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Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

《结构与土木工程前沿（英文）》   页码 1281-1294 doi: 10.1007/s11709-023-0975-9

摘要： Concrete is widely used in various large construction projects owing to its high durability, compressive strength, and plasticity. However, the tensile strength of concrete is low, and concrete cracks easily. Changes in the concrete structure will result in changes in parameters such as the frequency mode and curvature mode, which allows one to effectively locate and evaluate structural damages. In this study, the characteristics of the curvature modes in concrete structures are analyzed and a method to obtain the curvature modes based on the strain and displacement modes is proposed. Subsequently, various indices for the damage diagnosis of concrete structures based on the curvature mode are introduced. A damage assessment method for concrete structures is established using an artificial bee colony backpropagation neural network algorithm. The proposed damage assessment method for dam concrete structures comprises various modal parameters, such as curvature and frequency. The feasibility and accuracy of the model are evaluated based on a case study of a concrete gravity dam. The results show that the damage assessment model can accurately evaluate the damage degree of concrete structures with a maximum error of less than 2%, which is within the required accuracy range of damage identification and assessment for most concrete structures.

关键词： hydraulic structure     curvature mode     damage detection     artifical neural network     artificial bee colony    

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An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

《结构与土木工程前沿（英文）》 2020年 第14卷 第4期   页码 907-929 doi: 10.1007/s11709-020-0628-1

摘要： In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation ( ) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.

关键词： two-stage method     modal strain energy     surrogate model     GMDH     optimization damage detection    

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Damage detection in beam-like structures using static shear energy redistribution

《结构与土木工程前沿（英文）》 2022年 第16卷 第12期   页码 1552-1564 doi: 10.1007/s11709-022-0903-4

摘要： In this study, a static shear energy algorithm is presented for the damage assessment of beam-like structures. According to the energy release principle, the strain energy of a damaged element suddenly changes when structural damage occurs. Therefore, the change in the static shear energy is employed to determine the damage locations in beam-like structures. The static shear energy is derived from the spectral factorization of the elementary stiffness matrix and structural deflection variation. The advantage of using shear energy as opposed to total energy is that only a few deflection data points of the beam structure are required during the process of damage identification. Another advantage of the proposed approach is that damage detection can be performed without establishing a structural finite-element model in advance. The proposed technique is first validated using a numerical example with single, multiple, and adjacent damage scenarios. A channel steel beam and rectangular concrete beam are employed as experimental cases to further verify the proposed approach. The results of the simulation and experiment examples indicate that the proposed algorithm provides a simple and effective method for defect localization in beam-like structures.

关键词： damage detection     beam structure     strain energy     static displacement variation     energy damage index    

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Multiple damage detection in complex bridges based on strain energy extracted from single point measurement

Alireza ARABHA NAJAFABADI, Farhad DANESHJOO, Hamid Reza AHMADI

《结构与土木工程前沿（英文）》 2020年 第14卷 第3期   页码 722-730 doi: 10.1007/s11709-020-0624-5

摘要： Strain Energy of the structure can be changed with the damage at the damage location. The accurate detection of the damage location using this index in a force system is dependent on the degree of accuracy in determining the structure deformation function before and after damage. The use of modal-based methods to identify damage in complex bridges is always associated with problems due to the need to consider the effects of higher modes and the adverse effect of operational conditions on the extraction of structural modal parameters. In this paper, the deformation of the structure was determined by the concept of influence line using the Betti-Maxwell theory. Then two damage detection indicators were developed based on strain energy variations. These indices were presented separately for bending and torsion changes. Finite element analysis of a five-span concrete curved bridge was done to validate the stated methods. Damage was simulated by decreasing stiffness at different sections of the deck. The response regarding displacement of a point on the deck was measured along each span by passing a moving load on the bridge at very low speeds. Indicators of the strain energy extracted from displacement influence line and the strain energy extracted from the rotational displacement influence line (SERIL) were calculated for the studied bridge. The results show that the proposed methods have well identified the location of the damage by significantly reducing the number of sensors required to record the response. Also, the location of symmetric damages is detected with high resolution using SERIL.

