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Effect of propagation distance on acoustic emission fracture mode classification in textile reinforced cement
Construction and Building Materials 152 (2017) 872–879
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Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
Effect of propagation distance on acoustic emission fracture mode classification in textile reinforced cement D.G. Aggelis a,⇑, M. El Kadi a, T. Tysmans a, J. Blom b a b
Department of Mechanics of Materials and Constructions, Vrije Universiteit Brussel, Belgium Faculty of Applied Engineering Sciences, Universiteit Antwerpen, Belgium
h i g h l i g h t s Cracking and delaminations/pull-out of TRC are monitored by AE. Multiple sensors are used to examine the effect of wave propagation distance. Basic parameters of AE change with propagation distance. Classification success between different modes decreases for further sensors. Classification boundaries depend on the source-sensor distance.
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Article history: Received 20 August 2016 Received in revised form 20 April 2017 Accepted 28 June 2017
Keywords: Acoustic emission Textile reinforced cement Fracture mode classification Frequency RA value Linear classifier
a b s t r a c t Textile reinforced cement (TRC) is a composite material being increasingly used for load bearing applications. Damage in TRC as in all cementitious materials is an important issue in civil engineering. Acoustic emission (AE) exhibits promising outcomes in laboratory and in in-situ monitoring applications. Evaluation of the fracture mode is crucial as generally, shearing phenomena occur later than tensile (bending) cracking and indicate more severe damage. The acoustic signatures of the damage modes influence most of AE parameters including the average frequency AF and RA-value. However, there are no universal classification boundaries between tensile and shear signals mainly due to geometric effects, material properties, as well as sensor location and response function. In order to highlight this problem and discuss the possibility of a solution, the study occupies not only with the evaluation of the damage mode based on AE parameters but in addition uses multiple sensors to investigate the effect of the wave propagation distance. This is crucial in thin cementitious laminates since damping, scattering, reflections and plate wave dispersion seriously distort the signal having a strong effect on the classification result. It is seen that the classification boundaries between tensile and shear fracture should incorporate the information of propagation distance. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction AE is a technique used for health monitoring purposes of several types of structures and materials. Piezoelectric sensors are placed on the surface of the material and record the transient motion of the surface under the excitation of the elastic waves emitted by the cracking sources [1]. The rate and other parameters of the received signals depend on the damage process and are used for the monitoring of fracture, creep, corrosion, healing in cementitious materials [2–6]. AE is already a part of structural health mon⇑ Corresponding author. E-mail address: [email protected] (D.G. Aggelis). URL: http://www.vub.ac.be/MEMC/en (D.G. Aggelis). http://dx.doi.org/10.1016/j.conbuildmat.2017.06.166 0950-0618/Ó 2017 Elsevier Ltd. All rights reserved.
itoring procedures [7–10]. However, this is still largely based on the activity rate and amplitude of the signals, which are certainly sensitive to the severity of the fracture process. Furthermore, fracture consists of different successive mechanisms as the material is led to ultimate fracture. Therefore, identification of the mode of fracture, provides more information concerning the damage stage and allows predictions relatively to the useful life span. Different fracture processes have distinct AE patterns enabling the characterization of the fracture stage. In a material model like TRC used in this study, these mechanisms can be matrix cracking, debonding of successive layers and pull out of fibres. The first mechanism is easily activated by tensile stress due to the brittleness of the matrix while the other two are more related to shear. Typical AE waveforms are seen in Fig. 1. Among others, some of the most basic
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Fig. 1. Typical damage modes and their corresponding AE waveforms.
features are the amplitude, A (Volts or dB), the rise time, RT (ls) and the RA-value (ls/V) which is RT over A. RT and RA have been shown sensitive to the fracture mode, and obtain low values for tension-related phenomena and higher for shear, either in the form of debonding or pull-out [11,12]. This is connected to the elastic wave modes which are excited by the direction of displacement of the crack sides. Specifically for laminated composites, matrix cracking has been connected to stronger first symmetric mode, while debonding to stronger antisymmetric [13,14], see Fig. 1. The ‘‘energy” as measured by the envelope of the waveform is also used for characterization of fracture events [15]. Frequency content is measured by the average frequency, AF, which is the number of threshold crossings (counts) over the waveform duration and the central frequency, CF, which is the centroid of the frequency spectrum. In certain cases, when six or more sensors are recording each event it is possible to characterize the mode of the source by moment tensor analysis [16]. This is not always practical in all cases while in thin structures it cannot be applied due to plate wave dispersion that distorts the wave front. Therefore, a simpler characterization is attempted in cementitious media [17,18]. It is based on a simple two-dimensional ‘‘plot” of the AE characteristics. ‘‘Tensile” signals exhibit higher frequency and lower RA values than ‘‘shear” and can therefore, be discriminated. This has been confirmed in a number of recent studies [19–22]. In small scale tests the successful classification may reach almost absolute levels [18]. However, the results are not straightforward for larger scale. This is due to the strong effect of wave propagation in the heterogeneous material. Damping, scattering and reflections effectively distort the signals, seriously altering their amplitude, rise time, RA, duration and frequency components. Therefore, more elaborate pattern recognition approaches have been applied with the aim of resulting to classification of acceptable engineering value [21,23]. In any case, the effect of the propagation should be taken into account. Failure to account for this effect, will certainly mask the original information of the AE signals. The reason is that by losing frequency and by ‘‘stretching” in time domain, the originally short, high frequency waveforms of matrix cracking tend to resemble the lower frequency and longer shearing ones [20,24]. Since the waveform parameters change with propagation, the classification boundaries between tensile and shear signals should also be adjusted. A previous study in cementitious mortar beams under bending showed that the classification boundaries change even for an additional distance of 40 mm [23]. Furthermore the classification success was increased by treating the data of
different sensors separately. This was because the effect of attenuation and scattering dispersion is the same to all the signals travelling the same distance influencing the waveforms in a similar manner. In this case, a more detailed study takes place since the AE is recorded by five sensors in order to track the changes of the signal for longer distances. As the distance from the fracture zone increases, the effect of attenuation, dispersion and reflections is accumulated on the AE signals, rendering essential some special consideration. In this case the AE populations received at increasing distances are treated separately to examine the effect of the aforementioned factors on the classification success. The material model is textile reinforced cement (TRC), a relatively new material that comes into thin geometries and is used for external reinforcement of concrete members. Details about the material are given in the next section. 2. Brief introduction in TRC materials Textile reinforced cements (TRC) containing a high content of fibres (more than 20%) results in a strain hardening tensile behaviour. This makes them suitable for structural applications, like strengthening, retrofitting and manufacturing of moulds [25]. The chemically bounded calcium phosphate cement used in this work was developed at ‘‘Vrije Universiteit Brussel” and is called Inorganic Phosphate Cement (IPC). Due to its neutral pH after hardening, it is a cementitious material which can be combined with standard E-glass fibres [26]. IPC is mixture of powder and liquid which after hardening becomes a strong, durable, and heat resisting material. The use of TRC made by IPC and E-glass fibres, has certain beneficial aspects mainly related to the material’s fire resistance and the reduced cost by using cheap E-glass fibres instead of carbon fibres. Previous research has already indicated the potential of glass fibre reinforced IPC as a strengthening and repair method for concrete beams [27–31]. After these preliminary studies, there is a strong need to understand the mechanisms responsible for the mechanical and fracture behaviour of such materials. This material exhibits clear changes in its AE behaviour throughout loading until failure making it a good model to study AE populations coming from different sources [32]. This changing AE behaviour is related to the degrading stiffness of such TRC during loading. The evolution of the stiffness of a brittle cementitious composite during loading is described by the ACK (Aveston, Cooper and Kelly) theory [33]. For completeness, the basic stages defined by the theory are given below.
