A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining

Journal Pre-proof A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining Meisam Gordan, Hashim Abdul Razak, Zubaidah Ismail, Khaled Ghaedi, Zhi Xin Tan, Haider Hamad Ghayeb

PII: DOI: Reference:

S1568-4946(19)30795-1 https://doi.org/10.1016/j.asoc.2019.106013 ASOC 106013

To appear in:

Applied Soft Computing Journal

Received date : 24 June 2018 Revised date : 12 November 2019 Accepted date : 10 December 2019 Please cite this article as: M. Gordan, H.A. Razak, Z. Ismail et al., A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining, Applied Soft Computing Journal (2019), doi: https://doi.org/10.1016/j.asoc.2019.106013. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Elsevier B.V. All rights reserved.

Graphical abstract (for review)

Experimental Modal Analysis

Data Processing

Hybrid ANN-ICA

Dam

• Da • Da

urn a

Damage cases

lP

re-

pro of

Journal Pre-proof

Jo

• Time domain • Frequency domain • Modal domain

Data acquisition system

Data Exploration

Data Storage

Best cost of the network Data Construction

Model Induction and Evaluation

Know

Journal Pre-proof *Highlights (for review)

Highlights Damage identification of slab-on-girder bridge structures is developed using data mining.



The experimental modal analysis of intact and damaged structures is carried out.



Different damage scenarios are applied to generate modal parameters of composite slabs.



First four natural frequencies and mode shapes are used as inputs for data mining process.



Imperial competitive algorithm is used in the learning process of ANN.



A data mining-based damage identification procedure is proposed for SHM.

Jo

urn a

lP

re-

pro of



Journal Pre-proof *Manuscript Click here to view linked References

A Hybrid ANN-Based Imperial Competitive Algorithm Methodology for Structural Damage Identification of Slab-on-Girder Bridge Using Data Mining Meisam Gordana*, Hashim Abdul Razaka, Zubaidah Ismaila*, Khaled Ghaedib, Zhi Xin Tanc, and Haider Hamad Ghayebb a

StrucHMRSGroup, Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia b

c

pro of

Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia

School of Civil Engineering and Built Environment, Queensland University of Technology, QLD 4001, Australia Email Address of Corresponding Authors: [email protected], [email protected]

Abstract

Implementation of data mining (DM) techniques in different areas of civil engineering has recently given very

re-

good results. However, application of DM in structural health monitoring (SHM) is not used as much as expected, thus, many challenges are still ahead. Therefore, it seems a vital need is required to develop the applicability of DM in SHM. To this end, the current study attempts to present a DM-based damage detection

lP

methodology using modal parameter data, which trained by means of a hybrid artificial neural network-based imperial competitive algorithm (ANN-ICA). Likewise, the hybrid ANN is optimized by a new optimizationbased evolutionary algorithm, called ICA, to predict the severity and location of multiple damage cases obtained from experimental modal analysis of intact and damaged slab-on-girder bridge structures.

urn a

Furthermore, the applicability of DM approach was developed to detect the hidden patterns in vibration data using Cross Industry Standard Process for DM (CRISP-DM) tool. The performance of the model was carried out using comparison of a pre-developed ANN and ANN-ICA model. Keywords: Damage identification, data mining, imperial competitive algorithm, artificial neural network, Cross Industry Standard Process for Data Mining, evolutionary algorithms

Jo

1. Introduction

Engineering structures and infrastructures have so far experienced several damages induced by different sources of environmental effects (e.g. corrosion, micro structural defects, cracking, thermal stress, residual stress, instability, and fastening or adhesive faults) during their lifetime [1–3]. Structural damages can change the structural properties (i.e. mass, stiffness or damping) and consequently, changes of dynamic responses of structures such as natural frequency, made shape, damping ratio and frequency response function (FRF), which can lead to out-of-service conditions. Hence, real-time SHM can play an important role to ensure the integrity and safety of these structures [4,5].

Journal Pre-proof

Many non-destructive damage detection methods based on visual inspections have been conducted to identify the global or local condition of structures. However, they are costly, time consuming and cannot be used for structural continuous monitoring and deep damages [6–8]. Moreover, the implementations of other non-destructive methods (e.g. ultrasound, X ray, dye penetrants, magnetic particle, numerical investigation and acoustic emission) on damage detection are limited [9,10]. Therefore, a damage identification technique with

pro of

quick and higher global evaluation characteristics is required to detect the structural damages. Specifications of the relationship amongst structural properties and damage present the basis of the reliability assessment strategy. In this strategy the significance of the SHM stage, which takes most of the financial and technical efforts, must be taken into consideration. Hence, assessment data mining (DM) algorithms should be employed for the structural damage identification system [1]. Once enough information is gathered on the structure’s health status, the reliability of the structure can be judged by analyzing those data using different techniques such as DM patterns, design codes and safety recommendations.

re-

Main components of a SHM system can be categorized into two parts, i.e., a data collection system with a network of sensors and an extraction technique to achieve information of the structural health condition [11]. In this line, one of the new advanced approaches, DM, in data extraction has been risen up and embraced by civil engineers [12]. Applications of DM in the SHM have recently been reported [13,14], though due to the novelty

lP

of DM, application of DM in the SHM is still controversial and is not as much as expected. Therefore, it seems necessary studies are required to advance the DM application in the SHM. A systematic and applicable tool is required to implement the data mining analysis in SHM [13]. Currently, a number of DM tools exist such as DMAIC (Define-Measure-Analyze-Improve-Control), SEMMA (Sample-

urn a

Explore-Modify-Model-Assess), Cross-Industry Standard Process for DM (CRISP-DM), etc. [15–20]. Amongst these tools, CRISP-DM has the most extensive application [21]. Furthermore, many DM algorithms are also available for different functions such as prediction, exploration, clustering, association and etc. [22–24]. According to [13], ANN has the highest application rate for different functions. On the other hand, in the last decades, numerous attempts have been made to use ANN for damage detection of structures due to its high pattern recognition capability. Besides, nowadays many meta-heuristic-based biological evolution algorithms

Jo

exist, for instance, genetic algorithm [25], particle swarm optimization [26], ant colony optimization [27], artificial immune algorithm [28], firefly algorithm [29], artificial bee colony algorithm [30] and grey wolf optimization [31]. However, meta-heuristic evolutionary algorithms are not limited to biological evolution. Since another side of evolution can be employed as a meta-heuristic algorithm, humans’ social political behavior has been used for this purpose. To this end, in recent years, an evolutionary strategy has been introduced, known as Imperialist Competitive Algorithm (ICA) [32]. ICA is one of the pioneer algorithms that has been used in this domain. This optimization based meta-heuristic algorithm has indicated its high global optima. It has also shown its fast convergence speed. Hence, ICA has established its great performance in compare to other evolutionary algorithms [33,34]. Although this emerging meta-heuristic algorithm has been

Journal Pre-proof

employed in recent years for the development process of the ANN training [35,36]. Nonetheless, there is no specific application of this algorithm in SHM so far. Knowledge Discovery in Databases (KDD) process identifies novel, valid, potentially useful, and understandable forms of data (Buchheit et al., 2000). Generally, KDD defines the main procedures to transform raw data into beneficial knowledge [38]. DM is commonly used as one of the key steps in KDD and it can be

pro of

categorized into predictive mining and descriptive mining classes by means of statistical, machine learning, and artificial intelligence algorithms (e.g. ANN, ICA, support vector machine, principal component analysis, genetic algorithm, ant colony optimization, fuzzy logic, decision tree, particle swarm optimization, Bayesian analysis, etc.) and different functions (e.g. classification, clustering, visualization, prediction, association, summarization, etc.) [22,24,39,40].

