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Digital image correlation-based structural state detection through deep learning

《结构与土木工程前沿（英文）》 2022年 第16卷 第1期   页码 45-56 doi: 10.1007/s11709-021-0777-x

摘要： This paper presents a new approach for automatical classification of structural state through deep learning. In this work, a Convolutional Neural Network (CNN) was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state. The digital image correlation (DIC) technology was utilized to collect vibration information of an actual steel frame, and subsequently, the raw signals, without further pre-processing, were directly utilized as the CNN samples. The results show that CNN can achieve 99% classification accuracy for the research model. Besides, compared with the backpropagation neural network (BPNN), the CNN had an accuracy similar to that of the BPNN, but it only consumes 19% of the training time. The outputs of the convolution and pooling layers were visually displayed and discussed as well. It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals and classify them; 2) the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN; 3) the CNN has better anti-noise ability.

关键词： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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Estimation of optimum design of structural systems via machine learning

《结构与土木工程前沿（英文）》 2021年 第15卷 第6期   页码 1441-1452 doi: 10.1007/s11709-021-0774-0

摘要： Three different structural engineering designs were investigated to determine optimum design variables, and then to estimate design parameters and the main objective function of designs directly, speedily, and effectively. Two different optimization operations were carried out: One used the harmony search (HS) algorithm, combining different ranges of both HS parameters and iteration with population numbers. The other used an estimation application that was done via artificial neural networks (ANN) to find out the estimated values of parameters. To explore the estimation success of ANN models, different test cases were proposed for the three structural designs. Outcomes of the study suggest that ANN estimation for structures is an effective, successful, and speedy tool to forecast and determine the real optimum results for any design model.

关键词： optimization     metaheuristic algorithms     harmony search     structural designs     machine learning     artificial neural networks    

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Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Hamed BOLANDI; Xuyang LI; Talal SALEM; Vishnu Naresh BODDETI; Nizar LAJNEF

《结构与土木工程前沿（英文）》 2022年 第16卷 第11期   页码 1365-1377 doi: 10.1007/s11709-022-0882-5

摘要： Finite-element analysis (FEA) for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering structures. Conventional methods, such as FEA, provide high fidelity results but require the solution of large linear systems that can be computationally intensive. Instead, Deep Learning (DL) techniques can generate results significantly faster than conventional run-time analysis. This can prove extremely valuable in real-time structural assessment applications. Our proposed method uses deep neural networks in the form of convolutional neural networks (CNN) to bypass the FEA and predict high-resolution stress distributions on loaded steel plates with variable loading and boundary conditions. The CNN was designed and trained to use the geometry, boundary conditions, and load as input to predict the stress contours. The proposed technique’s performance was compared to finite-element simulations using a partial differential equation (PDE) solver. The trained DL model can predict the stress distributions with a mean absolute error of 0.9% and an absolute peak error of 0.46% for the von Mises stress distribution. This study shows the feasibility and potential of using DL techniques to bypass FEA for stress analysis applications.

关键词： Deep Learning     finite element analysis     stress contours     structural components    

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling ofthin-walled structural components

《机械工程前沿（英文）》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0688-0

摘要： The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components. In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components. The aim is to classify three typical features of a structural component—squares, slots, and holes—into various categories based on their dimensional errors (i.e., “high precision,” “pass,” and “unqualified”). Two different types of classification schemes have been considered in this study: those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure. The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model. Based on the experimental data collected during the milling experiments, the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters (i.e., “static features”) and cutting-force data (i.e., “dynamic features”). The average classification accuracy obtained using the proposed deep learning model was 9.55% higher than the best machine learning algorithm considered in this paper. Moreover, the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises. Hence, the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.

关键词： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

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Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

《工程（英文）》 doi: 10.1016/j.eng.2023.08.011

摘要： High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures, including post-earthquake damage assessment, structural health monitoring, and seismic resilience assessment of buildings. To improve the accuracy and efficiency of structural response prediction, this study proposes a novel physics-informed deep-learning-based real-time structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy. The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model, thereby enabling higher-precision predictions. Experiments were conducted on a four-story masonry structure, an eleven-story reinforced concrete irregular structure, and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method. In addition, the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study. Furthermore, by conducting a comparative experiment, the impact of the range of seismic wave amplitudes on the prediction accuracy was studied. The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.