关键词： damage detection     strain energy     influence line     complex bridges     rotation displacement    

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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

《结构与土木工程前沿（英文）》 2015年 第9卷 第1期   页码 1-16 doi: 10.1007/s11709-014-0277-3

摘要： A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.

关键词： structural health monitoring     early-damage detection     principal component analysis     symbolic data     symbolic dissimilarity measures     cluster analysis     numerical model     damage simulations    

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Detection of oxidative stress and DNA damage in freshwater snail

Daoud Ali, Huma Ali, Saud Alifiri, Saad Alkahtani, Abdullah A Alkahtane, Shaik Althaf Huasain

《环境科学与工程前沿（英文）》 2018年 第12卷 第5期 doi: 10.1007/s11783-018-1039-6

摘要：

Freshwater snail (Lymnea luteola L.) is good bio indicator of water pollution.

Profenofos is tested for its molluscicidal activity against Lymnea luteola L. snail.

Deleterious effects on some oxidative stress were detected.

Profenofos has a genotoxic effect on Lymnea luteola L. snails.

关键词： Acute toxicity     Profenofos     ROS     oxidative stress     DNA damage     Lymnea luteola    

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A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced

《结构与土木工程前沿（英文）》 2021年 第15卷 第6期   页码 1453-1479 doi: 10.1007/s11709-021-0767-z

摘要： This paper proposes a new Deep Feed-forward Neural Network (DFNN) approach for damage detection in functionally graded carbon nanotube-reinforced composite (FG-CNTRC) plates. In the proposed approach, the DFNN model is developed based on a data set containing 20 000 samples of damage scenarios, obtained via finite element (FE) simulation, of the FG-CNTRC plates. The elemental modal kinetic energy (MKE) values, calculated from natural frequencies and translational nodal displacements of the structures, are utilized as input of the DFNN model while the damage locations and corresponding severities are considered as output. The state-of-the art Exponential Linear Units (ELU) activation function and the Adamax algorithm are employed to train the DFNN model. Additionally, in order to enhance the performance of the DFNN model, the mini-batch and early-stopping techniques are applied to the training process. A trial-and-error procedure is implemented to determine suitable parameters of the network such as the number of hidden layers and the number of neurons in each layer. The accuracy and capability of the proposed DFNN model are illustrated through two distinct configurations of the CNT-fibers constituting the FG-CNTRC plates including uniform distribution (UD) and functionally graded-V distribution (FG-VD). Furthermore, the performance and stability of the DFNN model with the consideration of noise effects on the input data are also investigated. Obtained results indicate that the proposed DFNN model is able to give sufficiently accurate damage detection outcomes for the FG-CNTRC plates for both cases of noise-free and noise-influenced data.

关键词： damage detection     deep feed-forward neural networks     functionally graded carbon nanotube-reinforced composite plates     modal kinetic energy    

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Damage identification in connections of moment frames using time domain responses and an optimization

《结构与土木工程前沿（英文）》 2021年 第15卷 第4期   页码 851-866 doi: 10.1007/s11709-021-0739-3

摘要： Damage is defined as changes to the material and/or geometric properties of a structural system, comprising changes to the boundary conditions and system connectivity, adversely affecting the system’s performance. Inspecting the elements of structures, particularly critical components, is vital to evaluate the structural lifespan and safety. In this study, an optimization-based method for joint damage identification of moment frames using the time-domain responses is introduced. The beam-to-column connection in a metallic moment frame structure is modeled by a zero-length rotational spring at both ends of the beam element. For each connection, an end-fixity factor is specified, which changes between 0 and 1. Then, the problem of joint damage identification is converted to a standard optimization problem. An objective function is defined using the nodal point accelerations extracted from the damaged structure and an analytical model of the structure in which the nodal accelerations are obtained using the Newmark procedure. The optimization problem is solved by an improved differential evolution algorithm (IDEA) for identifying the location and severity of the damage. To assess the capability of the proposed method, two numerical examples via different damage scenarios are considered. Then, a comparison between the proposed method and the existing damage identification method is provided. The outcomes reveal the high efficiency of the proposed method for finding the severity and location of joint damage considering noise effects.