According to the literature, it is felt to advance the applicability of DM in SHM. Hence, in this study a DM based approach, which is a generalized form of CRISP-DM has been proposed to investigate the applicability of

re-

DM for structural damage identification of a slab-on-girder bridge structure. A hybrid ANN-ICA model has been presented based on ANN and ICA for predicting both the location and damage severity of slab-on-girder bridge structures by means of modal parameters (i.e. natural frequencies and mode shapes). To aid the aim, a series of experimental modal analysis of the slab-on-girder bridge has been implemented to create the first four

lP

flexural modes and all corresponding mode shape values of double-point damage cases as the input dataset for DM procedure. To this end, four individual networks corresponding to mode 1 to mode 4 were modeled. Simultaneously, ICA has been used as the weight initialization algorithm to optimize the initial weights of the network during the training process. Subsequently, the proposed model is compared with ANN approach to

2. Methodology

urn a

validate the accuracy of the presented damage detection technique.

One of the most widespread systematic DM tools is CRISP-DM, which includes six steps: business understanding, data understanding, data preparation, modeling, evaluation and deployment, as presented in Figure 1(a). CRISP-DM was introduced by a consortium of several companies such as National Cash Register

Jo

(NCR) System Engineering Copenhagen from USA and Denmark, Integral Solutions Ltd. (ISL)/SPSS from USA, Daimler Chrysler AG from Germany and an insurance corporation in Netherlands, called OHRA [18,41]. Taking advantage of this tool, the present study proposes a generalized form of CRISP-DM based on the SHM system, as shown in Figure 1(b). Similarly, the proposed DM-based damage identification approach consists of six new defined stages: target identification, data exploration, database construction, pattern identification, pattern evaluation and knowledge extraction. At the first stage, specimen description and experimental setup have been presented. In the second stage, vibration data are collected from experimental modal analysis of healthy (baseline) and damaged structures. Analysis of collected data is done in the third stage to generate datasets using the first four flexural modes and all corresponding mode shape values of double-point damage

Journal Pre-proof

cases as inputs for next stage, which is pattern identification. In this stage, ANN-Based ICA is employed to train datasets and build a model for damage identification of the structure. Then, in the fifth stage, model performance is assessed using evaluation methods and ANN approach. Finally, the last stage extracts valuable knowledge, damage identification. Subsequent writings describe the proposed DM-based damage identification

pro of

approach based on the following six steps:

Target identification

re-

Knowledge extraction

lP

Pattern evaluation

Data exploration

Database construction

Pattern identification

urn a

(a) (b) Figure 1. Data mining frameworks (a) CRISP-DM [18,20], and (b) generalized form of CRISP-DM for structural damage identification

2.1. Target Identification

The main focus of this phase is to understand the project objectives and convert it into a DM problem definition. Then a primary scheme is proposed to achieve the objectives. To aid the aim, damage detection in a simply-supported slab-on-girder bridge structure was considered as the main target. The built structure was

Jo

employed as a benchmark structure for experimental modal analysis, when it consisted of three steel I-beams attached to a slab through shear studs connectors, as shown in Figure 2. The length of the slab specimen was 3200 mm, while the supports were set at 100 mm distance from the slab ends. Other dimensions of the slab include the width of 1200 mm and the depth of 100 mm. The dimensions of the beams include the flange width of 75 mm, section depth of 150 mm and thickness of 7 mm and 5 mm for the flange and web, respectively. For steel materials, Young’s Modulus of 2.1*1010 kg/m2, Poisson’s ratio of 0.3 and density of 7,850 kg/m3 were used, whereas for concrete materials the density and strength of 2400 kg/m3 and 37.43 MPa were used, respectively. A mesh reinforcement panel with 5 mm diameter and 100 mm spacing was used to reinforce the concrete slab. Sixteen shear studs connected the I-beams to the slab. The diameter of each stud is 16 mm with

Journal Pre-proof

200 mm c/c spacing and height of 75 mm. The test setup of specimens for experimental study is shown in

(c)

lP

re-

(b)

pro of

Figure 2(d).

urn a

(a)

(d)

Figure 2. Schematic view and physical preparation of the slab-on-girder bridge: (a) dimensions of the specimen, (b) construction of the specimen, (c) casted model sample, and (d) test setup

Figure 3 illustrates the proposed primary diagram of the DM process used in the present study. As it can be observed from this figure, system identification procedure is carried out to develop the mathematical

Jo

representation of the physical system using experimental data.

pro of

Journal Pre-proof

Figure 3. Block diagram of the DM process

2.2.Data Exploration

re-

This stage started with an initial data collection. Likewise, it was required to carry on with the purpose of getting familiar with the data. Focus was on structural damage identification-based DM by means of modal parameters. To aid the aim, experimental modal analysis of a slab-on-girder bridge was carried out to collect the frequency response functions (FRFs) of the healthy and damaged structure. The experimental setup and

lP

architecture of experimental modal analysis of the specimen using different instruments are shown in Figures 4 and 5.

Experimental modal analysis is one of the useful methods in identification of the modal parameters. For example, presence of any damage in structural members causes stiffness degradation, leading decrease in the

urn a

natural frequency (eigenvalue) of vibration. As a result, natural frequency measurements are used for damage detection. The reliable evidence of damage can be extracted by means of modal frequencies that is generally used as the vibration-based method [42,43]. Resonant frequencies of undamaged and damaged conditions can be measured from limited points of the structure. Their measurement can easily and accurately be carried out compared to other structural dynamic characteristics, as they are less sensitive to measurement errors and less contaminated by noise [4,44,45]. In other words, their statistical variation from random error sources are much

Jo

less than other modal parameters [46]. However, it requires very accurate frequency measurements or large levels of damage.

lP

re-

pro of

Journal Pre-proof

Jo

urn a

Figure 4. Laboratory test setup

Figure 5. Architecture of experimental modal analysis setup

Analog signals obtained from accelerometers were filtered and converted to digital signals using a multichannel signal analyzer (OROS). The experimental modal parameters, i.e., frequencies and mode shapes were extracted from FRFs. The resolution of the frequencies was controlled by sampling rate and number of Fast Fourier Transform (FFT) lines. To this end, for each experimental modal analysis, the sampling rate was set to 5.12 kS/s. This value corresponded to the frequency bandwidth of 2500 Hz with 6401 FRFs data points. Consequently, the frequency resolution of 0.39 Hz per data point was considered in this study.