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a) The first stage occurs when the specimen is still undamaged, both fibre and matrix are responsible for the load bearing capacity and the TRC stiffness can be found using the law of mixtures. b) The second stage occurs when cracks start appearing in the matrix material. Subsequent small cracks will be generated until the matrix is fully cracked. This stage is generally referred to as the ‘‘matrix-cracking” stage. c) In the third stage only the fibres are responsible for the load bearing capacity until specimen failure due to fibre pull-out or debonding [33]. Therefore, this material offers the possibility to monitor failure mechanisms related to tension like the matrix cracking and the ones related to shear that will eventually lead to the final termination of the structural capacity of the material. Indeed in this study, in order to treat as clear populations as possible, two stages are targeted: the early part of matrix cracking during low load and the final AE population received after the load drop or macroscopic fracture when the matrix does not contribute anymore as the cracking has been saturated and the only possible mechanisms are fibre pull out and delaminations. It is mentioned that due to the bending mode, both tensile and compression stresses are developed. However, the early age cracking is bound to occur due to tension as the tensile strength of the matrix is low (cracks at 5 MPa), while the compressive strength of approximately 60 MPa will be exceeded close to final failure. In addition, fibre rupture is possible but not promoted due to the weak matrix-fibre interface that leads to debonding. 3. Experimental details 3.1. Material The inorganic calcium phosphate (IPC) matrix is a mixture of a calcium silicate powder and a phosphoric acid based solution of metal oxides. The weight ratio of powder to liquid is 1/0.8. For the mixing a Heidolph RZR 2102 overhead mixer is used. First the liquid and the powder are mixed at 250 rpm until the powder is mixed into the fluid, after which the speed is increased to 2000 rpm. E-glass chopped glass 2D-random fibre mats with a surface density of 300 g/m2 (Owens Corning M705-300) are used as reinforcement. Chopped Strand Mats are composed of chopped glass fibre strands that are bonded together into mats, either with an emulsion binder or with a small amount of highly soluble polyester powder. No functional coating was applied. The mat itself is flexible and their bending stiffness can be neglected. All eightlayered IPC laminates are made by hand lay-up with an average matrix consumption of 800 g/m2 for each layer, which results in an average fibre volume fraction (Vf) of 20%. Laminates are cured under ambient conditions (20 °C) for 24 h. Post-curing is performed at 60 °C for 24 h while both sides are covered with plastic to prevent early evaporation of water. The dimensions of the TRC specimens are: 350 50 4 mm. The laminates were loaded in a threepoint bending test. In this case the span between the supports was set to 330 mm. Other laminates were tested with bottom spans of 250, 150 and 60 mm. For all cases 3 different specimens were used yielding identical results. The test was performed using an Instron 5885H universal testing machine using a loading rate of 2 mm/min. 3.2. AE monitoring The acoustic part of the study was conducted by means of multiple sensors. This was necessary since one of the main aims was
Fig. 2. Experimental setup, (a) schematic representation, (b) photograph.
the assessment of the influence of propagation distance on the waveform parameters and eventually the source classification. In total five sensors were applied as seen in the sketch and photograph of Fig. 2. Four of them were placed at the same side of the fracture zone in order to examine the same emissions after different propagation distances, while the first was placed at the opposite side in order to obtain source location results. The type of the sensors was ‘‘pico” of Mistras Holdings. They have a relatively high bandwidth, with a centre frequency of 450 kHz. Acoustic coupling was improved by vaseline grease while the sensors were secured by the use of tape during the loading. The acquisition threshold was set to 35 dB and the pre-amplifier gain at 40 dB. The signals were recorded in an 8-channel micro-Samos system with sampling rate of 10 MHz. 4. Fracture behaviour of TRC and AE During bending of these laminates, matrix cracks are the first expression of damage under bending (Fig. 1). With the increase of load the matrix cracks are gradually saturated and give their place to other type of phenomena, namely fibre pull-out and delaminations, in accordance to ACK theory [33]. The two latter mechanisms are related to shear and are activated nominally at load and strain levels higher than the matrix cracking. Cracking which is progressively exhausted and, pull-out and delaminations that increase with load may be overlapping for most of the time of the test. Finally, if the loading continues after the moment of macroscopic failure, the major mechanism that remains active is the delaminations and pullout of fibres that are bridging the crack sides. This was particularly targeted in our study in order to lead to AE populations which are quite distinct. Therefore, the displacement of the load cell was continued even after the load drop and after the material had macroscopically failed (see Fig. 3a). The AE data received after load drop (Fig. 3b) were used as representative of shearing action (pull-out and further delaminations) while the AE data received at the start were representative of cement cracking due to bending. The different sources reasonably emit AE signals with different characteristics and therefore, the possibility to identify them based on simple waveform parameters is investigated herein. For this reason indicative parts of the population (early-before the load drop, and late-after the drop) are compared and the classification results are discussed. The gravity of the research is not given on the pattern recognition itself and reporting of the performance of different algorithms. The aim is focused on
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Fig. 3. (a) Load history (b) cumulative AE activity recorded by the four sensors (#2 [25 mm] to #5 [100 mm] of Fig. 2a).