关键词： damage identification     beam-to-column connection     time-domain response     optimization    

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Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep

Alipujiang JIERULA, Tae-Min OH, Shuhong WANG, Joon-Hyun LEE, Hyunwoo KIM, Jong-Won LEE

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 318-332 doi: 10.1007/s11709-021-0715-y

摘要： The aim of this study is to propose a new detection method for determining the damage locations in pile foundations based on deep learning using acoustic emission data. First, the damage location is simulated using a back propagation neural network deep learning model with an acoustic emission data set acquired from pile hit experiments. In particular, the damage location is identified using two parameters: the pile location ( ) and the distance from the pile cap ( ). This study investigates the influences of various acoustic emission parameters, numbers of sensors, sensor installation locations, and the time difference on the prediction accuracy of and . In addition, correlations between the damage location and acoustic emission parameters are investigated. Second, the damage step condition is determined using a classification model with an acoustic emission data set acquired from uniaxial compressive strength experiments. Finally, a new damage detection and evaluation method for pile foundations is proposed. This new method is capable of continuously detecting and evaluating the damage of pile foundations in service.

关键词： pile foundations     damage location     acoustic emission     deep learning     damage step    

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Spectral element modeling based structure piezoelectric impedance computation and damage identification

Zhigang GUO, Zhi SUN

《结构与土木工程前沿（英文）》 2011年 第5卷 第4期   页码 458-464 doi: 10.1007/s11709-011-0133-7

摘要： This paper presents a numerical simulation study on electromechanical impedance technique for structural damage identification. The basic principle of impedance based damage detection is structural impedance will vary with the occurrence and development of structural damage, which can be measured from electromechanical admittance curves acquired from PZT patches. Therefore, structure damage can be identified from the electromechanical admittance measurements. In this study, a model based method that can identify both location and severity of structural damage through the minimization of the deviations between structural impedance curves and numerically computed response is developed. The numerical model is set up using the spectral element method, which is promised to be of high numerical efficiency and computational accuracy in the high frequency range. An optimization procedure is then formulated to estimate the property change of structural elements from the electric admittance measurement of PZT patches. A case study on a pin-pin bar is conducted to investigate the feasibility of the proposed method. The results show that the presented method can accurately identify bar damage location and severity even when the measurements are polluted by 5% noise.

关键词： PZT     piezoelectric impedance     optimization     spectral element     damage identification    

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Uncertainty quantification of stability and damage detection parameters of coupled hydrodynamic-ground

Nazim Abdul NARIMAN, Tom LAHMER, Peyman KARAMPOUR

《结构与土木工程前沿（英文）》 2019年 第13卷 第2期   页码 303-323 doi: 10.1007/s11709-018-0462-x

摘要： In this paper, models of the global system of the Koyna dam have been created using ABAQUS software considering the dam-reservoir-foundation interaction. Non coupled models and the coupled models were compared regarding the horizontal displacement of the dam crest and the differential settlement of the dam base in clay foundation. Meta models were constructed and uncertainty quantification process was adopted by the support of Sobol’s sensitivity indices considering five uncertain parameters by exploiting Box-Behnken experimental method. The non coupled models results determined overestimated predicted stability and damage detection in the dam. The rational effects of the reservoir height were very sensitive in the variation of the horizontal displacement of the dam crest with a small interaction effect with the beta viscous damping coefficient of the clay foundation. The modulus of elasticity of the clay foundation was the decisive parameter regarding the variation of the differential settlement of the dam base. The XFEM approach has been used for damage detection in relation with both minimum and maximum values of each uncertain parameter. Finally the effects of clay and rock foundations were determined regarding the resistance against the propagation of cracks in the dam, where the rock foundation was the best.