Journal Pre-proof

In experimental modal analysis, a shaker was employed to excite the structure at a particular reference point and accelerometers were utilized to measure the responses of the structure. Figure 6 shows the distribution of accelerometers in the form of a grid model. As indicated in the figure, 16 points were specified at the center of each beam flange (total 48 points in three rows). The sensitivity range of the accelerometes were 95mV/g to 100 mV/g. It was concluded that, the location 19 had the best position for exciting the structure, since location 18

pro of

could not be considered due to its short distance from the support and it might be affected by the support (see Figure 7). Moreover, locations 20, 21 and 22 had node points of modes 5, 4 and 3, respectively. Therefore, these locations could not be selected as excitation point as well. In other words, location 19 did not have any nodal point for at least first five modes, hence, it was selected as the excitation point. It should be noted that, only

lP

re-

one-half of the test structure was investigated for the best shaker location due to symmetric configuration.

Jo

urn a

Figure 6. Layout of accelerometers and excitation point

Figure 7. Node points of first four vibrational modes

Journal Pre-proof

2.3.Database Construction In this stage, the final dataset was created from the initial raw data by means of several tasks including feature selection, data cleaning, data integration, data formatting, data transformation and finally data construction. The constructed datasets were used for the modeling. Modal tests were started with experimental modal analysis of the undamaged and damaged states of slab-on-

pro of

girder structure to extract the flexural modes by introducing various damage scenarios. As shown in Table 1, two different structural conditions were tested in this research.

Table 1. The detail description of structural damage cases in the test specimen

type Notch cutting

Damage case

Specifications

Damage type

Healthy state (HS)

No damage (Reference)

Damage state 1 (DS1)

Multiple-type damage

Damage state 2 (DS2)

Multiple-type damage

Damage location

Depth of damage

‒

‒

L/2 and 3L/4 of beam 2

3 up to 75 mm

L/4 of beam 1 and 3L/4 of beam 3

3 up to 75 mm

re-

Damage

Damage scenarios applied to the test structures consisted of four damage locations (i.e., L/2, 3L/4 and L/4 of the span length), as illustrated in Figure 8. In addition, Figure 9 illustrates the experimental model with a

lP

transverse damage implementing 25 levels of cut slots for each location. A cut slot of 5mm width with 3 mm incremental depth launched from 3 mm to 75 mm was considered to investigate the damage severity. As shown in Table 2, the damage locations corresponded to the ratio of damage location from the support to the length of

Jo

urn a

the beam (X/L). Table 3 also presents the damage depth to beam height ratio (a/h); damage index.

(b) DS 1

(a) HS

(c) DS 2

Figure 8. Damage scenarios of the test structure including (a) undamaged state, (b) double damage state in the middle and

Journal Pre-proof

pro of

three-quarter spans of beam 2, and (c) double damage state in one-quarter span of beam 1 and three-quarter span of beam 3.

h

x

L

Figure 9. Implementation of damage (saw cuts) in laboratory at X, h, a, and L represent beam height, damage depth and beam length, respectively.

a

re-

Table 2. Damage location index Damage location (Xi) Index (Xi/L) L/2 0.50 3L/4 0.75 L/4 0.25

urn a

lP

Table 3. Damage severity index Cut slot (mm) Index (a/h) Cut slot (mm) Index (a/h) Cut slot (mm) Index (a/h) 3 0.02 30 0.20 57 0.38 6 0.04 33 0.22 60 0.40 9 0.06 36 0.24 63 0.42 12 0.08 39 0.26 66 0.44 15 0.10 42 0.28 69 0.46 18 0.12 45 0.30 72 0.48 21 0.14 48 0.32 75 0.50 24 0.16 51 0.34 27 0.18 54 0.36 The acquisition software, NVGate, was used to implement the measurement of the raw sensor data. The modal analyzer software, ICATS, was used to compute the FRFs. The measured FRFs were then used to extract the modal parameters containing mode shapes and natural frequencies through a curve fitting method. The FRFs

Jo

of the intact and damaged structures corresponding to the first four modes are illustrated in Figure 10.

re-

pro of

Journal Pre-proof

Figure 10. FRFs of intact case and all damage cases obtained from a single accelerometer

lP

Based on the damage identified from data exploration and database construction, first four modal parameters of the healthy and damaged states were obtained, as presented in Table 4 and Figure 11. Consequently, all natural frequency values reduced after damage extension, though these two damage cases indicated a similar performance. The maximum reduction of natural frequency values was 3.54% and 4.16% for mode 3 in DS1

urn a

and DS2, respectively. In contrast, the lowest reduction of natural frequencies belonged to modes 2 and 4 in DS1 (0.53% and 0.56%) as well as mode 4 of DS2 (0.94%). This was because of node points for these particular flexural mode shapes.

Table 4. Natural frequencies of the first four modes at undamaged state Damage case

Jo

DS 1 DS 2

ω1 31.60 31.30

Natural Frequencies (Hz) of first four modes ω2 ω3 ω4 255.19 389.75 558.59 258.87 389.72 559.19

ω1-DS1 ω1-DS2

pro of

31.60 31.50 31.40 31.30 31.20 31.10 31.00 30.90 30.80 30.70 30.60 30.50 30.40

Und… 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75

Frequency (Hz)

Journal Pre-proof

Damage severity (mm)

ω2-DS2

Damage severity (mm)

urn a

ω3-DS1 ω3-DS2

Und… 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75

391 389 387 385 383 381 379 377 375 373

Jo

Frequency (Hz)

(b)

(c)

ω2-DS1

re-

lP

259 258 257 256 255 254 253 252 251 250 249

Und… 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75

Frequency (Hz)

(a)

Damage severity (mm)

560

ω4-DS1

559

ω4-DS2

558 557 556

pro of

555 554 553

Und… 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75

Frequency (Hz)

Journal Pre-proof

Damage severity (mm)

(d)

Figure 11. First four experimental natural frequencies of DS 1 and DS 2 in (a) mode 1, (b) mode 2, (c) mode 3, and (d) mode 4.

re-

2.4.Model induction

In this phase, appropriate DM algorithms were considered to apply to the database for pattern identification. This stage consisted of a number of steps such as algorithm selection, test design generation, pattern creation and initial model assessment. A hybrid ANN-ICA model was presented based on ANN and ICA for predicting

lP

the location and severity of damage via modal parameters. Then, the performance of the proposed method was compared with the results of applicable statistical and machine learning methods, i.e. decision tree and support vector machine. In this direction, the flowchart of the modeling stream using three different data mining

Jo

algorithms.

urn a

methods in the present study is depicted in Figure 12. The subsequent section presents the details of these

pro of

Journal Pre-proof

re-

Figure 12. Flowchart of the modeling procedure in present study

2.4.1. Artificial Neural Network-Based Imperial Competitive Algorithm ANN is a self-organizing DM method inspired by the human biological neurons, as shown in Figure 13. This

lP

method has proven its high capacities to work out in various purposes through pattern recognition. In general, the main components of a typical ANN consist of three layers including the input layer, the hidden layer and the output layer [23]. The neurons of the input layer represent the independent variables. The computation process of the network and the calculation of the dependent variables are carried out in the hidden and output layers,

urn a

respectively [47,48]. According to [13], this method has been the highest application rate in classification, regression and prediction due to its non-linear learning characteristics. It was concluded that, ANN is more flexible and more accurate in compare to other DM methods. Nonetheless, different neural networks have been presented (see Figure 14), however, the Multi-Layer Perceptron (MLP) has been the most frequently applied algorithm in the SHM [49]. Besides, the backpropagation neural network is also one of the most applicable algorithms that can effectively train MLP due to its high nonlinear learning ability [50,51]. This algorithm has a