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Fig. 4. (a) AE events localization. 94% of the events occur within a zone of 50 mm in the middle. (b) Photograph of a laminate after failure.
the boundary between tensile and shear signals, as defined by simple AE features and how this is shifted when the propagation distance increases. Classification is conducted for the different sensors separately as well as altogether for the whole population regardless the sensor location. The distortion that is accumulating especially to the further sensors due to reflections, scattering, damping and plate wave dispersion, has a crucial effect on the boundaries of classification between cracking and debonding, seriously masking the results if it is not accounted for. 5. General results The cumulative AE behaviour is seen in Fig. 3b separately for each sensor. The rate of AE smoothly increased until the moment of load drop, due to macroscopic material failure. The rate of cracking reasonably increased with the rise of load as the stress locally surpassed the strength in continuously increasing points. At the moment of breaking, a burst in the rate is noted due to the extensive failure events. As explained above, the final activity is dominated by shearing events since matrix cracking has been saturated. The sensor at 25 mm from the centre reasonably recorded the highest number of hits due to the proximity to the fracture events, while the rest exhibited slightly lower activity according to their distance. As expected in any case of three-point bending the localized fracture area lies in the centre of the specimen. This is shown in the example of Fig. 4 where the AE event distribution is shown relatively to the length of the specimen. It is obvious that the vast majority of the events (specifically 94%) occur in between sensors 1 and 2 within a zone of 50 mm in the centre of the specimen. Therefore, it can be supported that the recorded activity by all sensors comes from this specific zone. This is also shown in Fig. 4b in a photograph after the test, where the shape of the failed laminate demonstrates the severe damage near the load application point. Apart from the cumulative number of emissions, some of their parameters are presented in Fig. 5a and b. These concern the time evolution of AF and RA. Again the graphs contain different lines for
Fig. 5. Moving average of (a) AF and (b) RA value of AE signals as recoded by different sensors.
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the different transducers so that the effect of distance is evident. The lines are the moving averages of the recent 300 points. Considering the AF it is seen that the value decreases smoothly for each sensor until the moment of macroscopic fracture. At that moment there is a sudden drop while later the line never recovers to the pre-peak levels. This signifies the different type of sources that are active at the two stages (before and after the peak load) and the sensitivity of AE as monitoring technique. Nevertheless, there is also a clear difference between the successive sensors. While the initial emissions received at the closest sensor to the central fracture zone fluctuates around 275 kHz, the further sensor at 100 mm exhibits frequencies between 150 and 200 kHz. There are similar differences after the peak, though of less impressive nature. This downshift of frequency for further distances is certainly related to damping and scattering and has been investigated in ultrasonic studies of cementitious materials [34–36]. The results are similar for the RA value, though with the inverse trends. In this case, the closest sensor exhibits the lowest values before and after the load drop. This result is related to the velocities of the different wave modes, as already discussed. With the propagation of the waveform, the delay between the second and first wave packets (presumably 1st symmetric and antisymmetric respectively) increases. This creates a longer waveform, with higher rise time and duration, decreasing also the rise angle (see Fig. 1). In any case, the difference between the pre-peak and post-peak areas are again obvious, indicating that after macroscopic failure, shearing due to debonding and pull out is dominant, while earlier the AE activity came mainly from matrix cracking. 6. Classification To get an impression of the scatter of the parameters of AE, Fig. 6 presents the whole data population of AF as recorded by sensor 2, (near the crack). The frequencies vary throughout the band 0–500 kHz. Nevertheless, the lowest bands (e.g. below 200 kHz) become more frequently populated as the time increases and after the load drop they cause a strong decrease of the average line. In order to try to characterize the sources in a more robust way, indicative parts of the populations were selected. The first 500 hits reasonably corresponding to matrix cracking formed one class (‘‘1”), while the last 500 hits, corresponding to shearing (pull-out and debonding) formed another (‘‘0”, see Fig. 6). The same procedure was repeated for the population of each one of the sensors separately. This separation is based on the actual mechanical behaviour of the material in accordance to ACK theory. The initial AE is expected to originate mostly from matrix cracking, while the latest AE originates from delaminations and pullout events. The population of 500 was selected in order for the classes to have
Fig. 6. Full population of AF values received by sensor #2 (close to the centre) according to time.
