Recent Developments in Damage Identification of Structures Using Data Mining

Meisam Gordan Hashim Abdul Razak Zubaidah Ismail Khaled Ghaedi About the authors

Abstract

Civil structures are usually prone to damage during their service life and it leads them to loss their serviceability and safety. Thus, damage assessment can guarantee the integrity of structures. As a result, a structural damage detection approach including two main components, a set of accelerometers to record the response data and a data mining (DM) procedure, is widely used to extract the information on the structural health condition. In the last decades, DM has provided numerous solutions to structural health monitoring (SHM) problems as an all-inclusive technique due to its powerful computational ability. This paper presents the first attempt to illustrate the data mining techniques (DMTs) applications in SHM through an intensive review of those articles dealing with the use of DMTs aimed for classification-, prediction- and optimization-based data mining methods. According to this categorization, applications of DMTs with respect to SHM research area are classified and it is concluded that, applications of DMTs in the SHM domain have increasingly been implemented, in the last decade and the most popular techniques in the area were artificial neural network (ANN), principal component analysis (PCA) and genetic algorithm (GA), respectively.

Keywords:
Structural damage detection; data mining technique; artificial neural network; genetic algorithm; principal component analysis

1 INTRODUCTION

Structural systems in civil engineering such as tall buildings, long hydraulic structures, and long span bridges are damage-prone under different loadings such as fatigue, aging, overloading, earthquakes and other natural disasters during their service life (Duan and Zhang 2006Duan Z, Zhang K (2006) Data Mining Technology for Structural Health Monitoring. Pacific Sci Rev 8:27-36.; Hakim and Razak 2014Hakim SJS, Razak HA (2014b) Modal parameters based structural damage detection using artificial neural networks - a review. Smart Struct Syst 14:159-189.a; Khanzaei et al. 2015Khanzaei P, Abdulrazeg AA, Samali B, Ghaedi K (2015) Thermal and Structural Response of RCC Dams During Their Service Life. J Therm Stress 38:591-609. doi: 10.1080/01495739.2015.1015862
https://doi.org/10.1080/01495739.2015.10...
; Ghaedi et al. 2016Ghaedi K, Jameel M, Ibrahim Z, Khanzaei P (2016) Seismic Analysis of Roller Compacted Concrete ( RCC ) Dams Considering Effect of Sizes and Shapes of Galleries. KSCE J Civ Eng 20:261. doi: 10.1007/s12205-015-0538-2
https://doi.org/10.1007/s12205-015-0538-...
, 2017aGhaedi K, Hejazi F, Ibrahim Z, Khanzaei P (2017a) Flexible foundation effect on seismic analysis of Roller Compacted Concrete (RCC) dams using finite element method. KSCE J Civ Eng 1-13., bGhaedi K, Ibrahim Z, Adeli H (2017b) Invited Review: Recent developments in vibration control of building and bridge structures.). Existence of damage can disturb functionality and safety of the structure. Therefore, damage detection is one of the most important factors in order to guarantee the integrity and safety of civil structures (Kanwar et al. 2007Kanwar V, Kwatra N, Aggarwal P (2007) Damage Detection for Framed RCC Buildings using ANN Modeling. Int J Damage Mech 16:457-472.; Xu et al. 2012Xu B, Song G, Masri SF (2012) Damage detection for a frame structure model using vibration displacement measurement. Struct Heal Monit 11:281-292.; Hakim and Razak 2014bHakim SJS, Razak HA (2014a) Frequency Response Function-based Structural Damage Identification using Artificial Neural Networks-a Review. Res J Appl Sci Eng Technol 7:1750-1764.; Hanif et al. 2016Hanif MU, Ibrahim Z, Jameel M, et al (2016) A new approach to estimate damage in concrete beams using non-linearity. Constr Build Mater 124:1081-1089.).

Many damage identification techniques have been applied to civil structures. For instance, visual inspections are the most common damage detection techniques. However, they have been used in detecting damage of many structures; they are time consuming and costly. These techniques cannot also be used for continuous monitoring of structures (Razak and Choi 2001Razak HA, Choi FC (2001) The effect of corrosion on the natural frequency and modal damping of reinforced concrete beams. Eng Struct 23:1126-1133.; He 2008He X (2008) Vibration-Based Damage Identification and Health Monitoring of Civil Structures. Doctoral dissertation, University of California).

With the development of data inverse analysis methods, it is required to combine problem-based internal mechanism analysis and DM technology to facilitate the application of the measured data analyses in complex engineering system modeling (Zhang et al. 2013Zhang Q, Kang Y, Zheng Z, Wang L (2013) Inverse Analysis and Modeling for Tunneling Thrust on Shield Machine.). Based on this strategy, DMTs have, recently, been employed in SHM area due to their powerful computational ability to detect damage in structural systems, where the main components of any structural damage detection approach consist of a set of accelerometers and a DM procedure. In the monitoring process, the network of accelerometers is utilized to create a database using response data collection. The DM approach is used to extract information on the structural health condition from the database and to obtain the relationship between data in the form of patterns (Edeki 2012Edeki CA (2012) A Comparative Study of Data Mining and Statistical Learning Techniques for Prediction of Canser Survovability. Capella University; Hou et al. 2013Hou Z, Hera A, Noori M (2013) Wavelet-Based Techniques for Structural Health Monitoring. In: Health Assessment of Engineered Structures: Bridges, Buildings and Other Infrastructures. World Scientific, pp 179-202).