Jo

performance evaluation index named the least mean square error (MSE) which can calculate the difference between the real and predicted values (et) in a number of error terms (N). MSE can be defined as follows [52]. (1)

re-

pro of

Journal Pre-proof

lP

Figure 13. Schematic structure of a biological/artificial neuron

urn a

Feed forward

ANNs

Single-layer perceptron Multi-layer perceptron Radial Basis Function nets Competitive Networks Kohenen’s SOM

Jo

Recurrent/feedback Hopefield Network ART models Figure 14. Classification of ANNs

ANNs attracted the most attentions of civil engineer programmers in various types of structures such as steel plates [53], reinforced concrete beams [54], truss structures [55,56], bridges [57,58], buildings [59–61], and dams [62]. However, according to reports by [52] and [63], they had uncertainty in assigning weights to connections between layers, which could decrease the accuracy of the network. To overcome this limitation, an

Journal Pre-proof

optimization based method is required to apply in the learning process of ANN. Consequently, a hybrid technique is proposed by combining the ANN and imperial competitive algorithm (ICA). ICA was inspired by humans’ socio-political evolution [32] and it is one of the most recent populationbased, evolutionary algorithms [64]. This computing algorithm has been effectively applied in many optimization problems. It also indicated its high potential to achieve the global optima and convergence rate

pro of

[65]. Briefly, ICA consists of several steps. In the first step, similar to other bio-inspired algorithms, this computing algorithm started with a random number of initial populations, here so called countries (i.e. imperialist and colonies). In ICA, countries are corresponding to chromosomes in genetic algorithm and an imperialist with colonized countries form an empire. In an N-dimensional optimization problem, a country (Pi) represented by

array as follows:

Country = [P1, P2, …, PNvariable]

(2)

re-

and corresponding cost function of the country is described as,

Cost = f(country) = f([P1, P2, …, PNvariable])

(3)

lP

Next step is assimilation. Based on the optimization terminology, the imperialists are countries with the least cost. The normalized cost of an imperialist (Cn) for colonization of the counties is determined as: Cn = maxi{ci}-cn

(4)

urn a

where maxi{ci} is the imperialist with maximum cost (weakest imperialist) and cn is the cost of n-th imperialist. The normalized power of each imperialist (Pn) is:

(5)

is denoted as:

Jo

Therefore, each empire can occupy some colonies. The number of colonies (N.Cn) occupied by the Nth empire

N.Cn = round {Pn . Ncolony}

(6)

where round is a function represented the round numbers and Ncolony is the total number of initial colonies. The movement of colonies towards their relevant imperialist is the next step. As shown in Figure 15, the colony moves to the imperialist by x units, which is obtained by: x ≈ U (0, β x d)

(7)

Journal Pre-proof

where d is the initial distance between the colony and β is a random number (1< β≤2). The process continues with revolution and exchanging positions of the imperialist and colonies steps. After uniting similar empires, the final step is to compute the total power of an empire, and imperialistic competition. The total power of an imperialist is taken as:

pro of

T.Cn = Cost (Imperialsitn) + ξmean{Cost(colonies of impiren)}

(8)

where T.Cn is the total cost of Nth empire, and ξ is a positive number (0<ξ<1). The above steps and ICA theory

Jo

urn a

lP

re-

are detailed in Figure 15.

Journal Pre-proof

Figure 15. Flowchart of ICA-based ANN optimization

2.4.2. Statistical and Machine Learning Techniques Applicable statistical and machine learning methods, i.e. Classification and Regression Tree (CART) and Support Vector Machine (SVM) are also considered to predict the model behavior and damage severity. Decision tree is one of the statistical techniques associated with a number of algorithms, e.g. chi-squared automatic

pro of

interaction detector (CHAID) and classification and regression trees (CART). The CART algorithm has a quicker preprocessing procedure. A CART pattern can be generated by means of a tree-based process comprising tree growing, split rules creation, and Gini reduction criteria, respectively. In tree growing, the “impurity” of two child nodes should be minimized in order to generate the split rules, as defined in Eq. 9. The reason for this comes from the fact that the child node should be more homogeneous in compare to parent node. Eventually, Gini reduction

re-

evaluates the maximum homogeneity of all splits.

(9)

where i(t) presents a measure of impurity of node t, p(j|t) indicates the node proportions (i.e. the cases in node t belonging to class j), and Φ is a nonnegative function [66].

lP

According to [67] and [68], SVM is a novel machine learning technique due to its high accuracy and good generalization capability which aims to divide data into classes. The main goal of the simplest SVM model is to

urn a

find a linear hyperplane with a maximal margin, which can be defined as: (10)

where f(x,ω) is the feature space, wj, j=1,…,m represents the weight vector, gj(x) is a set of nonlinear transformations and b is a bias term. Estimation quality is measured by a loss function L(x,ф) in which:

Jo

(11)

where ɛ represents the radius of the tube located around f(x,ω) and y is the output. The weight vector and bias of SVM can be optimized by minimizing the following function:

(12) where ξi and ξi*, i=1,…,n are non-negative slack variables and c represents a regularization constant (c ≥ 0).

Journal Pre-proof

3. Result and Discussion Finite element modeling of the slab-on-girder bridge was also conducted to generate the modal frequencies in order to validate the experimental results. In the simulation process, models were precisely designed as per the test structure. For the I-beams, a 4-node shell homogeneous (S4R) with reduced integration and hourglass control was used. The element type for the finite element model of girder deck was solid homogeneous with 8-

pro of

node linear brick (C3D8R), reduced integration and hourglass control. 432 nodes and 371 elements were used to discretize the I-beam model, whereas 7533 and 4800 nodes and elements made the girder deck, respectively. Table 5 illustrates the simulated results of the first four natural frequencies obtained from the undamaged finite element model of the test structure. For numerical analysis it was also decided to model three damage scenarios including all locations (i.e., HS, DS 1 and DS 2) considering 24 mm, 48 mm and 75 mm damage depth, individually. For the sake of clarity, the first four mode shapes for DS 1 are displayed in Figure 16. As it can be seen from this figure, the shape of entire modes changed due to the enhancement of damage severity.

re-

Besides, it was found that the variations in the third and fourth mode shapes were more important in comparison to two first modes.

lP

Table 5. Natural frequencies of undamaged state 1st mode 2nd mode 3rd mode 4th mode (Hz) (Hz) (Hz) (Hz) 33.01 256.32 391.29 554.69

urn a

(a) Numerical structure and damage simulation

28.33 Hz

Jo

(c) Mode 1 (numerical and experimental)

31.22 Hz

(e) Mode 3 (numerical and experimental)

391.22 Hz

391 Hz

(b) Laboratory structure and damage simulation

(d) Mode 2 (numerical and experimental)

256.33Hz

258.55 Hz

(f) Mode 4 (numerical and experimental)

554.94 Hz

556.44 Hz

Figure 16. Numerical and experimental variations in the shape of the first four flexural modes for 75 mm damage in DS1

Journal Pre-proof

The relationship between experimental modal analysis and numerical simulation of the tested structure was presented by means of a correlation analysis. To this end, the relative percentage error (ωrpe) of the first four experimental and numerical natural frequencies was employed to verify their correlation, as given in Eq. (13). The natural frequencies of the modal testing and numerical simulation were presented by ωexp and ωnum, respectively.