adequate statistical significance. Larger classes would be possible but then it would be more dangerous to mix different failure mechanisms in the same class, since they may overlap during loading. Fig. 7 shows the two populations (early and late group) in correlation plot of AF vs. RA as prescribed by the corresponding recommendation [17]. It is obvious that the populations are not identical with class ‘‘1” (matrix cracking) exhibiting higher frequency and lower RA than ‘‘0” (shearing). However, the overlap is very strong. In order to check the possibility of classification, a simple algorithm was applied for separation in two clusters in two-dimensional space. A pre-process to reduce the variance to one was also applied before the main pattern recognition action. Using the software Noesis, the ‘‘linear classifier” was selected for supervised pattern recognition. The odd numbers of data were assumed as training system and the even were the test set. This created equal training and testing sets of 500 vectors each for all distances, except for the closest; in this case the populations of sensor 1 and 2 were merged as they share the same distance from the centre, making training and testing sets of 1000 vectors each. The outcome of the linear classifier is the separation of the test set in two clusters by a simple line (in two dimensional space). The result of the separation is seen in Fig. 8a where the population of the assumed cracking lies at the top of the graph and debonding/ pullout at the bottom. This linear boundary resulted in an error of 21.4%, meaning that there are 214 out of 1000 points that are misclassified. The straight line separating in the best possible way the populations is seen in Fig. 9 for the case of 25 mm propagation distance. After repeating the same procedure for the populations of the successive sensors, the classification results are shown in the rest of the subfigures in Fig. 8. It is obvious that the classification boundary changes steadily as the sensor distance increases. The matrix cracking population expands towards the bottom of the chart, while the shear population is suppressed to lower frequencies and slightly higher RA values. The comparison is characteristic for the furthest sensor (Fig. 8d) where the classification line seems to have been shifted downwards by almost 100 kHz relatively to Fig. 8a (closest sensor). The different classification boundaries for all sensors are included in Fig. 9. There, it is obvious that the boundaries between cement matrix cracking and debonding/pull out strongly depend on the propagation distance between the source and the sensor. If the linear classifier algorithm is applied to the populations of all sensors treated as a unified group, the boundary is in between
Fig. 7. Correlation plot between AF (kHz) and RA (ls/V) for the two classes of AE data (1 = matrix cracking, 0 = debonding, pull-out). Data come from sensors #1 and #2 with 25 mm distance from the damage zone.
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Fig. 8. Correlation plot between AF (kHz) and RA (ls/V) for the two classes of AE data (1 = matrix cracking, 0 = debonding, pull-out) after application of the linear classifier on the test set. Data come from sensors: (a) #1 and #2 with 25 mm distance from the damage zone, (b) #3, 50 mm, (c) #4, 75 mm, (d) #5, 100 mm.
Fig. 9. Classification boundaries between matrix cracking and debonding/pullout signals based on populations from sensors at different distances from the damage zone.
the individual curves. This simple analysis shows in a vivid way that classification of AE sources should always be combined with the knowledge of the propagation distance. Even in the small scale of this experiment, tremendous differences appear in the
classification for a few cm of additional propagation, implying that the effect would be much higher for large scale. This could be combined with the recommendation of practical use [17]. Classifying the sources based on waveform parameters like AF and RA is a very effective way considering its simplicity. Additionally, the next step is to realize moveable boundaries depending on the distance between the source and the sensors, as seen in Fig. 10. Since the boundaries change, the classification should be conducted separately for different populations according to the distance between the source and the receiver. Fig. 11 shows the classification error based on the data obtained by the specific test setup and the linear classifier. It is clear that as the receiving transducer gets more distant from the fracture zone, the classification error percentage increases (up to 31.4%). Treating the data as unified population results in a moderate error of approximately 25%, while the lowest error percentage (21.4%) comes from the closest distance of 25 mm, where the effect of attenuation and reflections is arguably the weakest than for the longer distances. The fact that the classification boundaries move according to the propagation distance is the result of translation of the average of the populations, see Fig. 12. The average value of frequency (a) exhibits a smoothly decreasing trend for longer distances. Both the initial population (cracking) and the late population (shearing) exhibit a drop of approximately 30% compared to the closest sensor.
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It is mentioned that other classifiers (nearest neighbourhood, NNC [37]) resulted in lower error percentages (between 10 and 20%). However, the linear classifier is discussed herein as it results in a complete separation of the two populations by a straight line and allows direct comparison between the boundaries derived for different location of sensors. Additionally, the performance of the linear classifier can be improved by including energy-related parameters in three- (or higher) dimensional space. This is not presented herein as the energy emitted by a fracture event strongly depends on the formed fractured area. A matrix cracking event involving small new created area will emit low AE energy, while if the same mode of fracture creates a large new area the energy will be higher. The same holds for debonding. Therefore, despite in certain cases AE energy can solely be used for fracture type identification [6,13,15] in this case it was not applied. Fig. 10. Classification boundaries for AE signals in TRC laminates for different sensor-crack distances.
7. Conclusions The present paper reports on AE behaviour of TRC laminates under bending. Multiple broadband sensors were applied in order to examine the effect of distance on the waveform parameters. Monitoring of the AE behaviour during bending of the laminates enlightens the actual fracture mechanisms, their activation and dominance throughout loading and allows an evaluation of the current state of the material. According to the ACK theory of composites, the initial AE is assigned to matrix cracking while the latest AE is related to shearing through debonding and pull out. The main conclusions follow:
Fig. 11. Classification error as percentage of misclassified vectors of the test set for AE collected at different distances from the fracture zone.
Therefore, considering that the boundary is in between the averages it is reasonable that it also translates to lower values accordingly. The trend is inverse for RA while the relative change is much higher (Fig. 12b). For the cracking signals the increase is almost 100% for long propagation and even higher for the shearing ones. This increase of RA is related as already mentioned to the increasing delay between the different wave modes included in the same waveform. In addition, it is promoted by scattering on heterogeneities (fibres/pores/cracks), which do not allow straight path propagation and increase the transit time of energy components. Therefore, this effect is expected to be stronger when the damage has been accumulated (population of shearing signals collected at the end of the experiment).
i) The AE characteristics are very sensitive to the fracture mode of Textile Reinforced Cement. Matrix cracking emits higher frequency and shorter AE signals than debonding and pull-out. ii) The distance between the location of damage and the sensor has a significant effect on the propagating waveform. Frequency is strongly downshifted and RA increases for just a few cm of additional propagation. iii) Apart from the effect on the waveform parameters, this study goes one step further showing the effect of sensor location on the final classification. The classification boundaries between tension-related mechanisms (cracking) and shear-related (pull-out, debonding) are strongly influenced by the location of sensors. Therefore, event localization should always be taken into account in order to modify the boundaries between the different modes. The study continues with international round robin tests of TRC material in different laboratories in the framework of Rilem Technical Committee IAM in order to expand the conclusions in different environments and technical equipment [38].