关键词： massed foundation     hydrodynamic pressure     Box-Behnken method     meta model     Sobol’s sensitivity indices    

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Optimization of the mechanical performance and damage failure characteristics of laminated composites

《结构与土木工程前沿（英文）》   页码 1357-1369 doi: 10.1007/s11709-023-0996-4

摘要： In this study, the effect of fiber angle on the tensile load-bearing performance and damage failure characteristics of glass composite laminates was investigated experimentally, analytically, and numerically. The glass fabric in the laminate was perfectly aligned along the load direction (i.e., at 0°), offset at angles of 30° and 45°, or mixed in different directions (i.e., 0°/30° or 0°/45°). The composite laminates were fabricated using vacuum-assisted resin molding. The influence of fiber orientation angle on the mechanical properties and stiffness degradation of the laminates was studied via cyclic tensile strength tests. Furthermore, simulations have been conducted using finite element analysis and analytical approaches to evaluate the influence of fiber orientation on the mechanical performance of glass laminates. Experimental testing revealed that, although the composite laminates laid along the 0° direction exhibited the highest stiffness and strength, their structural performance deteriorated rapidly. We also determined that increasing the fiber offset angle (i.e., 30°) could optimize the mechanical properties and damage failure characteristics of glass laminates. The results of the numerical and analytical approaches demonstrated their ability to capture the mechanical behavior and damage failure modes of composite laminates with different fiber orientations, which may be used to prevent the catastrophic failures that occur in composite laminates.

关键词： fiber orientation     composite laminates     stiffness degradation     analytical approaches     finite element analysis    

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Development of temperature-robust damage factor based on sensor fusion for a wind turbine structure

Jong-Woong PARK,Sung-Han SIM,Jin-Hak YI,Hyung-Jo JUNG

《结构与土木工程前沿（英文）》 2015年 第9卷 第1期   页码 42-47 doi: 10.1007/s11709-014-0285-3

摘要： Wind power systems have gained much attention due to the relatively high reliability, maturity in technology and cost competitiveness compared to other renewable alternatives. Advances have been made to increase the power efficiency of the wind turbines while less attention has been focused on structural integrity assessment of the structural systems. Vibration-based damage detection has widely been researched to identify damages on a structure based on change in dynamic characteristics. Widely spread methods are natural frequency-based, mode shape-based, and curvature mode shape-based methods. The natural frequency-based methods are convenient but vulnerable to environmental temperature variation which degrades damage detection capability; mode shapes are less influenced by temperature variation and able to locate damage but requires extensive sensor instrumentation which is costly and vulnerable to signal noises. This study proposes novelty of damage factor based on sensor fusion to exclude effect of temperature variation. The combined use of an accelerometer and an inclinometer was considered and damage factor was defined as a change in relationship between those two measurements. The advantages of the proposed method are: 1) requirement of small number of sensor, 2) robustness to change in temperature and signal noise and 3) ability to roughly locate damage. Validation of the proposed method is carried out through numerical simulation on a simplified 5 MW wind turbine model.

关键词： sensor fusion     damage detection     structural health monitoring    

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基于双层多目标分割的超高速撞击航天器损伤红外检测算法 Research Article

杨晓1,殷春1,Sara DADRAS2,雷光钰1,谭旭彤1,邱根1

《信息与电子工程前沿（英文）》 2022年 第23卷 第4期   页码 571-586 doi: 10.1631/FITEE.2000695

摘要： 针对超高速撞击引起的航天器损伤检测，提出一种先进的基于红外成像检测的航天器缺陷提取算法。采用高速混合模型对红外视频流采样数据中的温度变化特征进行分类，并重构图像，得到反映缺陷特征的红外重构图像。设计的分割目标函数用于保证图像分割结果对噪声去除和细节保留的有效性，同时考虑到红外重构图像的复杂性，即所需权衡不同。因此，引入多目标优化算法以实现细节保留和噪声去除之间的平衡，并采用基于分解的多目标进化算法（MOEA/D）进行优化，以保证损伤分割的准确性。实验结果验证了所提算法的有效性。