pro of

(13)

To validate the work, a comparison of the experimental and numerical findings for DS 1 and DS 2 models was made deliberating first four modes. This comparison is depicted in Figure 17(a)-(d). According to the figures, in two damage cases (i.e., DS1 and DS2), the highest relative percentage error was less than 2% for mode 2 to mode 4. However, this correlation value was approximately 5% for the first mode. The obtained results from the first four natural frequencies in the numerical analysis were well-agreed with the experiments

re-

and proved the validity of the study. 33.50

lP

32.50 32.00 31.50 31.00 30.50

urn a

Frequency (Hz)

33.00

(a)

Jo

30.00 Undam.

24

Mode 1 - DS 1 - Experimental

Mode 1 - DS 1 - Numerical Mode 1 - DS 2 - Experimental Mode 1 - DS 2 - Numerical

48

Damage severity (mm)

75

Journal Pre-proof

Mode 2 - DS 1 - Experimental Mode 2 - DS 1 - Numerical

257.00

Mode 2 - DS 2 - Experimental

255.00

Mode 2 - DS 2 - Numerical

253.00 251.00

pro of

Frequency (Hz)

259.00

249.00 247.00 245.00 Undam.

24

48

75

Damage severity (Hz)

(b)

Mode 3 - DS 1 - Experimental

392.00

Mode 3 - DS 1 - Numerical

390.00

re-

386.00 384.00 382.00 380.00 378.00 376.00 374.00

(c) 560.00

559.00 557.00 556.00 555.00 554.00

Mode 3 - DS 2 - Experimental Mode 3 - DS 2 - Numerical

48

75

Damage severity (Hz) Mode 4 - DS 1 - Experimental Mode 4 - DS 1 - Numerical Mode 4 - DS 2 - Experimental Mode 4 - DS 2 - Numerical

Jo

Frequency (Hz)

558.00

24

urn a

372.00 Undam.

lP

Frequency (Hz)

388.00

553.00 552.00 551.00

550.00 Undam.

(d)

24

Damage severity (mm)

48

75

Figure 17. Comparison of the experimental and numerical results of DS 1 and DS 2 in (a) mode 1, (b) mode 2, (c) mode 3, and (d) mode 4.

Journal Pre-proof

In the modeling phase, a hybrid ANN-ICA pattern was applied to identify the location and severity of damage. The process was started with the modeling of four networks for each damage case. For this purpose, the first four flexural natural frequencies (e.g. ωi, i=1,2,3,4) along with all corresponding mode shapes values, except the support positions (ϕi,j, j=2,3,4,…,15), were considered as the inputs of networks. In the output layer, damage severities ratio (SMi) as well as first and second damage locations ratios (e.g. L1Mi, L2Mi) were

pro of

considered to train the network. Moreover, ICA was employed to train the ANN in order to optimize the weights of all nodes and bias terms. Therefore, a dataset including fifteen neurons in the input layer and three neurons in the output layer was generated. Consequently, the parameters of the ANN-ICA for the first mode

(14)

re-

(M1) were arranged as:

In which, X1 and X2 represent the first and second location of damage, while i is the number of flexural mode.

lP

Any ANN architecture has different features for training, such as, topology of the network, types of data, number of neurons in each layer, forms of activations, the weights and parameter settings of the network. Therefore, these criteria play important roles to construct the best network. In this study, amongst ANN functions, the back-propagation (BP) algorithm in feed-forward network with different topologies was assessed

urn a

in order to obtain high quality patterns with the best forecasting capacity. However, the training process carries on updating and changing the connectivity weights up to the satisfactory level, the drawbacks of over-fitting and inefficient optimal topologies can reduce the accuracy of the network. Thus, ICA was employed in the training procedure of ANN to initialize the weights of the network. The variance of the predicted output and target output was considered as network error. In fact, reducing the network error was the main purpose in ANN training. Consequently, the MSE was considered as a cost function in ICA. Hence, the most important goal of

Jo

proposed algorithm was to minimize MSE cost function. As a result, ICA was obtained by means of the subsequent factors: the number of initial counties set to 100; imperialists set to 15 and coefficient β set to 2. For the multiple damage cases (i.e. DS1 and DS2), 52 different datasets from intact and damaged test structures were applied as inputs for DM process and ANN-ICA training. These datasets were collected to be used for locations and severities of the damage in the slab-on-girder bridge structure. In this study, 80% of the datasets were employed randomly as training datasets and the remaining datasets (20%) were used for testing and validation phase. Figure 18(a)-(d) shows a comparison between actual measured and normalized ANN-ICA predicted damage severity values at their training and validation phases in the first four flexural modes. Variations of damage

Journal Pre-proof

severities for all modes are clear from the figure. Based on the obtained results, the damage patterns from the ANN-ICA revealed an interesting fitness for all the modes. However, fitness value of each particular model was different from the others. For instance, in modes 1 and 4, the predicted results fitted to the real measured data

re-

pro of

with the same pattern. Nevertheless, modes 2 and 3 gave the lowest fitness amongst predicted and actual data.

Jo

urn a

lP

(a)

(b)

urn a

lP

re-

(c)

pro of

Journal Pre-proof

(d) Figure 18. Comparison results of damage severity values in (a) mode 1, (b) mode 2, (c) mode 3, and (d) mode 4

The comparison of the predicted two damage locations by ANN-ICA and actual value (target value) for the mode1 is depicted in Figure 19. According to the results from this figure, severer damage levels created lower

Jo

errors in compare to light damage levels. This could be explained by the fact that the uncertainties of experimental modal analysis can cause more effects on the identification of lower damage level.

0.75

0.5

0.25

0 0

5

Target

10 15 Damage scenario

20

25

20

25

Predicted by ANN-ICA (a)

re-

1 0.75 0.5 0.25 0

5

urn a

0

lP

Second location of damage

pro of

First location of damage

Journal Pre-proof

Target

10 15 Damage scenario

Predicted by ANN-ICA (b)

Figure 19. Damage location predicted by ANN-ICA and actual values (mode 1), for (a) first location, and (b) second location

3.1.Pattern Evaluation

Jo

It is important to evaluate the construction of the proposed model built by ANN-ICA. The main function of this phase was to check the validity and confirm the achieved values by means of different error measures. Herein, the prediction performance of the ANN-ICA model was interpreted by the basis of efficiency coefficient (R2) and MSE (cost function of the network). Figure 20 presents the performance of the predicted values of the ANN-ICA and the ANN using efficiency coefficient. As it can be observed, the prediction error using the ANN-ICA indicates the robustness of the proposed hybrid algorithm compared to the prediction using ANN by 6.23% improvement. As mentioned before, the principal objective of proposed ANN-ICA algorithm was to minimize the cost function of the network. Therefore, the best costs of the four ANN-ICA networks are

Journal Pre-proof

presented in Table 6. It can be seen from the table that, the cost function of the network N1 is less than others, which illustrated the better accuracy of this model for damage identification of the test structure. Table 6. Performance of the ANN-ICA networks Best cost of the network

N1 (Mode 1) N2 (Mode 2) N3 (Mode 3) N4 (Mode 4)