Fig. 12. Dependence of average value of (a) AF and (b) RA on the distance between sensor and fracture zone.
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Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
Effect of propagation distance on acoustic emission fracture mode classification in textile reinforced cement D.G. Aggelis a,⇑, M. El Kadi a, T. Tysmans a, J. Blom b a b
Department of Mechanics of Materials and Constructions, Vrije Universiteit Brussel, Belgium Faculty of Applied Engineering Sciences, Universiteit Antwerpen, Belgium
h i g h l i g h t s Cracking and delaminations/pull-out of TRC are monitored by AE. Multiple sensors are used to examine the effect of wave propagation distance. Basic parameters of AE change with propagation distance. Classification success between different modes decreases for further sensors. Classification boundaries depend on the source-sensor distance.
a r t i c l e
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Article history: Received 20 August 2016 Received in revised form 20 April 2017 Accepted 28 June 2017
Keywords: Acoustic emission Textile reinforced cement Fracture mode classification Frequency RA value Linear classifier
a b s t r a c t Textile reinforced cement (TRC) is a composite material being increasingly used for load bearing applications. Damage in TRC as in all cementitious materials is an important issue in civil engineering. Acoustic emission (AE) exhibits promising outcomes in laboratory and in in-situ monitoring applications. Evaluation of the fracture mode is crucial as generally, shearing phenomena occur later than tensile (bending) cracking and indicate more severe damage. The acoustic signatures of the damage modes influence most of AE parameters including the average frequency AF and RA-value. However, there are no universal classification boundaries between tensile and shear signals mainly due to geometric effects, material properties, as well as sensor location and response function. In order to highlight this problem and discuss the possibility of a solution, the study occupies not only with the evaluation of the damage mode based on AE parameters but in addition uses multiple sensors to investigate the effect of the wave propagation distance. This is crucial in thin cementitious laminates since damping, scattering, reflections and plate wave dispersion seriously distort the signal having a strong effect on the classification result. It is seen that the classification boundaries between tensile and shear fracture should incorporate the information of propagation distance. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction AE is a technique used for health monitoring purposes of several types of structures and materials. Piezoelectric sensors are placed on the surface of the material and record the transient motion of the surface under the excitation of the elastic waves emitted by the cracking sources [1]. The rate and other parameters of the received signals depend on the damage process and are used for the monitoring of fracture, creep, corrosion, healing in cementitious materials [2–6]. AE is already a part of structural health mon⇑ Corresponding author. E-mail address: [email protected] (D.G. Aggelis). URL: http://www.vub.ac.be/MEMC/en (D.G. Aggelis). http://dx.doi.org/10.1016/j.conbuildmat.2017.06.166 0950-0618/Ó 2017 Elsevier Ltd. All rights reserved.
itoring procedures [7–10]. However, this is still largely based on the activity rate and amplitude of the signals, which are certainly sensitive to the severity of the fracture process. Furthermore, fracture consists of different successive mechanisms as the material is led to ultimate fracture. Therefore, identification of the mode of fracture, provides more information concerning the damage stage and allows predictions relatively to the useful life span. Different fracture processes have distinct AE patterns enabling the characterization of the fracture stage. In a material model like TRC used in this study, these mechanisms can be matrix cracking, debonding of successive layers and pull out of fibres. The first mechanism is easily activated by tensile stress due to the brittleness of the matrix while the other two are more related to shear. Typical AE waveforms are seen in Fig. 1. Among others, some of the most basic
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Fig. 1. Typical damage modes and their corresponding AE waveforms.
features are the amplitude, A (Volts or dB), the rise time, RT (ls) and the RA-value (ls/V) which is RT over A. RT and RA have been shown sensitive to the fracture mode, and obtain low values for tension-related phenomena and higher for shear, either in the form of debonding or pull-out [11,12]. This is connected to the elastic wave modes which are excited by the direction of displacement of the crack sides. Specifically for laminated composites, matrix cracking has been connected to stronger first symmetric mode, while debonding to stronger antisymmetric [13,14], see Fig. 1. The ‘‘energy” as measured by the envelope of the waveform is also used for characterization of fracture events [15]. Frequency content is measured by the average frequency, AF, which is the number of threshold crossings (counts) over the waveform duration and the central frequency, CF, which is the centroid of the frequency spectrum. In certain cases, when six or more sensors are recording each event it is possible to characterize the mode of the source by moment tensor analysis [16]. This is not always practical in all cases while in thin structures it cannot be applied due to plate wave dispersion that distorts the wave front. Therefore, a simpler characterization is attempted in cementitious media [17,18]. It is based on a simple two-dimensional ‘‘plot” of the AE characteristics. ‘‘Tensile” signals exhibit higher frequency and lower RA values than ‘‘shear” and can therefore, be discriminated. This has been confirmed in a number of recent studies [19–22]. In small scale tests the successful classification may reach almost absolute levels [18]. However, the results are not straightforward for larger scale. This is due to the strong effect of wave propagation in the heterogeneous material. Damping, scattering and reflections effectively distort the signals, seriously altering their amplitude, rise time, RA, duration and frequency components. Therefore, more elaborate pattern recognition approaches have been applied with the aim of resulting to classification of acceptable engineering value [21,23]. In any case, the effect of the propagation should be taken into account. Failure to account for this effect, will certainly mask the original information of the AE signals. The reason is that by losing frequency and by ‘‘stretching” in time domain, the originally short, high frequency waveforms of matrix cracking tend to resemble the lower frequency and longer shearing ones [20,24]. Since the waveform parameters change with propagation, the classification boundaries between tensile and shear signals should also be adjusted. A previous study in cementitious mortar beams under bending showed that the classification boundaries change even for an additional distance of 40 mm [23]. Furthermore the classification success was increased by treating the data of
different sensors separately. This was because the effect of attenuation and scattering dispersion is the same to all the signals travelling the same distance influencing the waveforms in a similar manner. In this case, a more detailed study takes place since the AE is recorded by five sensors in order to track the changes of the signal for longer distances. As the distance from the fracture zone increases, the effect of attenuation, dispersion and reflections is accumulated on the AE signals, rendering essential some special consideration. In this case the AE populations received at increasing distances are treated separately to examine the effect of the aforementioned factors on the classification success. The material model is textile reinforced cement (TRC), a relatively new material that comes into thin geometries and is used for external reinforcement of concrete members. Details about the material are given in the next section. 2. Brief introduction in TRC materials Textile reinforced cements (TRC) containing a high content of fibres (more than 20%) results in a strain hardening tensile behaviour. This makes them suitable for structural applications, like strengthening, retrofitting and manufacturing of moulds [25]. The chemically bounded calcium phosphate cement used in this work was developed at ‘‘Vrije Universiteit Brussel” and is called Inorganic Phosphate Cement (IPC). Due to its neutral pH after hardening, it is a cementitious material which can be combined with standard E-glass fibres [26]. IPC is mixture of powder and liquid which after hardening becomes a strong, durable, and heat resisting material. The use of TRC made by IPC and E-glass fibres, has certain beneficial aspects mainly related to the material’s fire resistance and the reduced cost by using cheap E-glass fibres instead of carbon fibres. Previous research has already indicated the potential of glass fibre reinforced IPC as a strengthening and repair method for concrete beams [27–31]. After these preliminary studies, there is a strong need to understand the mechanisms responsible for the mechanical and fracture behaviour of such materials. This material exhibits clear changes in its AE behaviour throughout loading until failure making it a good model to study AE populations coming from different sources [32]. This changing AE behaviour is related to the degrading stiffness of such TRC during loading. The evolution of the stiffness of a brittle cementitious composite during loading is described by the ACK (Aveston, Cooper and Kelly) theory [33]. For completeness, the basic stages defined by the theory are given below.