In this paper, various DMTs, which are applicable to SHM, have thoroughly reviewed. A brief background and different tasks of DMTs are described in Section 2. Classification, prediction and optimization-based data mining methods and their applications in SHM are presented in Sections 3 and 4. Trend and comparison of DM applications in SHM as well as the most used DMTs with highest application rate are discussed in Section 5. Here, capabilities and limitations of the most applicable data mining-based methods in SHM are presented. Finally, due to the lack of specific straightforward data mining-based SHM as well as requiring more attention to improve the health monitoring of structures, a flow chart based on the DM steps along with prediction and optimization-based algorithms is proposed for SHM assessment. In Section 6, the important conclusions are drawn. To the best of our knowledge, current research is the first attempt to illustrate the DMTs applications in SHM.

2 DATA MINING (DM)

DM is one of the key steps in Knowledge Discovery in Databases (KDD) process. This process is used to identify valid, valuable and understandable forms of data (Buchheit et al. 2000Buchheit RB, Garrett, Jr. JH, Lee SR, Brahme R (2000) A Knowledge Discovery Case Study for the Intelligent Workplace. Comput Civ Build Eng 914-921.; Miranda et al. 2011Miranda T, Correia AG, Santos M, et al (2011) New Models for Strength and Deformability Parameter Calculation in Rock Masses Using Data-Mining Techniques. Int J Geomech 11:44-58.). In general, DM can be categorized into two groups: descriptive mining and predictive mining. Each of these groups has its specific tasks (Pang-Ning et al. 2006Pang-Ning T, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson Addison-Wesley, Boston). For instance, some DM tasks consist of clustering, prediction, classification, exploration and association. The purpose of clustering is to divide the samples into groups with related behavior. The numerical prediction activity determines patterns, rules or models to predict continuous or discrete target values which can also be used for other functions. Classification is used to recognize several rules which can be applied in future work to determine whether a previously unknown item belongs to a known class. Exploration is used to find out dimensionality of an input data and, eventually, the association activity is used to frequently detect occurring related objects. Based on their particular utilizations in consequence of their assumptions and drawbacks, one or combination of some of these tasks can be used to find the hidden information (Obenshain 2004Obenshain MK (2004) Application of data mining techniques to healthcare data. Infect Control Hosp Epidemiol 25:690-695.; Liao et al. 2012Liao S-H, Chu P-H, Hsiao P-Y (2012) Data mining techniques and applications - A decade review from 2000 to 2011. Expert Syst Appl 39:11303-11311.; Chen and Huang 2013Chen B, Nagarajaiah S (2013) Observer-based structural damage detection using genetic algorithm. Struct Control Heal Monit 20:520-531.).

3 CLASSIFICATION AND PREDICTION-BASED DATA MINING IN STRUCTURAL DAMAGE DETECTION

Classification and prediction-based DM algorithms can be divided into supervised and unsupervised learning broads. In supervised learning techniques (e.g. ANN, regression, support vector machine, decision tree, Bayesian analysis, etc.), targets are known, nevertheless, in unsupervised learning (e.g. clustering and principal component analysis) target known values do not exist (Liao et al. 2012Liao S-H, Chu P-H, Hsiao P-Y (2012) Data mining techniques and applications - A decade review from 2000 to 2011. Expert Syst Appl 39:11303-11311.). Subsequent categorization attempts to summarize the classification and prediction-based DMTs and their applications in SHM. To aid the aim, the applications of DMTs in SHM area are separately presented in a tabular form.

3.1 Artificial Neural Network (ANN)

ANN approach was first proposed in 1980s. It is a self-organizing computational technique and it can solve many functions through pattern recognition (Shahriar and Nehdi 2011Shahriar A, Nehdi ML (2011) Modeling Rheological Properties of Oil Well Cement Slurries Using Artificial Neural Networks. J Mater Civ Eng 23:1703-1710.; Ahmed et al. 2015Ahmed R, El Sayed M, Gadsden SA, et al (2015) Artificial neural network training utilizing the smooth variable structure filter estimation strategy.). ANN can effectively be used to reconstruct nonlinear relationship learning from training (Ali et al. 2013Ali A, Amin SE, Ramadan HH, Tolba MF (2013) Enhancement of OMI aerosol optical depth data assimilation using artificial neural network. Neural Comput Appl 23:2267-2279.).