0.0040 0.0046 0.0067 0.0046

(b)

urn a

(a)

lP

re-

pro of

Network

Figure 20. The performance of predicted values of (a) ICA-ANN and (b) ANN vs. the measured (Mode 1)

In this study, pre-developed ANN, SVM and CART algorithms also have been employed to compare the performance of the proposed method. To this end, Multi-Layer Perceptron (MLP) and Polynomial kernel function were used to train the ANN and SVM, respectively. Likewise, a standard CART model was created by means of a single tree. Figure 21 presents the comparison between normalized predicted outputs and actual

Jo

measured data on the first four flexural modes at training and validation parts for CART, SVM, ANN, and ANN-ICA models. In this figure, the laboratory records and predicted results of ANN-ICA, SVM and CART are displayed by hollow red circles, black squares, triangles, and green circles, respectively. As it can be seen, the results of aforesaid statistical, machine learning and artificial algorithms agreed well with the real measured data. However, it can be inferred from the predicted outputs that the performance of these methods, from best to worst, is as follows: The best fitness achieved by the proposed hybrid ANN-ICA. This is due to the fact that the proposed method could achieve accurate outputs because of optimizing the learning process of the ANN using ICA. The pre-developed ANN model also indicated a better performance in compare to SVM. In the next step,

Journal Pre-proof

SVM model presented more reliable capability of prediction in compare to the statistical method. Eventually, the lowest fitness of predicted values belonged to CART amongst all the patterns. The reason for this comes from the fact that the flexibility, complexity and capacity of the statistical methods are lower than artificial intelligence methods.

CART

pro of

Train SVM

ANN

1 0.8 0.6

0.2 0

-0.2

lP

-0.4 -0.6

urn a

-0.8 -1

Actual

re-

Normalized Output

0.4

ANN-ICA

2

3

Jo

1

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21

Damage Case (a)

Journal Pre-proof

Valid CART

SVM

ANN

ANN-ICA

Actual

pro of

1

0

re-

Normalized Output

0.5

-1 1

lP

-0.5

2

3

4

5

Damage Case (b)

urn a

Figure 21. Comparison between normalized predicted outputs and actual measured data for CART, SVM, ANN, and ANN-ICA models at (a) training and (b) validation parts 3.2.Knowledge Extraction

Pattern creation cannot be considered as the last phase of the study. The achieved data obtained from the previous process should be employed in a feasible and efficient outline to be used for other projects. In this regard, the proposed structural deterioration was carried out to establish a benchmark structure for any

Jo

implementation of the real-time structural health monitoring of civil structures and development a robust damage detection system. For this aim, a workflow based on DM phases along with various algorithms (i.e. prediction-based and optimization-based) was proposed for the SHM assessment of civil structures, as shown in Figure 22. Based on the proposed flowchart, measuring damage level is the first phase of the SHM assessment to collect data. In the next phase, all data are transformed as inputs for modeling. Then, in the modeling phase, appropriate algorithms are employed to train the database. Since a number of DM algorithms (e.g. ANN, Fuzzy, principle component analysis (PCA), support vector machine (SVM), genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), Bays) exist for the same difficulty, consequently, these

Journal Pre-proof

algorithms can be applied for various functions such as classification, optimization or perdition. The obtained results are used for damage identification. After pattern assessment, deployment of the procedure can be carried

lP

re-

pro of

out introducing suitable activities to improve the health condition of structures.

Figure 22. The proposed work flow for knowledge extraction

urn a

4. Conclusion

One of the new approaches in SHM consists of two major components, i.e. a network of sensors to collect the response data and data mining (DM) to extract information on the structural health condition. Data mining (DM) is one of the new emerging computing tools which has been rapidly embraced by civil engineers. In this research, a DM-based damage identification method was performed to investigate the applicability of data mining for development of damage identification of slab-on-girder bridge structures with multiple damage

Jo

points using a hybrid ANN-ICA. A group of first four natural frequencies and all corresponding mode shape values collected from the experimental modal analysis of the structures were employed as the input database for DM procedure. At first, four ANN-ICA networks corresponding to modes 1 to 4 were trained for identification of the severity and location of damage. The ICA was used as a weight initialization algorithm to optimize the initial weights of the ANN during the training. Then, the performance of the ICA-ANN was examined using MSE and efficiency coefficient (R2) of ANN. In addition, the performance of the proposed method was compared with the results of applicable statistical methods. Based on the above investigations, the following conclusions are drawn.

Journal Pre-proof



The proposed DM-based damage identification approach was capable of application in structural health monitoring for any degree of complication to predict the severity and location of damage.



The obtained results through the proposed DM procedure using ANN-ICA algorithm indicated that, the proposed damage identification model can be considered as a precise approach for monitoring the structural condition subjected to vibrational loads. The damage severity prediction was more accurate than damage localization prediction, especially in

pro of



light damage levels. This is due to the difficulty in achieving particular change of the modal curvature obtained from experimental observations. This result indicates the fact that damage identification based on change in mode shape values for lower damage levels is complicated and might lead up to huge errors. 

It is feasible to utilize the modal parameters of slab-on-girder bridge structures as damage indices for damage detection. In addition, this study demonstrated the efficiency and feasibility of ANN-ICA



re-

based on modal parameters of structure.

The results obtained from CART, SVM and pre-developed ANN patterns indicated that the artificial intelligence algorithm could provide the best performance in compare to statistical and machine

lP

learning methods. This makes sense due to the fact that artificial intelligence techniques have the highest application rate in SHM due to their accuracy, flexibility, autonomy, complexity, and optimization capability. 

In the second stage, SVM presented a better performance than statistical CART method. It is because



urn a

the SVM has the potential to produce high quality predictions to solve the problems. In contrast, CART gave the lowest performance in the training and validation sets. This is most likely due to the fact that statistical methods such as decision tree have the lowest application rate in SHM because of the lack of capacity, flexibility and complexity. 

It is concluded that the ICA could successfully improve the learning process of the neural network. Therefore, the results of ANN-ICA confirmed the robustness of the hybrid network in compare to the

Jo

pre-developed network. Acknowledgment

The authors would like to express their sincere thanks to University of Malaya (UM) and the Ministry of Education (MOE), Malaysia for the support given through research grants IIRG007A and PG144-2016A. References [1]

M. Radzieński, M. Krawczuk, M. Palacz, Improvement of damage detection methods based on experimental modal parameters, Mech. Syst. Signal Process. 25 (2011) 2169–2190. doi:10.1016/j.ymssp.2011.01.007.

[2]

P. Khanzaei, A.A. Abdulrazeg, B. Samali, K. Ghaedi, Thermal and Structural Response of RCC Dams During Their Service

Journal Pre-proof

Life, J. Therm. Stress. 38 (2015) 591–609. doi:10.1080/01495739.2015.1015862. [3]

K. Ghaedi, M. Jameel, Z. Ibrahim, P. Khanzaei, Seismic Analysis of Roller Compacted Concrete ( RCC ) Dams Considering Effect of Sizes and Shapes of Galleries, KSCE J. Civ. Eng. 20 (2016) 261–272. doi:10.1007/s12205-015-0538-2.