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a) The first stage occurs when the specimen is still undamaged, both fibre and matrix are responsible for the load bearing capacity and the TRC stiffness can be found using the law of mixtures. b) The second stage occurs when cracks start appearing in the matrix material. Subsequent small cracks will be generated until the matrix is fully cracked. This stage is generally referred to as the ‘‘matrix-cracking” stage. c) In the third stage only the fibres are responsible for the load bearing capacity until specimen failure due to fibre pull-out or debonding [33]. Therefore, this material offers the possibility to monitor failure mechanisms related to tension like the matrix cracking and the ones related to shear that will eventually lead to the final termination of the structural capacity of the material. Indeed in this study, in order to treat as clear populations as possible, two stages are targeted: the early part of matrix cracking during low load and the final AE population received after the load drop or macroscopic fracture when the matrix does not contribute anymore as the cracking has been saturated and the only possible mechanisms are fibre pull out and delaminations. It is mentioned that due to the bending mode, both tensile and compression stresses are developed. However, the early age cracking is bound to occur due to tension as the tensile strength of the matrix is low (cracks at 5 MPa), while the compressive strength of approximately 60 MPa will be exceeded close to final failure. In addition, fibre rupture is possible but not promoted due to the weak matrix-fibre interface that leads to debonding. 3. Experimental details 3.1. Material The inorganic calcium phosphate (IPC) matrix is a mixture of a calcium silicate powder and a phosphoric acid based solution of metal oxides. The weight ratio of powder to liquid is 1/0.8. For the mixing a Heidolph RZR 2102 overhead mixer is used. First the liquid and the powder are mixed at 250 rpm until the powder is mixed into the fluid, after which the speed is increased to 2000 rpm. E-glass chopped glass 2D-random fibre mats with a surface density of 300 g/m2 (Owens Corning M705-300) are used as reinforcement. Chopped Strand Mats are composed of chopped glass fibre strands that are bonded together into mats, either with an emulsion binder or with a small amount of highly soluble polyester powder. No functional coating was applied. The mat itself is flexible and their bending stiffness can be neglected. All eightlayered IPC laminates are made by hand lay-up with an average matrix consumption of 800 g/m2 for each layer, which results in an average fibre volume fraction (Vf) of 20%. Laminates are cured under ambient conditions (20 °C) for 24 h. Post-curing is performed at 60 °C for 24 h while both sides are covered with plastic to prevent early evaporation of water. The dimensions of the TRC specimens are: 350 50 4 mm. The laminates were loaded in a threepoint bending test. In this case the span between the supports was set to 330 mm. Other laminates were tested with bottom spans of 250, 150 and 60 mm. For all cases 3 different specimens were used yielding identical results. The test was performed using an Instron 5885H universal testing machine using a loading rate of 2 mm/min. 3.2. AE monitoring The acoustic part of the study was conducted by means of multiple sensors. This was necessary since one of the main aims was
Fig. 2. Experimental setup, (a) schematic representation, (b) photograph.
the assessment of the influence of propagation distance on the waveform parameters and eventually the source classification. In total five sensors were applied as seen in the sketch and photograph of Fig. 2. Four of them were placed at the same side of the fracture zone in order to examine the same emissions after different propagation distances, while the first was placed at the opposite side in order to obtain source location results. The type of the sensors was ‘‘pico” of Mistras Holdings. They have a relatively high bandwidth, with a centre frequency of 450 kHz. Acoustic coupling was improved by vaseline grease while the sensors were secured by the use of tape during the loading. The acquisition threshold was set to 35 dB and the pre-amplifier gain at 40 dB. The signals were recorded in an 8-channel micro-Samos system with sampling rate of 10 MHz. 4. Fracture behaviour of TRC and AE During bending of these laminates, matrix cracks are the first expression of damage under bending (Fig. 1). With the increase of load the matrix cracks are gradually saturated and give their place to other type of phenomena, namely fibre pull-out and delaminations, in accordance to ACK theory [33]. The two latter mechanisms are related to shear and are activated nominally at load and strain levels higher than the matrix cracking. Cracking which is progressively exhausted and, pull-out and delaminations that increase with load may be overlapping for most of the time of the test. Finally, if the loading continues after the moment of macroscopic failure, the major mechanism that remains active is the delaminations and pullout of fibres that are bridging the crack sides. This was particularly targeted in our study in order to lead to AE populations which are quite distinct. Therefore, the displacement of the load cell was continued even after the load drop and after the material had macroscopically failed (see Fig. 3a). The AE data received after load drop (Fig. 3b) were used as representative of shearing action (pull-out and further delaminations) while the AE data received at the start were representative of cement cracking due to bending. The different sources reasonably emit AE signals with different characteristics and therefore, the possibility to identify them based on simple waveform parameters is investigated herein. For this reason indicative parts of the population (early-before the load drop, and late-after the drop) are compared and the classification results are discussed. The gravity of the research is not given on the pattern recognition itself and reporting of the performance of different algorithms. The aim is focused on
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Fig. 3. (a) Load history (b) cumulative AE activity recorded by the four sensors (#2 [25 mm] to #5 [100 mm] of Fig. 2a).