A basic biological neuron, as shown in Figure 1(a), consists of a cell body, axons, dendrites and synapses. Signals (input connections) are carried by the dendrites into the cell body. Axons transfer the signals (output connections) from one neuron to others, whereas synapses are the point contacts between dendrites of one cell and axon of another cell (Karacı and Arıcı 2014Karacı A, Arıcı N (2014) Determining students’ level of page viewing in intelligent tutorial systems with artificial neural network. Neural Comput Applic 24:675-684.). The main elements of an artificial neuron are indicated in Figure 1(b) which includes weights, bias and activation function (Azimzadegan et al. 2012Azimzadegan T, Khoeini M, Etaat M, Khoshakhlagh A (2012) An artificial neural-network model for impact properties in X70 pipeline steels. Neural Comput Appl 23:1473-1480.). A typical artificial neural network model has two parts; processing units (neurons) and connections between elements (Ali et al. 2013Ali A, Amin SE, Ramadan HH, Tolba MF (2013) Enhancement of OMI aerosol optical depth data assimilation using artificial neural network. Neural Comput Appl 23:2267-2279.), in which neurons are located in layers of network. A layered ANN structure, called multilayer perceptron (MLP), is one of the most widespread ANN methods especially in structural identification (He and Yan 2007He H-X, Yan W (2007) Structural damage detection with wavelet support vector machine: introduction and applications. Struct Control Heal Monit 14:162-176.) (see Figure 1(c)). In general, a conventional ANN has three layers which are input layer, hidden layer and output layer (Mert et al. 2015Mert İ, Karakuş C, Üneş F (2015) Estimating the energy production of the wind turbine using artificial neural network.). ANNs can be categorized by their network topology such as feed forward and feedback or by their learning algorithms such as supervised learning and unsupervised learning (Azimzadegan et al. 2012). Outputs (Yi) of ANN can be defined by following equation:

Y i = f i ( j = 1 n w ij x i + b i ) (1)

where xi indicates the input values, wij illustrates the connection weight values between input, hidden and output layers, bi is the bias, and fi shows the transfer function (Shah and Ghazali 2011Shah H, Ghazali R (2011) Prediction of earthquake magnitude by an improved ABC-MLP. In: Proceedings - 4th International Conference on Developments in eSystems Engineering, DeSE 2011. IEEE, pp 312-317; Chu et al. 2014Chu F, Wang F, Wang X, Zhang S (2014) A hybrid artificial neural network-mechanistic model for centrifugal compressor. Neural Comput Appl 24:1259-1268.).

Figure 1:
Overview of ANN.

Strain-based emulator neural network (SENN), parametric evaluation neural network (PENN), wavelet neural network (WNN), Bayesian neural network, fuzzy wavelet neural networks (FWNN), neural network-based damage classification, auto-associative neural network (AANN) and displacement-based neural network emulator (DNNE) are some of the important applications of neural networks in SHM. The detailed neural network technique applications are presented in Table 1.

Table 1:
Artificial Neural Networks applications in SHM.

3.2 Fuzzy Logic Technique

Fuzzy logic was proposed by Lotfi Zadeh for the first time in 1965. It has been employed in different applications such as pattern recognition, classification, decision making, etc. (Rutkowski 2004Rutkowski L (2004) Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation. Springer Science & Business Media). As Figure 2 indicates, the basic configuration of a fuzzy technique consists of four important components, which are fuzzification, fuzzy rule-base, fuzzy inference and defuzzification. Fuzzification is a mapping from a crisp input to fuzzy membership sets. The fuzzy rule-base has set rules of fuzzy variables described by membership functions. Fuzzy inference is a decision making mechanism of the fuzzy system. The defuzzifier changes the fuzzy consequences from different rules into crisp values (Nyongesa 1998Nyongesa HO (1998) Enhancing Neural Control Systems by Fuzzy Logic and Evolutionary Reinforcement. Neural Comput Appl 121-130.). Fuzzy is a model free technique for structural system identification, where the most important advantages of fuzzy systems are their high parallel implementation, nonlinearity and being capable of adapting (Nerves and Krishnan 1995Nerves AC, Krishnan R (1995) Active control strategies for tall civil structures. Proc IECON ’95 - 21st Annu Conf IEEE Ind Electron 2:962-967.). Applications of Fuzzy logic in SHM are detailed in Table 2.

Figure 2:
Schematic representation of a fuzzy system (Rafiee et al. 2015Rafiee R, Ataei M, KhalooKakaie R (2015) A new cavability index in block caving mines using fuzzy rock engineering system. Int J Rock Mech Min Sci 77:68-76.).

Table 2:
Fuzzy Logic applications in SHM.

3.3 Classification

Classification, which is based on the machine learning, is used to classify databases into several groups. This technique is employed to indicate not only a form of classification rules but also mathematical formulae from a function of individual classes of data in a database. Therefore, these rules are capable of organizing upcoming data. Consequently, classification method can present a better view of the database contents (Han et al. 2006Han J, Kamber M, Pei J (2006) Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco).Hence, refer to Table 3, key point of this technique is to predict a categorical feature based on other features of known data.

