. (1) The temporal variation of AE energy and AE amplitude bvalue are related to the mechanical behaviors of asphalt mixtures during the fatigue damage process. The rest time between each loading cycle would affect the intensity of AE activities, which could be reflected by the ‘‘blank area” in the temporal distribution of AE energy. With the increase of rest time, the magnitude of AE energy decreases, and the feature of ‘‘blank area” gradually disappears at the stage of microcracks initiation and concentration, demonstrating that asphalt mixture has a better resistance to deformation. Additionally, the AE amplitude b-value could effectively determine the transformation of fatigue damage condition
X. Qiu et al. / Construction and Building Materials 240 (2020) 117961
of asphalt mixtures, and the inflection point of the decline of AE amplitude b-value curve could be considered as a precursor to the rupture of asphalt mixtures. (2) The spatial distribution of AE events could identify the crack propagation path of asphalt mixtures, and their dynamic development reveals the fatigue damage in asphalt mixtures develops from a relatively localized area to a larger area. The locations of AE events also reflect the influence of the microstructures of asphalt mixtures and loading modes on the fatigue damage process. The large-size aggregate could inhibit the formation of microcracks to some extent, generating less AE events. Affected by the rest time probably, AE events are more generated and widely distributed in asphalt mixtures. (3) The change of Felicity ratio could be divided into three stages, corresponding to the deformation and compaction stage where 1 RF, microcracks initiation and concentration stage where 0.4 RF < 1, and macrocracks extension stage where RF < 0.4. The Kaiser effect of AE process mainly occurs in deformation and compaction stage of asphalt mixtures. Once entering the stage of microcracks initiation and concentration, Kaiser effect gradually disappears and Felicity effect begins to play a role. The wavelet scalogram of AE waveform could identify the micro damage mechanism associated with the Kaiser effect and the Felicity effect. It shows that the Kaiser effect point propagates faster with the increase of rest time, while the time–frequency concentration of Felicity effect point become worse in general. In conclusion, AE technique is effective in describing and evaluating the fatigue damage behaviors and characteristics of asphalt mixtures. Meanwhile, due to the sensitivity of Kaiser effect, it is significant to determine the formation and propagation of irreversible damage in asphalt pavements. 5. Compliance with ethical Standards: Funding: This work was supported by the Natural Science Foundation of Zhejiang Province [grant numbers LY18E080020, LQ20E080009]; and Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC [grant number MK201901]. CRediT authorship contribution statement Xin Qiu: Conceptualization, Writing - review & editing, Supervision, Project administration. Jingxian Xu: Formal analysis, Investigation, Validation, Writing - original draft. Wenyi Xu: Investigation, Data curation, Validation, Writing - original draft. Shanglin Xiao: Data curation, Validation. Feng Wang: Writing review & editing. Jie Yuan: Writing - review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement The authors are great grateful for the assistance from Yujie Wang, Weihao Cheng, Ningning Li and Ganghua Hu in conducting experiments for this paper. The authors would like to thank Xiaoxiao Tang for commendable opinions and revising the English style of the manuscript, which improved its legibility.
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