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Fig. 4. (a) AE events localization. 94% of the events occur within a zone of 50 mm in the middle. (b) Photograph of a laminate after failure.
the boundary between tensile and shear signals, as defined by simple AE features and how this is shifted when the propagation distance increases. Classification is conducted for the different sensors separately as well as altogether for the whole population regardless the sensor location. The distortion that is accumulating especially to the further sensors due to reflections, scattering, damping and plate wave dispersion, has a crucial effect on the boundaries of classification between cracking and debonding, seriously masking the results if it is not accounted for. 5. General results The cumulative AE behaviour is seen in Fig. 3b separately for each sensor. The rate of AE smoothly increased until the moment of load drop, due to macroscopic material failure. The rate of cracking reasonably increased with the rise of load as the stress locally surpassed the strength in continuously increasing points. At the moment of breaking, a burst in the rate is noted due to the extensive failure events. As explained above, the final activity is dominated by shearing events since matrix cracking has been saturated. The sensor at 25 mm from the centre reasonably recorded the highest number of hits due to the proximity to the fracture events, while the rest exhibited slightly lower activity according to their distance. As expected in any case of three-point bending the localized fracture area lies in the centre of the specimen. This is shown in the example of Fig. 4 where the AE event distribution is shown relatively to the length of the specimen. It is obvious that the vast majority of the events (specifically 94%) occur in between sensors 1 and 2 within a zone of 50 mm in the centre of the specimen. Therefore, it can be supported that the recorded activity by all sensors comes from this specific zone. This is also shown in Fig. 4b in a photograph after the test, where the shape of the failed laminate demonstrates the severe damage near the load application point. Apart from the cumulative number of emissions, some of their parameters are presented in Fig. 5a and b. These concern the time evolution of AF and RA. Again the graphs contain different lines for
Fig. 5. Moving average of (a) AF and (b) RA value of AE signals as recoded by different sensors.
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the different transducers so that the effect of distance is evident. The lines are the moving averages of the recent 300 points. Considering the AF it is seen that the value decreases smoothly for each sensor until the moment of macroscopic fracture. At that moment there is a sudden drop while later the line never recovers to the pre-peak levels. This signifies the different type of sources that are active at the two stages (before and after the peak load) and the sensitivity of AE as monitoring technique. Nevertheless, there is also a clear difference between the successive sensors. While the initial emissions received at the closest sensor to the central fracture zone fluctuates around 275 kHz, the further sensor at 100 mm exhibits frequencies between 150 and 200 kHz. There are similar differences after the peak, though of less impressive nature. This downshift of frequency for further distances is certainly related to damping and scattering and has been investigated in ultrasonic studies of cementitious materials [34–36]. The results are similar for the RA value, though with the inverse trends. In this case, the closest sensor exhibits the lowest values before and after the load drop. This result is related to the velocities of the different wave modes, as already discussed. With the propagation of the waveform, the delay between the second and first wave packets (presumably 1st symmetric and antisymmetric respectively) increases. This creates a longer waveform, with higher rise time and duration, decreasing also the rise angle (see Fig. 1). In any case, the difference between the pre-peak and post-peak areas are again obvious, indicating that after macroscopic failure, shearing due to debonding and pull out is dominant, while earlier the AE activity came mainly from matrix cracking. 6. Classification To get an impression of the scatter of the parameters of AE, Fig. 6 presents the whole data population of AF as recorded by sensor 2, (near the crack). The frequencies vary throughout the band 0–500 kHz. Nevertheless, the lowest bands (e.g. below 200 kHz) become more frequently populated as the time increases and after the load drop they cause a strong decrease of the average line. In order to try to characterize the sources in a more robust way, indicative parts of the populations were selected. The first 500 hits reasonably corresponding to matrix cracking formed one class (‘‘1”), while the last 500 hits, corresponding to shearing (pull-out and debonding) formed another (‘‘0”, see Fig. 6). The same procedure was repeated for the population of each one of the sensors separately. This separation is based on the actual mechanical behaviour of the material in accordance to ACK theory. The initial AE is expected to originate mostly from matrix cracking, while the latest AE originates from delaminations and pullout events. The population of 500 was selected in order for the classes to have
Fig. 6. Full population of AF values received by sensor #2 (close to the centre) according to time.