Table 3:
Classification applications in SHM.

3.4 Support Vector Machine (SVM)

Support vector machine (SVM) was first introduced by Vapnik and his assistants in 1963 (Vapnik 1995Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York; Statnikov et al. 2011Statnikov A, Aliferis CF, Hardin DP (2011) Gentle Introduction to Support Vector Machines in Biomedicine: Theory and Methods. World Scientific). SVM works based on statistical learning theory and because of its high accuracy and good generalization capability, it has the potential to produce high quality predictions in numerous tasks. Therefore, SVM has various applications which can be found in several areas such as machine learning, data classification and pattern recognition (Cristianini and Shawe-Taylor 2000Cristianini N, Shawe-Taylor J (2000b) An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, UKb; Samanta et al. 2003Samanta B, Al-Balushi KR, Al-Araimi SA (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intell 16:657-665.; He and Yan 2007He H-X, Yan W (2007) Structural damage detection with wavelet support vector machine: introduction and applications. Struct Control Heal Monit 14:162-176.; Tinoco et al. 2014Tinoco J, Gomes Correia A, Cortez P (2014) Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Comput Geotech 55:132-140.). Basic models of SVM are linear SVM with linear functions and nonlinear SVM with kernel functions (Mita and Hagiwara 2003Mita BA, Hagiwara H (2003) Quantitative Damage Diagnosis of Shear Structures Using Support Vector Machine. KSCE J Civ Eng 7:683-689.). Moreover, aim of SVM classifier is to determine a separating hyperplane to divide the given data into two classes (i.e. positive class and negative class) in the optimal form. Therefore, as shown in Figure 3, the optimal separating hyperplane is determined by solving an optimization problem, defined as:

Minimize to : 1 2 w 2 Subject to : y i ( w T . X i + b ) 1, i = 1,2, , N y i { 1, 1 } , x i R n (2)

where w, yi, and xi are a m-dimensional vector, class label and given data, respectively. N is the number of samples, and b is a scalar (Mita and Hagiwara 2003Mita BA, Hagiwara H (2003) Quantitative Damage Diagnosis of Shear Structures Using Support Vector Machine. KSCE J Civ Eng 7:683-689.; Kishore et al. 2014Kishore B, Satyanarayana MRS, Sujatha K (2014) Efficient fault detection using support vector machine based hybrid expert system.).

Figure 3:
Graphic diagram of SVM.

Wavelet support vector machine (WSVM), nonlinear support vector machines, support vector regression (SVR), multiclass support vector machine and multiclass nonlinear relevance vector machine (MNRVM) are some of the important applications of SVMs in SHM. Table 4 illustrates the different use of SVM applications in detail for SHM area.

Table 4:
Support Vector Machine (SVM) applications in SHM.

3.5 Regression Analysis

Regression analysis is one of the statistical methods that is used for prediction of different functions through different algorithms such as linear regression, nonlinear regression, logistic regression, and stepwise regression. The method is used to find the mathematical relationship between one or more independent and dependent variables (Jeon et al. 2014Jeon J, Shafieezadeh A, DesRoches R (2014) Statistical models for shear strength of RC beam-column joints using machine-learning techniques. Earthq Eng Struct Dyn 43:2075-2095.), as shown in Figure 4. This relationship can be defined as:

y i = f ( x i ) + e (3)

where xi indicates the input and yi illustrates the output; f(x) represents the linear regression function and e shows the independent random error (Liu and Li 2009Liu Q, Li C (2009) Researches on Support Vector Machine Based Semi-Active Control of Structures. 2009 Int Conf Comput Technol Dev 2:411-415.).

Figure 4:
Schematic illustration of regression analysis.

Support vector regression (SVR), multivariate linear regression (MLR), nonlinear regression and robust regression analysis (RRA) are some of the important applications of regression analysis in SHM. Table 5 shows the applications of regression analysis in SHM, thoroughly.

Table 5:
Regression Analysis applications in SHM.

3.6 Principal Component Analysis (PCA)

PCA is a well-known method for data analysis, which is used as a dimensional reduction tool (Lautour and Omenzetter 2010Lautour OR de, Omenzetter P (2010) Nearest Neighbor and Learning Vector Quantization Classification for Damage Detection using Time Series Analysis. Struct Control Heal Monit 17:614-631.; Kwak 2014Kwak N (2014) Principal Component Analysis by L p-Norm Maximization. IEEE Trans Cybern 44:594-609.). PCA also is employed in exploratory data analysis and pattern recognition (Yiqiu et al. 2012Yiqiu T, Lei Z, Lun J (2012) Analysis of the Evaluation Indices from TSRST. J Mater Civ Eng 90:1310-1316.). The main concept behind PCA is to transform high-dimensional correlated variables into low-dimensional uncorrelated variables by an orthogonal projection, in which the new low-dimensional uncorrelated variables are known as principal components, as shown in Table 6. Main steps to have the principal components of a database is to (1) construct a matrix from data, (2) construct the normalized matrix, (3) calculate the covariance matrix, and (4) calculate the largest eigenvalue and eigenvector. Architecture of a two-dimensional PCA is shown in Figure 5, where it can principally be defined as:

Y = W T X (4)

in which Y shows principal components, W indicates eigenvector and X represents the input data in the new coordinates (Ku and Waszczyszyn 2006Ku K, Waszczyszyn Z (2006) Neural Networks and Principal Component Analysis for Identification of Building Natural Periods. J Comput Civ Eng 20:431-436.; Hua et al. 2007Hua XG, Ni YQ, Ko JM, Wong KY (2007) Modeling of Temperature - Frequency Correlation Using Combined Principal Component Analysis and Support Vector Regression Technique. J Comput Civ Eng 21:122-135.; Hao et al. 2011Hao L, Lewin PL, Hunter J a, et al (2011) Discrimination of multiple PD sources using wavelet decomposition and principal component analysis. Dielectr Electr Insul IEEE Trans 18:1702-1711.).

Table 6:
Principal Component Analysis (PCA) applications in SHM.

Figure 5:
PCA transformation.

3.7 Bayesian Analysis

Bayesian analysis is considered as a statistical method, which is applied to pattern recognition (Cristianini and Shawe-Taylor 2000Cristianini N, Shawe-Taylor J (2000a) An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, UKa) and it is a classification method based on Bayes’s theorem. Bayesian method has been used for addressing data uncertainty and modeling errors in structural damage detection (Jiang and Mahadevan 2008Jiang X, Mahadevan S, Adeli H (2007) Bayesian wavelet packet denoising for structural system identification. Struct Control Heal Monit 14:333-356.b). In recent years, a new type of Bayesian analysis has been derived from neural networks (Arangio and Beck 2012Arangio S, Beck JL (2012) Bayesian neural networks for bridge integrity assessment. Struct Control Heal Monit 19:3-21.). For instance, a four-phase structural damage identification approach (see Figure 6) can be used by means of Bayesian inference and fuzzy wavelet neural network (WNN) model (Jiang and Mahadevan 2008a). Bayes’s theorem can be defined as follows:

P ( A|B ) = P ( B|A ) . P ( A ) P ( B ) (5)

where P(A) is prior probability of A with no knowledge of observation B; P(B) is prior probability of B with no knowledge of A; P(A|B) is posterior probability of A after observation B; and P(B|A) is probability of observation B given that A is true (Dawsey et al. 2006Dawsey WJ, Minsker BS, Van Blaricum VL (2006) Bayesian Belief Networks to Integrate Monitoring Evidence of Water Distribution System Contamination. J Water Resour Plan Manag 132:234-241.; Yuen 2010Yuen K-V (2010) Basic Concepts and Bayesian Probabilistic Framework. In: Bayesian Methods for Structural Dynamics and Civil Engineering. John Wiley & Sons (Asia) Pte Ltd, pp 11-98). Applications of Bayesian analysis are shown in Table 7.

Figure 6:
Damage detection methodology using combination of Bayesian and fuzzy WNN(Jiang and Mahadevan 2008Jiang X, Mahadevan S (2008b) Bayesian wavelet methodology for structural damage detection. Struct Control Heal Monit 15:974-991.a).

Table 7:
Bayesian Analysis applications in SHM.

3.8 Clustering

Clustering is an unsupervised statistical data analysis technique, which is used in pattern recognition, image analysis and bioinformatics (Park et al. 2007Park S, Lee J-J, Yun C-B, Inman DJ (2007) Electro-Mechanical Impedance-Based Wireless Structural Health Monitoring Using PCA-Data Compression and k-means Clustering Algorithms. J Intell Mater Syst Struct 19:509-520.). This method is employed to divide datasets into separated similar subsets (clusters) according to typical patterns identified in the clustering analysis (Ghaedi and Ibrahim 2017Ghaedi K, Ibrahim Z (2017) Earthquake Prediction. In: Earthquakes - Tectonics, Hazard and Risk Mitigation. InTech,). Figure 7 indicates the basic concept of clustering. As shown in Figure 7(a), in order to have a successful clustering, maximum intra-cluster similarity as well as minimum inter-cluster similarity is required. Moreover, Figure 7(b) presents a schematic plot of K-means algorithm, which divides the space into three clusters (C1, C2, and C3). The K-means is one of the most descriptive partitioning clustering algorithms with a quite reliable effectiveness at local optimum. However, it can be employed only to numerical datasets. Furthermore, K-means has poor handling for data prone to noise and outliers (Symeonidis and Mitkas 2005Symeonidis A, Mitkas P (2005) DATA MINING AND KNOWLEDGE DISCOVERY : A BRIEF OVERVIEW. In: Agent Intelligence Through Data Mining. pp 11-40). Clustering can also help to decrease the distance between datasets and improve the similarity of datasets in each cluster (Saitta et al. 2008Saitta S, Kripakaran P, Raphael B, Smith IFC (2008) Improving System Identification Using Clustering. J Comput Civ Eng 22:292-302.; Yu et al. 2011Yu Z, Fung BCM, Haghighat F, et al (2011) A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy Build 43:1409-1417.; Chuang et al. 2011Chuang L-Y, Hsiao C-J, Yang C-H (2011) Chaotic particle swarm optimization for data clustering. Expert Syst Appl 38:14555-14563.; Chen and Huang 2013Chen TY, Huang JH (2013) Application of data mining in a global optimization algorithm. Adv Eng Softw 66:24-33.; Xiao and Fan 2014Xiao F, Fan C (2014) Data mining in building automation system for improving building operational performance. Energy Build 75:109-118.). Table 8 illustrates the applications of clustering analysis in SHM system.