关键词： Structural seismic response prediction     Physics information informed     Real-time prediction     Earthquake engineering     Data-driven machine learning    

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Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

《结构与土木工程前沿（英文）》 2020年 第14卷 第5期   页码 1083-1096 doi: 10.1007/s11709-020-0654-z

摘要： The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: falling weight deflectometer and ground-penetrating radar are expensive tests; back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of , , and . Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria ( =0.841, =0.592, and =0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

关键词： transportation infrastructure     flexible pavement     structural number prediction     Gaussian process regression     M5P model tree     random forest    

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Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

Baiwan Zhou, Dongmei An, Fenglai Xiao, Running Niu, Wenbin Li, Wei Li, Xin Tong, Graham J Kemp, Dong Zhou, Qiyong Gong, Du Lei

《医学前沿（英文）》 2020年 第14卷 第5期   页码 630-641 doi: 10.1007/s11684-019-0718-4

摘要： Mesial temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is associated with functional and structural brain alterations. Machine learning (ML) techniques have been successfully used in discriminating mTLE from healthy controls. However, either functional or structural neuroimaging data are mostly used separately as input, and the opportunity to combine both has not been exploited yet. We conducted a multimodal ML study based on functional and structural neuroimaging measures. We enrolled 37 patients with left mTLE, 37 patients with right mTLE, and 74 healthy controls and trained a support vector ML model to distinguish them by using each measure and the combinations of the measures. For each single measure, we obtained a mean accuracy of 74% and 69% for discriminating left mTLE and right mTLE from controls, respectively, and 64% when all patients were combined. We achieved an accuracy of 78% by integrating functional data and 79% by integrating structural data for left mTLE, and the highest accuracy of 84% was obtained when all functional and structural measures were combined. These findings suggest that combining multimodal measures within a single model is a promising direction for improving the classification of individual patients with mTLE.

关键词： mesial temporal lobe epilepsy     functional magnetic resonance imaging     structural magnetic resonance imaging     machine learning     support vector machine    

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结构健康监测数据科学与工程研究进展 Article

鲍跃全, 陈智成, 魏世银, 徐阳, 唐志一, 李惠

《工程（英文）》 2019年 第5卷 第2期   页码 234-242 doi: 10.1016/j.eng.2018.11.027

摘要：

结构健康监测（SHM）是一个多学科交叉领域，涉及利用大量传感器和仪器对结构荷载和响应进行自动感知，然后根据收集到的数据对结构进行健康诊断。由于安装在结构上的 SHM 系统能自动实时地感知、评估和预警结构状态，所以海量数据是 SHM 的一个显著特征。与海量数据处理与分析相关的方法与技术被称为数据科学与工程，其包括数据采集、数据转换、数据管理以及数据处理与挖掘算法。本文旨在简要回顾笔者在 SHM 数据科学与工程方面开展的最新研究，具体涵盖基于压缩采样的数据采集算法、基于深度学习算法的异常数据诊断方法、基于计算机视觉技术的桥梁裂纹识别方法，以及基于机器学习算法的桥梁结构状态评估方法。最后，本文在结语部分对该领域的未来发展趋势进行了展望。

关键词： 结构健康监测     监测数据     压缩采样     机器学习     深度学习    

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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 490-505 doi: 10.1007/s11709-020-0669-5

摘要： This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy ( ), precision, recall, , and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

关键词： seismic soil liquefaction     Bayesian belief network     cone penetration test     parameter learning     structural learning    

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Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

《结构与土木工程前沿（英文）》 2022年 第16卷 第10期   页码 1249-1266 doi: 10.1007/s11709-022-0858-5

摘要： The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer (GFRP) elastic gridshell structures. Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacement of GFRP elastic gridshell structures. Several ML algorithms, including linear regression (LR), ridge regression (RR), support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine (LightGBM), are implemented in this study. Output features of structural performance considered in this study are the maximum stress as f1(x) and the maximum displacement to self-weight ratio as f2(x). A comparative study is conducted and the Catboost model presents the highest prediction accuracy. Finally, interpretable ML approaches, including shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions. SHAP is employed to describe the importance of each variable to structural performance both locally and globally. The results of sensitivity analysis (SA), feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for f1(x) and f2(x).

关键词： machine learning     gridshell structure     regression     sensitivity analysis     interpretability methods    

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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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Approximation of structural damping and input excitation force

Mohammad SALAVATI

《结构与土木工程前沿（英文）》 2017年 第11卷 第2期   页码 244-254 doi: 10.1007/s11709-016-0371-9

摘要： Structural dynamic characteristics are the most significant parameters that play a decisive role in structural damage assessment. The more sensitive parameter to the damage is the damping behavior of the structure. The complexity of structural damping mechanisms has made this parameter to be one of the ongoing research topics. Despite all the difficulties in the modeling of damping, there are some approaches like as linear and nonlinear models which are described as the energy dissipation throughout viscous, material or structural hysteretic and frictional damping mechanisms. In the presence of a mathematical model of the damping mechanisms, it is possible to estimate the damping ratio from the theoretical comparison of the damped and un-damped systems. On the other hand, solving the inverse problem of the input force estimation and its distribution to each SDOFs, from the measured structural responses plays an important role in structural identification process. In this paper model-based damping approximation method and a model-less structural input estimation are considered. The effectiveness of proposed methods has been carried out through analytical and numerical simulation of the lumped mass system and the results are compared with reference data. Consequently, high convergence of the comparison results illustrates the satisfactory of proposed approximation methods.