关键词： 超高速撞击损伤； 缺陷检测；高斯混合模型；图像分割    

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Crack detection of the cantilever beam using new triple hybrid algorithms based on Particle Swarm Optimization

Amin GHANNADIASL; Saeedeh GHAEMIFARD

《结构与土木工程前沿（英文）》 2022年 第16卷 第9期   页码 1127-1140 doi: 10.1007/s11709-022-0838-9

摘要： The presence of cracks in a concrete structure reduces its performance and increases in the size of cracks result in the failure of the structure. Therefore, the accurate determination of crack characteristics, such as location and depth, is one of the key engineering issues for assessment of the reliability of structures. This paper deals with the inverse analysis of the crack detection problems using triple hybrid algorithms based on Particle Swarm Optimization (PSO); these hybrids are Particle Swarm Optimization-Genetic Algorithm-Firefly Algorithm (PSO-GA-FA), Particle Swarm Optimization-Grey Wolf Optimization-Firefly Algorithm (PSO-GWO-FA), and Particle Swarm Optimization-Genetic Algorithm-Grey Wolf Optimization (PSO-GA-GWO). A strong correlation exists between the changes in the natural frequency of a concrete beam and the crack parameters. Thus, the location and depth of a crack in a beam can be predicted by measuring its natural frequency. Hence, the measured natural frequency can be used as the input parameter of the algorithm. In this paper, this is applied to identify crack location and depth in a cantilever beam using the new hybrid algorithms. The results show that among the proposed triple hybrid algorithms, the PSO-GA-FA and PSO-GWO-FA algorithms are much more effective than PSO-GA-GWO algorithm for the crack detection.

关键词： crack     cantilever beam     triple hybrid algorithms     Particle Swarm Optimization    

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标题 作者 时间 类型 操作

Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

期刊论文

An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

期刊论文

Damage detection in beam-like structures using static shear energy redistribution

期刊论文

Multiple damage detection in complex bridges based on strain energy extracted from single point measurement

Alireza ARABHA NAJAFABADI, Farhad DANESHJOO, Hamid Reza AHMADI

期刊论文

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

期刊论文

Detection of oxidative stress and DNA damage in freshwater snail

Daoud Ali, Huma Ali, Saud Alifiri, Saad Alkahtani, Abdullah A Alkahtane, Shaik Althaf Huasain

期刊论文

A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced

期刊论文

Damage identification in connections of moment frames using time domain responses and an optimization

期刊论文

Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep

Alipujiang JIERULA, Tae-Min OH, Shuhong WANG, Joon-Hyun LEE, Hyunwoo KIM, Jong-Won LEE

期刊论文

Spectral element modeling based structure piezoelectric impedance computation and damage identification

Zhigang GUO, Zhi SUN

期刊论文

Uncertainty quantification of stability and damage detection parameters of coupled hydrodynamic-ground

Nazim Abdul NARIMAN, Tom LAHMER, Peyman KARAMPOUR

期刊论文

Optimization of the mechanical performance and damage failure characteristics of laminated composites

期刊论文

Development of temperature-robust damage factor based on sensor fusion for a wind turbine structure

Jong-Woong PARK,Sung-Han SIM,Jin-Hak YI,Hyung-Jo JUNG

期刊论文

基于双层多目标分割的超高速撞击航天器损伤红外检测算法

杨晓1,殷春1,Sara DADRAS2,雷光钰1,谭旭彤1,邱根1

期刊论文

Crack detection of the cantilever beam using new triple hybrid algorithms based on Particle Swarm Optimization

Amin GHANNADIASL; Saeedeh GHAEMIFARD

期刊论文