Figure 7:
Algorithms of (a) Intra- and inter-cluster similarity, and (b) Schematic plot of K-means clustering (Symeonidis and Mitkas 2005Symeonidis A, Mitkas P (2005) DATA MINING AND KNOWLEDGE DISCOVERY : A BRIEF OVERVIEW. In: Agent Intelligence Through Data Mining. pp 11-40).

Table 8:
Clustering analysis applications in SHM.

3.9 Decision Tree

One of the statistical methods in order to solve classification problems is decision tree that can widely be implemented (Gal et al. 2013Gal G, Skerjanec M, Atanasova N (2013) Fluctuations in water level and the dynamics of zooplankton: a data-driven modelling approach. Freshw Biol 58:800-816.). Components of a decision tree, as shown in Figure 8, consist of nodes and arcs. The chance nodes indicate features, while decision nodes represent pattern classes and arcs denote feature values. The main advantages of using this approach is easy solution to understand and its higher interpretable capacity compared to other statistical techniques (Wei and Hsu 2008Wei C-C, Hsu N-S (2008) Derived operating rules for a reservoir operation system: Comparison of decision trees, neural decision trees and fuzzy decision trees.).

Figure 8:
Configuration of a decision tree.

Starting point to design a decision tree model is a decision node creation including several alternatives. For example, Kim et al. (Kim et al. 2011Kim S, Frangopol DM, Zhu B (2011) Probabilistic Optimum Inspection / Repair Planning to Extend Lifetime of Deteriorating Structures. J Perform Constr Facil 25:534-545.) considered two alternatives (i.e. first inspection time (tinsp,1) after and before initial service life (t(0)life)) and four branches to create a decision tree model for prediction of lifetime of deteriorating structures with one inspection, as shown in Figure 9(a). In Figure 9(b), each branch represents different cases of decision tree including late inspection in first branch, changing node without damage detection in second branch and damage detection in third or fourth branches. The repair process can be conducted by decision maker selection in the third or fourth branch. Applications of decision tree method in SHM are presented in Table 9.

Figure 9:
Application of decision tree for (a) prediction of lifetime with one inspection, (b) extended lifetime of each branch in (a).

Table 9:
Decision Tree applications in SHM.

4 OPTIMIZATION-BASED DATA MINING IN STRUCTURAL DAMAGE DETECTION

4.1 Genetic Algorithms (GA)

Genetic algorithm (GA) was first proposed by John Holland in 1970’s. In GA, a chromosome is used to determine the solution. The chromosome includes a group of genes that optimize parameters. In damage identification problems, damage parameters are determined in real number of genes, which form the chromosome of an individual solution. This algorithm employs a random solution from a current population. Then, next generation will be created using crossover and mutation operators (Kouchmeshky et al. 2008Kouchmeshky B, Aquino W, Billek AE (2008) Structural damage identification using co-evolution and frequency response functions. Struct Control Heal Monit 15:162-182.; Aghajanloo and Sabziparvar 2012Aghajanloo M, Sabziparvar A (2012) Artificial neural network - genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran. Neural Comput Appl 23:1387-1393.). Figure 10 illustrates a typical crossover and mutation operator in GA algorithm. Accordingly, the basic steps to form a GA include:

  • Select a population of chromosomes;

  • Calculate the fitness of each chromosome;

  • select parents chromosomes from population, randomly;

  • Crossover creates offspring;

  • Mutation forms a new generation;

  • Calculate the fitness of new chromosomes;

  • Repeat the selection of parents chromosomes to find the fittest chromosomes in the next generation (Fu 2000Fu Z (2000) Using Genetic Algorithms to Develop Inteligent Decision Trees. University of Maryland).

Figure 10:
Configuration of Genetic algorithm.