[4]

Wei Fan, Pizhong Qiao, Vibration-based Damage Identification Methods: A Review and Comparative Study, Struct. Heal. Monit. 10 (2011) 83–111.

[5]

A. Bagheri, A.Z. Hosseinzadeh, P. Rizzo, G.G. Amiri, Time domain damage localization and quantification in seismically

[6]

pro of

excited structures using a limited number of sensors, J. Vib. Control. (2016). doi:10.1177/1077546315625141. H.A. Razak, F.C. Choi, The effect of corrosion on the natural frequency and modal damping of reinforced concrete beams, Eng. Struct. 23 (2001) 1126–1133. [7]

E. Aktan, S. Chase, D. Inman, D. Pines, Monitoring and Managing the Health of Infrastructure Systems And an NSFSupported Workshop on Health Monitoring of Long-Span Bridges, in: Proc. 2001 SPIE Conf. Heal. Monit. Highw. Transp. Infrastruct., Irvine, CA, 2001.

[8]

X. He, Vibration-Based Damage Identification and Health Monitoring of Civil Structures, Doctoral dissertation, University of California, 2008.

[9]

R. a. Saeed, a. N. Galybin, V. Popov, Crack identification in curvilinear beams by using ANN and ANFIS based on natural

[10]

re-

frequencies and frequency response functions, Neural Comput. Appl. 21 (2011) 1629–1645.

M.U. Hanif, Z. Ibrahim, M. Jameel, K. Ghaedi, M. Aslam, A new approach to estimate damage in concrete beams using nonlinearity, Constr. Build. Mater. 124 (2016) 1081–1089.

[11]

Z. Hou, A. Hera, M. Noori, Wavelet-Based Techniques for Structural Health Monitoring, in: Heal. Assess. Eng. Struct. Bridg.

[12]

H. Adeli, Sustainable Infrastructure Systems and Environmentally-Conscious Design — A View for the Next Decade, J. Comput. Civ. Eng. 16 (2002) 231–233.

[13]

M. Gordan, H.A. Razak, Z. Ismail, K. Ghaedi, Recent developments in damage identification of structures using data mining, Lat. Am. J. Solids Struct. (2017) 1–27.

X. Li, W. Yu, S. Villegas, Structural Health Monitoring of Building Structures With Online DataMiningMethods, Ieee Syst. J.

urn a

[14]

lP

Build. Other Infrastructures, World Scientific, 2013: pp. 179–202.

10 (2016) 1291–1300. [15]

A. Azevedo, M.F. Santos, KDD, SEMMA AND CRISP-DM:A Parallel Overview, in: IADIS Eur. Conf. Data Min., IADIS, 2008: pp. 182–185.

[16]

S.S. nand, . .

[17]

U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, Knowledge Discovery and Data Mining : Towards a Unifying Framework, KDD. 96 (1996) 82–88.

P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, R. Wirth, CRISP-DM 1.0 Step-by-step data mininng

Jo

[18]

chner, Decision Support Using Data Mining, Financial Times Management, London, 1998.

guide, 2000. [19]

L.A. Kurgan, P. Musilek, A survey of Knowledge Discovery and Data Mining process models, Knowl. Eng. Rev. 21 (2006) 1–24.

[20]

S.C. Chen, M.Y. Huang, Constructing credit auditing and control & management model with data mining technique, Expert Syst. Appl. 38 (2011) 5359–5365.

[21]

M. Saltan, S. Terzi, E. Ug, ackcalculation of pavement layer moduli and Poisson’s ratio using data mining, Expert Syst. Appl. 38 (2011) 2600–2608.

[22]

T.Y. Chen, J.H. Huang, Application of data mining in a global optimization algorithm, Adv. Eng. Softw. 66 (2013) 24–33.

[23]

K. Ghaedi, Z. Ibrahim, Earthquake Prediction, in: T. Zouaghi (Ed.), Earthquakes - Tectonics, Hazard Risk Mitig., InTech, 2017: pp. 205–227. doi:10.5772/65511.

Journal Pre-proof

[24]

S.-H. Liao, P.-H. Chu, P.-Y. Hsiao, Data mining techniques and applications – A decade review from 2000 to 2011, Expert Syst. Appl. 39 (2012) 11303–11311.

[25]

M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, 1998.

[26]

B. Xue, M.J. Zhang, W.N. Browne, Particle swarm optimization for feature selection in classification: a multi-objective approach, IEEE Trans. Cybern. 43 (2013) 1656–1671.

[27]

Y. Zhou, X. Lai, Y. Li, W. Dong, Ant colony optimization with combining Gaussian eliminations for matrix multiplication,

[28]

pro of

IEEE Trans. Cybern. 43 (2013) 347–357. A. Poteralski, M. Szczepanik, G. Dziatkiewicz, W. Kus, T. Burczynski, Comparison between PSO and AIS on the Basis of Identification of Material Constants in Piezoelectrics, Artif. Intell. Soft Comput. Pt Ii. 7895 (2013) 569–581. doi:http://dx.doi.org/10.1007/978-3-642-38610-7_52. [29]

G. Zhou, T. Yi, H. Li, Sensor Placement Optimization in Structural Health Monitoring Using Cluster-in-Cluster Firefly Algorithm, Adv. Struct. Eng. 17 (2014) 1103–1116.

[30]

P. Civicioglu, E. Besdok, A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms, 2013. doi:10.1007/s10462-011-9276-0.

[31]

A. Noshadi, J. Shi, W.S. Lee, P. Shi, A. Kalam, Optimal PID-type fuzzy logic controller for a multi-input multi-output active

[32]

re-

magnetic bearing system, Neural Comput. Appl. (2015). doi:10.1007/s00521-015-1996-7.

E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, 2007 IEEE Congr. Evol. Comput. CEC 2007. (2007) 4661–4667. doi:10.1109/CEC.2007.4425083.

[33]

E. Atashpaz Gargari, F. Hashemzadeh, R. Rajabioun, C. Lucas, Colonial competitive algorithm: A novel approach for PID

[34]

lP

controller design in MIMO distillation column process, Int. J. Intell. Comput. Cybern. 1 (2008) 337–355. H. Taghavifar, A. Mardani, L. Taghavifar, A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility, Measurement. 46 (2013) 2288–2299. [35]

S.M. Berneti, M. Shahbazian, An Imperialist Competitive Algorithm-Artificial Neural Network Method to Predict Oil Flow Rate of the Wells, Int. J. Comput. Appl. 26 (2011) 47–50.

M.A. Ahmadi, M. Ebadi, A. Shokrollahi, S. Mohammad, J. Majidi, Evolving artificial neural network and imperialist

urn a

[36]

competitive algorithm for prediction oil flow rate of the reservoir, Appl. Soft Comput. J. 13 (2013) 1085–1098. [37]

R.B. Buchheit, J.H. Garrett, Jr., S.R. Lee, R. Brahme, A Knowledge Discovery Case Study for the Intelligent Workplace, Comput. Civ. Build. Eng. (2000) 914–921.

[38]

T. Miranda, A.G. Correia, M. Santos, L. Ribeiro, P. Cortez, New Models for Strength and Deformability Parameter Calculation in Rock Masses Using Data-Mining Techniques, Int. J. Geomech. 11 (2011) 44–58.