adequate statistical significance. Larger classes would be possible but then it would be more dangerous to mix different failure mechanisms in the same class, since they may overlap during loading. Fig. 7 shows the two populations (early and late group) in correlation plot of AF vs. RA as prescribed by the corresponding recommendation [17]. It is obvious that the populations are not identical with class ‘‘1” (matrix cracking) exhibiting higher frequency and lower RA than ‘‘0” (shearing). However, the overlap is very strong. In order to check the possibility of classification, a simple algorithm was applied for separation in two clusters in two-dimensional space. A pre-process to reduce the variance to one was also applied before the main pattern recognition action. Using the software Noesis, the ‘‘linear classifier” was selected for supervised pattern recognition. The odd numbers of data were assumed as training system and the even were the test set. This created equal training and testing sets of 500 vectors each for all distances, except for the closest; in this case the populations of sensor 1 and 2 were merged as they share the same distance from the centre, making training and testing sets of 1000 vectors each. The outcome of the linear classifier is the separation of the test set in two clusters by a simple line (in two dimensional space). The result of the separation is seen in Fig. 8a where the population of the assumed cracking lies at the top of the graph and debonding/ pullout at the bottom. This linear boundary resulted in an error of 21.4%, meaning that there are 214 out of 1000 points that are misclassified. The straight line separating in the best possible way the populations is seen in Fig. 9 for the case of 25 mm propagation distance. After repeating the same procedure for the populations of the successive sensors, the classification results are shown in the rest of the subfigures in Fig. 8. It is obvious that the classification boundary changes steadily as the sensor distance increases. The matrix cracking population expands towards the bottom of the chart, while the shear population is suppressed to lower frequencies and slightly higher RA values. The comparison is characteristic for the furthest sensor (Fig. 8d) where the classification line seems to have been shifted downwards by almost 100 kHz relatively to Fig. 8a (closest sensor). The different classification boundaries for all sensors are included in Fig. 9. There, it is obvious that the boundaries between cement matrix cracking and debonding/pull out strongly depend on the propagation distance between the source and the sensor. If the linear classifier algorithm is applied to the populations of all sensors treated as a unified group, the boundary is in between
Fig. 7. Correlation plot between AF (kHz) and RA (ls/V) for the two classes of AE data (1 = matrix cracking, 0 = debonding, pull-out). Data come from sensors #1 and #2 with 25 mm distance from the damage zone.
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Fig. 8. Correlation plot between AF (kHz) and RA (ls/V) for the two classes of AE data (1 = matrix cracking, 0 = debonding, pull-out) after application of the linear classifier on the test set. Data come from sensors: (a) #1 and #2 with 25 mm distance from the damage zone, (b) #3, 50 mm, (c) #4, 75 mm, (d) #5, 100 mm.
Fig. 9. Classification boundaries between matrix cracking and debonding/pullout signals based on populations from sensors at different distances from the damage zone.
the individual curves. This simple analysis shows in a vivid way that classification of AE sources should always be combined with the knowledge of the propagation distance. Even in the small scale of this experiment, tremendous differences appear in the
classification for a few cm of additional propagation, implying that the effect would be much higher for large scale. This could be combined with the recommendation of practical use [17]. Classifying the sources based on waveform parameters like AF and RA is a very effective way considering its simplicity. Additionally, the next step is to realize moveable boundaries depending on the distance between the source and the sensors, as seen in Fig. 10. Since the boundaries change, the classification should be conducted separately for different populations according to the distance between the source and the receiver. Fig. 11 shows the classification error based on the data obtained by the specific test setup and the linear classifier. It is clear that as the receiving transducer gets more distant from the fracture zone, the classification error percentage increases (up to 31.4%). Treating the data as unified population results in a moderate error of approximately 25%, while the lowest error percentage (21.4%) comes from the closest distance of 25 mm, where the effect of attenuation and reflections is arguably the weakest than for the longer distances. The fact that the classification boundaries move according to the propagation distance is the result of translation of the average of the populations, see Fig. 12. The average value of frequency (a) exhibits a smoothly decreasing trend for longer distances. Both the initial population (cracking) and the late population (shearing) exhibit a drop of approximately 30% compared to the closest sensor.
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It is mentioned that other classifiers (nearest neighbourhood, NNC [37]) resulted in lower error percentages (between 10 and 20%). However, the linear classifier is discussed herein as it results in a complete separation of the two populations by a straight line and allows direct comparison between the boundaries derived for different location of sensors. Additionally, the performance of the linear classifier can be improved by including energy-related parameters in three- (or higher) dimensional space. This is not presented herein as the energy emitted by a fracture event strongly depends on the formed fractured area. A matrix cracking event involving small new created area will emit low AE energy, while if the same mode of fracture creates a large new area the energy will be higher. The same holds for debonding. Therefore, despite in certain cases AE energy can solely be used for fracture type identification [6,13,15] in this case it was not applied. Fig. 10. Classification boundaries for AE signals in TRC laminates for different sensor-crack distances.
7. Conclusions The present paper reports on AE behaviour of TRC laminates under bending. Multiple broadband sensors were applied in order to examine the effect of distance on the waveform parameters. Monitoring of the AE behaviour during bending of the laminates enlightens the actual fracture mechanisms, their activation and dominance throughout loading and allows an evaluation of the current state of the material. According to the ACK theory of composites, the initial AE is assigned to matrix cracking while the latest AE is related to shearing through debonding and pull out. The main conclusions follow:
Fig. 11. Classification error as percentage of misclassified vectors of the test set for AE collected at different distances from the fracture zone.
Therefore, considering that the boundary is in between the averages it is reasonable that it also translates to lower values accordingly. The trend is inverse for RA while the relative change is much higher (Fig. 12b). For the cracking signals the increase is almost 100% for long propagation and even higher for the shearing ones. This increase of RA is related as already mentioned to the increasing delay between the different wave modes included in the same waveform. In addition, it is promoted by scattering on heterogeneities (fibres/pores/cracks), which do not allow straight path propagation and increase the transit time of energy components. Therefore, this effect is expected to be stronger when the damage has been accumulated (population of shearing signals collected at the end of the experiment).
i) The AE characteristics are very sensitive to the fracture mode of Textile Reinforced Cement. Matrix cracking emits higher frequency and shorter AE signals than debonding and pull-out. ii) The distance between the location of damage and the sensor has a significant effect on the propagating waveform. Frequency is strongly downshifted and RA increases for just a few cm of additional propagation. iii) Apart from the effect on the waveform parameters, this study goes one step further showing the effect of sensor location on the final classification. The classification boundaries between tension-related mechanisms (cracking) and shear-related (pull-out, debonding) are strongly influenced by the location of sensors. Therefore, event localization should always be taken into account in order to modify the boundaries between the different modes. The study continues with international round robin tests of TRC material in different laboratories in the framework of Rilem Technical Committee IAM in order to expand the conclusions in different environments and technical equipment [38].
Fig. 12. Dependence of average value of (a) AF and (b) RA on the distance between sensor and fracture zone.
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