Genetic algorithm is an attractive tool to optimize difficult problems because of its benefits such as parallelism, convergence to global optima, adaptation, and no need for the gradient of the objective function. Considering these benefits, GA has been successfully applied in structural damage identification problems (Sohn et al. 2001Sohn H, Farrar CR, Hemez F, Czarnecki J (2001) A Review of Structural Health Monitoring Literature 1996 - 2001. LA-UR-02-2095 1-7.; Raich and Liszkai 2003Raich A, Liszkai T (2003) Benefits of Implicit Redundant Genetic Algorithms for Structural Damage Detection in Noisy Environments. In: Genetic and Evolutionary Computation-GECCO 2003. Springer-Verlag Berlin Heidelberg, pp 2418-2419). Applications of GA in SHM are shown in Table 10.

Table 10:
Genetic Algorithm (GA) applications in SHM.

4.2 Particle Swarm Optimization (PSO)

PSO which was first proposed by Kennedy and Eberhart (Kennedy and Eberhart 1995Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc ICNN’95 - Int Conf Neural Networks 4:1942-1948.), is one of the population-based artificial intelligence optimization techniques. The approach was simulated by the social behavior of organisms such as bird flocking to be used a suitable tool for global optimization. In PSO, a particle represents a potential solution where each particle has two updatable features; position and velocity. The main steps of this algorithm can be presented as follows:

  • Particles initialization with arbitrary position and velocity;

  • Objective function evaluation and updating the position and velocity based on the best fitness function;

  • Determination of global minimum fitness value;

  • Velocity modification and particle movement to new position;

  • Process iteration to meet criterion (Talatahari et al. 2013Talatahari S, Rahbari NM, Kaveh A (2013) A new hybrid optimization algorithm for recognition of hysteretic non-linear systems. KSCE J Civ Eng 17:1099-1108.).

Furthermore, PSO is easy to apply and has great computational capacity. In comparison with other optimization approaches, PSO is, however, more efficient and requiring fewer number of function evaluations while gives better or the same quality of results, but it has some weaknesses such as trapping into local optimum in a complex search space and disability to do a good local search around a local optimum (Dimou and Koumousis 2009Dimou CK, Koumousis VK (2009) Reliability-Based Optimal Design of Truss Structures Using Particle Swarm Optimization. J Comput Civ Eng 23:100-110.; Gholizadeh and Fattahi 2014Gholizadeh S, Fattahi F (2014) Design optimization of tall steel buildings by a modi fi ed particle. Struct Des Tall Spec Build 23:285-301.; Gundogdu et al. 2015Gundogdu O, Egrioglu E, Aladag CH, Yolcu U (2015) Multiplicative neuron model artificial neural network based on Gaussian activation function.). Optimization function, as the critical attribute of PSO, is very useful to assess the structural damages; however, as shown in Table 11, PSO implementation in SHM is not as much of GA applications.

Table 11:
Particle Swarm Optimization (PSO) applications in SHM.

4.3 Ant Colony Optimization (ACO)

Ant Colony Optimization (ACO) was introduced by Marco Dorigo in 1992 for combinatorial optimization problems (Yu and Xu 2011Yu L, Xu P (2011) Structural health monitoring based on continuous ACO method. Microelectron Reliab 51:270-278.; Guerrero et al. 2014Guerrero GD, Cecilia JM, Llanes A, et al (2014) Comparative evaluation of platforms for parallel Ant Colony Optimization. J Supercomput 69:318-329.; Cottone et al. 2014Cottone G, Fileccia Scimemi G, Pirrotta A (2014) α-stable distributions for better performance of ACO in detecting damage on not well spaced frequency systems. Probabilistic Eng Mech 35:29-36.). ACO is a probabilistic population-based technique inspired by the ants’ foraging activities and their communication system by using pheromone trail, as the most significant role to discover the direction (Amini and Ghaderi 2012Amini F, Ghaderi P (2012) Optimal locations for MR dampers in civil structures using improved Ant Colony Algorithm. Optim Control Appl Methods 33:232-248.; Fidanova et al. 2012Fidanova S, Marinov P, Alba E (2012) Ant Algorithm for Optimal Sensor Deployment. In: Computational Intelligence, SCI 399. Springer-Verlag Berlin Heidelberg, pp 21-29; Majumdar et al. 2012Majumdar A, Kumar D, Maity D (2012) Damage assessment of truss structures from changes in natural frequencies using ant colony optimization. Appl Math Comput 218:9759-9772.). Optimization problems can be solved utilizing ACO by iterating two steps:

  • Construct candidate solutions in a probabilistic way by using a probability distribution over the search space;

  • Modify the probability distribution by candidate solutions (Yu and Xu 2011Yu Z, Fung BCM, Haghighat F, et al (2011) A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy Build 43:1409-1417.).

Ant colony based algorithm can be classified into three forms including ant-quantity, ant-density and ant-cycle. The main concept of ACO is the power of finding the shortest way from ant’s nest to food source, as indicated in Figure 11. ACO Applications used by researchers in SHM are shown in Table 12.

Figure 11:
Ant colony algorithm concept. (a) start searching for food, (b) searching all available (short or long) ways, (c) choosing the shortest way (Bachir et al. 2012Bachir B, Ali A, Abdellah M (2012) Multiobjective Optimization of an Operational Amplifier by the Ant Colony Optimisation Algorithm. Electr Electron Eng 2:230-235.).