[39]

A. Tayyebi, B.C. Pijanowski, M. Linderman, C. Gratton, Comparing three global parametric and local non-parametric models

Jo

to simulate land use change in diverse areas of the world, Environ. Model. Softw. 59 (2014) 202–221. doi:10.1016/j.envsoft.2014.05.022. [40]

V. Alves, C. Cremona, A. Cury, On the use of symbolic vibration data for robust structural health monitoring, Proc. Inst. Civ. Eng. Build. 169 (2015) 715–723.

[41]

I.B. Fernandez, S.H. Zanakis, S. Walczak, Knowledge discovery techniques for predicting country investment risk, Comput. Ind. Eng. 43 (2002) 787–800.

[42]

G. Bamnios, A. Trochides, Dynamic behaviour of a cracked cantilever beam, Appl. Acoust. 45 (1995) 97–112.

[43]

U. Dackermann, J. Li, B. Samali, Dynamic-Based Damage Identification Using Neural Network Ensembles and Damage Index Method, Adv. Struct. Eng. 13 (2010) 1001–1016.

[44]

N.T. Khiem, L.K. Toan, A novel method for crack detection in beam-like structures by measurements of natural frequencies, J. Sound Vib. 333 (2014) 4084–4103. doi:10.1016/j.jsv.2014.04.031.

Journal Pre-proof

[45]

O.K. Ihesiulor, K. Shankar, Z. Zhang, T. Ray, Delamination detection with error and noise polluted natural frequencies using computational intelligence concepts, Compos. Part B Eng. 56 (2014) 906–925. doi:10.1016/j.compositesb.2013.09.032.

[46]

S.W. Doebling, C.R. Farrar, M.B. Prime, A Summary Review of Vibration-Based Damage Identification Methods, Shock Vib. Dig. 30 (1998) 91–105.

[47]

J.C. Palomares-Salas, A. Agüera-Pérez, J.J.G. de la Rosa, A. Moreno-Muñoza, A novel neural network method for wind speed forecasting using exogenous measurements from agriculture stations, Meas. J. Int. Meas. Confed. 55 (2014) 295–304. I.. Attarzadeh, S.H.. Ow, Proposing an effective artificial neural network architecture to improve the precision of software cost

pro of

[48]

estimation model, Int. J. Softw. Eng. Knowl. Eng. 24 (2014) 935–953. [49]

Z. Wu, B. Xu, K. Yokoyama, Decentralized parametric damage detection based on neural networks, Comput. Civ. Infrastruct. Eng. 17 (2002) 175–184.

[50]

S.J.S. Hakim, H.A. Razak, Structural damage detection of steel bridge girder using artificial neural networks and finite element models, Steel Compos. Struct. 14 (2013) 367–377.

[51]

H. Hasanzadehshooiili, A. Lakirouhani, Neural network prediction of buckling load of steel arch-shells, Arch. Civ. Mech. Eng. 12 (2012) 477–484. doi:10.1016/j.acme.2012.07.005.

[52]

R.P. Bandara, Damage Identification and Condition Assessment of Building Structures Using Frequency Response Functions

[53]

re-

and Neural Networks, PhD thesis, Queensland University of Technology, 2013.

S. Goswami, P. Bhattacharya, A Scalable Neural-Network Modular-Array Architecture for Real-Time Multi-Parameter Damage Detection in Plate Structures Using Single Sensor Output, Int. J. Comput. Intell. Appl. 11 (2012) 1–22.

[54]

C.A. Jeyasehar, K. Sumangala, Nondestructive Evaluation of Prestressed Concrete Beams using an Artificial Neural Network

[55]

lP

(ANN) Approach, Struct. Heal. Monit. 5 (2006) 313–323.

B. Xu, G. Chen, Parametric Identification for a Truss Structure Using Axial Strain, Comput. Civ. Infrastruct. Eng. 22 (2007) 210–222.

[56]

H. Lam, J.L. Beck, Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks, Comput. Civ. Infrastruct. Eng. 21 (2006) 232–241.

S. Kabir, P. Rivard, G. Ballivy, Neural-network-based damage classification of bridge infrastructure using texture analysis,

urn a

[57]

Can. J. Civ. Eng. 35 (2008) 258–267. [58]

V. Mosquera, A.W. Smyth, R. Betti, Rapid evaluation and damage assessment of instrumented highway bridges, Earthq. Eng. Struct. Dyn. 41 (2012) 755–774.

[59]

S. Hung, C.S. Huang, C.M. Wen, Y.C. Hsu, Nonparametric Identification of a Building Structure from Experimental Data Using Wavelet Neural Network, Comput. Civ. Infrastruct. Eng. 18 (2003) 356–368.

[60]

V. Kanwar, N. Kwatra, P. Aggarwal, Damage Detection for Framed RCC Buildings using ANN Modeling, Int. J. Damage

[61]

Jo

Mech. 16 (2007) 457–472.

X. Jiang, S. Mahadevan, Bayesian Probabilistic Inference for Nonparametric Damage Detection of Structures, J. Eng. Mech. 134 (2008) 820–832.

[62]

J. Li, U. Dackermann, Y. Xu, B. Samali, Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles, Struct. Control Heal. Monit. 18 (2011) 207–226.

[63]

L.S. Lee, V.K. M., S. Charles, Investigation of Integrity and Effectiveness of RC Bridge Deck Rehabilitation with CFRP Composites, Structural Systems Research Project, University of California, San Diego, 2004. https://pdfs.semanticscholar.org/55f3/1433f849fc8273f5c5b8fb12b2a70fd772e2.pdf.

[64]

E. Ebrahimi, K. Mollazade, S. abaei, Toward an automatic wheat purity measuring device :

machine vision-based neural

networks-assisted imperialist competitive algorithm approach, Measurement. 55 (2014) 196–205. [65]

M. Hajihassani, D.J. Armaghani, A. Marto, E.T. Mohamad, Ground vibration prediction in quarry blasting through an

Journal Pre-proof

artificial neural network optimized by imperialist competitive algorithm, Bull. Eng. Geol. Environ. (2015) 873–886. doi:10.1007/s10064-014-0657-x. [66]

L. Chang, W. Chen, Data mining of tree-based models to analyze freeway accident frequency, J. Safety Res. 36 (2005) 365– 375.

[67]

B. DeVille, P. Neville, Decision Trees for Analytics Using SAS® Enterprise Miner TM, 2013.

[68]

G. Linoff, M.J.A. Berry, Data mining techniques: for marketing, sales, and customer relationship management, Wiley Pub,

Jo

urn a

lP

re-

pro of

Indianapolis, Ind., 2011.

Journal Pre-proof *Declaration of Interest Statement

Declaration of interests ☒ 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.

Jo

urn a

lP

re-

Declarations of interest: none

pro of

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

*Author Contributions Section

Author contributions

Journal Pre-proof

Meisam Gordan: Methodology, Formal analysis, Writing - Original Draft Hashim Abdul Razak: Project administration, Conceptualization, Resources, Funding acquisition Zubaidah Ismail: Supervision, Funding acquisition, Resources

pro of

Khaled Ghaedi: Validation, Writing - Review & Editing Zhi Xin Tan: Data Curation

Jo

urn a

lP

re-

Haider Hamad Ghayeb: Investigation