关键词： structural modal parameters     damping identification method     input excitation force identification     Inverse problem    

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Effects of green roof damping and configuration on structural seismic response

《结构与土木工程前沿（英文）》   页码 1133-1144 doi: 10.1007/s11709-023-0959-9

摘要： Sustainable structures are critical for addressing global climate change. Hence, their structural resilience or ability to recover from natural events must be considered comprehensively. Green roofs are a widely used sustainable feature that improve the environment while providing excellent occupant amenity. To expand their usage, their inherent damping and layout sensitivity to seismic performance are investigated in this study. The soil of a green roof can serve as a damper to dissipate the energy generated by earthquakes or other dynamic events. Results of preliminary analysis show that a green roof soil can increase localized damping by 2.5% under both dry and saturated conditions. Based on these findings, nonlinear time-history analyses are conducted on a three-story building in SAP2000 to monitor the structural behavior with and without a green roof. The increased damping in the green roof soil is beneficial to the structural performance, i.e., it reduces the building displacement and acceleration by 10% and 12%, respectively. Additionally, certain configurations are more effective and beneficial to the structural response than others, which suggests the possibility of design optimization. Based on the findings of this study, new methods of modeling and considering green roofs in structural design are established.

关键词： green infrastructure     green roof     structural resilience     seismic design    

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Development of an integrated structural health monitoring system for bridge structures in operational

Xinqun ZHU, Hong HAO

《结构与土木工程前沿（英文）》 2012年 第6卷 第3期   页码 321-333 doi: 10.1007/s11709-012-0161-y

摘要： This paper presents an overview of development of an integrated structural health monitoring system. The integrated system includes vibration and guided-wave based structural health monitoring. It integrates the real-time heterogeneous sensor data acquiring system, data analysis and interpretation, physical-based numerical simulation of complex structural system under operational conditions and structural evaluation. The study is mainly focused on developing: integrated sensor technology, integrated structural damage identification with operational loads monitoring, and integrated structural evaluation with results from system identification. Numerical simulation and its implementation in laboratory show that the system is effective and reliable to detect local damage and global conditions of bridge structures.

关键词： integrated structural health monitoring     operational conditions     vibration and guided wave    

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基于计算机视觉的民用基础设施的检查与监测研究进展 Review

Billie F. Spencer Jr.,Vedhus Hoskere,Yasutaka Narazaki

《工程（英文）》 2019年 第5卷 第2期   页码 199-222 doi: 10.1016/j.eng.2018.11.030

摘要：

计算机视觉技术与远程摄像机和无人机（UAVs）的采集相结合，为民用基础设施状况评估提供了前景良好的非接触式解决方案。这种系统的最终目标是自动且稳健地将图像或视频数据转换为可操作的信息。本文概述了将计算机视觉技术应用于民用基础设施状态评估的最新进展。特别介绍了计算机视觉、机器学习和结构工程领域的相关研究。评估工作分为两类：检查应用和监测应用。检查应用包括识别环境，如结构构件，表征局部和全部的可见损坏，以及检测参考图像的变化。监测应用包括应变和位移的静态测量，以及模态分析的位移动态测量。最后，文章指出了为实现基于自动化视觉的民用基础设施和监测目标而持续存在的一些关键挑战，以及为解决这些挑战而正在进行的工作。

关键词： 结构检查和监测     人工智能     计算机视觉     机器学习     光流    

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标题 作者 时间 类型 操作

Digital image correlation-based structural state detection through deep learning

期刊论文

Estimation of optimum design of structural systems via machine learning

期刊论文

Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Hamed BOLANDI; Xuyang LI; Talal SALEM; Vishnu Naresh BODDETI; Nizar LAJNEF

期刊论文

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling ofthin-walled structural components

期刊论文

Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

期刊论文

Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

期刊论文

Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

Baiwan Zhou, Dongmei An, Fenglai Xiao, Running Niu, Wenbin Li, Wei Li, Xin Tong, Graham J Kemp, Dong Zhou, Qiyong Gong, Du Lei

期刊论文

结构健康监测数据科学与工程研究进展

鲍跃全, 陈智成, 魏世银, 徐阳, 唐志一, 李惠

期刊论文

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

期刊论文

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

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

Approximation of structural damping and input excitation force

Mohammad SALAVATI

期刊论文

Effects of green roof damping and configuration on structural seismic response

期刊论文

Development of an integrated structural health monitoring system for bridge structures in operational

Xinqun ZHU, Hong HAO

期刊论文

基于计算机视觉的民用基础设施的检查与监测研究进展

Billie F. Spencer Jr.,Vedhus Hoskere,Yasutaka Narazaki

期刊论文