Table 12:
Ant Colony Optimization (ACO) applications in SHM.

5 RESULTS AND DISCUSSIONS

5.1 Comparison of DMTs Applications in SHM

The DMT applications used in SHM area have been introduced and discussed in sections 3 and 4. As shown in Figure 12, the general development of DM applications in SHM, primarily since 2000, demonstrates that the main three DMTs used in SHM were ANN, PCA and GA, respectively. Overall, outcomes showed that, applications of statistical techniques in the SHM were less than artificial intelligence techniques. The percentage of DMT’s applications in SHM is shown in Figure 13. This figure indicates that ANN and PCA were the highest application rate by conducing 30% and 20% of researches, respectively. Further, GA by 10% application rate stood at the third level. In contrary, decision tree, clustering, Bayesian, PSO, regression and ACO techniques were rarely used in SHM. Other DMTs such as SVM and fuzzy were occasionally used in SHM with 6% to 8% application rate.

Figure 12:
Trend of DM applications in SHM.

Figure 13:
Application percentage of DMTs in SHM.

5.2 Capabilities and limitations

In recent years, there has been a vast increase in the number of reverse analysis in SHM generally based on two methodologies; artificial neural networks and optimization techniques. In optimization-based data mining, meta-heuristic techniques, including (1) biology inspired intelligent global search approaches such as GA and (2) swarm-based techniques such as ACO and PSO, have several applications in SHM. Nevertheless, in general, drawback of optimization approaches is time consuming. On the other hand, these methods have many differences in detail. For instance, PSO has memory and all particles retain the knowledge of good solutions, but in GA, there is no memory and when the population changes, all previous knowledge about the problem will be destroyed. From this viewpoint, ACO is not appropriate for continuous optimization problems. Despite using PCA in several SHM studies as it was able to decrease dimensions of multidimensional datasets, ANN is the most powerful DMT in classification, regression and prediction which has been broadly used in many SHM applications, because of its nonlinear learning abilities. ANN is more flexible and more accurate in comparison to other DMTs. Figure 14 illustrates the discussed DMTs trends for all applicable methods. This is also demonstrated the development of DMTs from 2000 to present.

Figure 14:
Application and developing of DMTs trend used in SHM since 2000.

In the present study, a flow chart based on DM steps along with prediction and optimization-based algorithms is proposed for SHM assessment and indicated in Figure 15. According to proposed flow chart, SHM assessment starts with measuring the damage level. After collecting the data, preprocessing step including cleaning, construction, integration and transformation of data is carried out in order to build the database. In next step (modeling), the suitable DMTs are suggested to be applied for training the data. Since various DMTs such as ANN, Fuzzy, PCA, SVM, GA, PSO, ACO, Bays, etc. exist for the same problem type, therefore, they can be employed for different purposes such as optimization, classification and perdition. Moreover, test design generation, patterns creation and their validation are also detailed in this step to determine the severity and location of damages. After pattern assessment, several rehabilitation activities (e.g. modification and strengthening the structural members, repair or replacement of the existing damaged members, and minor or major maintenance) are suggested to improve the health condition of civil structures. Based on existing problem, one or combination of these activities needs to be done to protect the structural performance.

Figure 15:
Schematic structure of the proposed Data Mining-Based Procedure to be used in SHM.

6 CONCLUSION

DM is just one of the very important steps in the Knowledge Discovery in Databases (KDD) process to extract the models, patterns and rules from raw data in large databases. There are three main approaches, namely statistical techniques, machine learning techniques and artificial intelligence techniques to apply in structural real world problems. In this paper, various DMTs which are applicable to SHM from 2000 were widely reviewed and, consequently, their main concept, methodologies, capabilities and drawbacks were discussed. Based on the presented information, the following conclusions can be drawn.

  • ANN, PCA and GA are the most common DMTs in SHM, respectively, in order to structural damage identification of various types of civil structures; buildings, bridges, reinforced concrete beams, dams, truss structures, and steel plates.

  • Classification and prediction based DMTs can be categorized into two different methods which are statistical methods and artificial intelligence methods.

  • Statistical methods such as decision tree and Bayesian have the lowest application rate in SHM due to lack of capacity, flexibility and complexity. In contrast, artificial intelligence techniques such as ANN and GA have the highest application rate in SHM due to their accuracy, flexibility, autonomy, complexity, and optimization capability.

  • In optimization-based DMTs, GA is the best optimal tool to improve the efficiency of difficult problems due to its criteria, which is based on parallelism. GA can utilize a coding set of variable from a population instead of employing the variables directly from a particular solution.

Acknowledgements

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 PG144-2016A and UM.C/625/1/HIR/MOHE/ENG/55.

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Publication Dates

  • Publication in this collection
    Dec 2017

History

  • Received
    08 Aug 2017
  • Accepted
    28 Aug 2017
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