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Digital image correlation-based structural state detection through deep learning

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 1,   Pages 45-56 doi: 10.1007/s11709-021-0777-x

Abstract: This paper presents a new approach for automatical classification of structural state through deep learningdesigned to fuse both the feature extraction and classification blocks into an intelligent and compact learningsystem and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state.It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals

Keywords： 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

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 6,   Pages 1441-1452 doi: 10.1007/s11709-021-0774-0

Abstract: Three different structural engineering designs were investigated to determine optimum design variablesTo explore the estimation success of ANN models, different test cases were proposed for the three structural

Keywords： optimization     metaheuristic algorithms     harmony search     structural designs     machine learning     artificial    

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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

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1365-1377 doi: 10.1007/s11709-022-0882-5

Abstract: Instead, Deep Learning (DL) techniques can generate results significantly faster than conventional run-timeThis can prove extremely valuable in real-time structural assessment applications.

Keywords： 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

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0688-0

Abstract: study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structuralThe aim is to classify three typical features of a structural component—squares, slots, and holes—intoThe classification accuracy of the popular machine learning methods has been evaluated in comparisonwith the proposed deep learning model.than the best machine learning algorithm considered in this paper.

Keywords： 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,

Engineering doi: 10.1016/j.eng.2023.08.011

Abstract: High-precision and efficient structural response prediction is essential for intelligent disaster preventionand mitigation in building structures, including post-earthquake damage assessment, structural healthTo improve the accuracy and efficiency of structural response prediction, this study proposes a novelphysics-informed deep-learning-based real-time structural response prediction method that can predict

Keywords： 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

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1083-1096 doi: 10.1007/s11709-020-0654-z

Abstract: 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 deflectometerIn this study, three machine learning methods entitled Gaussian process regression, M5P model tree, andrandom forest used for the prediction of structural numbers in flexible pavements.Using machine learning methods instead of back-calculation improves the calculation process quality and

Keywords： transportation infrastructure     flexible pavement     structural number prediction     Gaussian process regression    

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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

Frontiers of Medicine 2020, Volume 14, Issue 5,   Pages 630-641 doi: 10.1007/s11684-019-0718-4

Abstract: temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is associated with functional and structuralMachine learning (ML) techniques have been successfully used in discriminating mTLE from healthy controlsHowever, either functional or structural neuroimaging data are mostly used separately as input, and theWe conducted a multimodal ML study based on functional and structural neuroimaging measures.We achieved an accuracy of 78% by integrating functional data and 79% by integrating structural data

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

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The State of the Art of Data Science and Engineering in Structural Health Monitoring Article

Yuequan Bao, Zhicheng Chen, Shiyin Wei, Yang Xu, Zhiyi Tang, Hui Li

Engineering 2019, Volume 5, Issue 2,   Pages 234-242 doi: 10.1016/j.eng.2018.11.027

Abstract:

Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensingof structural loads and response by means of a large number of sensors and instruments, followed bya diagnosis of the structural health based on the collected data.compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learningapproaches using computer vision techniques, and condition assessment approaches for bridges using machine learning

Keywords： Structural health monitoring     Monitoring data     Compressive sampling     Machine learning     Deep learning    

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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

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 490-505 doi: 10.1007/s11709-020-0669-5

Abstract: This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-inducedbased on the cone penetration test field case history records using the Bayesian belief network (BBN) learningclimbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning

Keywords： seismic soil liquefaction     Bayesian belief network     cone penetration test     parameter learning     structurallearning    

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

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   Pages 1249-1266 doi: 10.1007/s11709-022-0858-5

Abstract: The prediction of structural performance plays a significant role in damage assessment of glass fiberMachine learning (ML) approaches are implemented in this study, to predict maximum stress and displacementOutput features of structural performance considered in this study are the maximum stress as fSHAP is employed to describe the importance of each variable to structural performance both locally and

Keywords： 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

Frontiers of Structural and Civil Engineering 2015, Volume 9, Issue 1,   Pages 1-16 doi: 10.1007/s11709-014-0277-3

Abstract: large amount of researches and studies have been recently performed by applying statistical and machine learningdata driven strategy is proposed, consisting of the combination of advanced statistical and machine learning

Keywords： structural health monitoring     early-damage detection     principal component analysis     symbolic data     symbolic    

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Approximation of structural damping and input excitation force

Mohammad SALAVATI

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 2,   Pages 244-254 doi: 10.1007/s11709-016-0371-9

Abstract: Structural dynamic characteristics are the most significant parameters that play a decisive role in structuralThe complexity of structural damping mechanisms has made this parameter to be one of the ongoing researchand nonlinear models which are described as the energy dissipation throughout viscous, material or structuralresponses plays an important role in structural identification process.In this paper model-based damping approximation method and a model-less structural input estimation are

Keywords： structural modal parameters     damping identification method     input excitation force identification     Inverse    

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Effects of green roof damping and configuration on structural seismic response

Frontiers of Structural and Civil Engineering   Pages 1133-1144 doi: 10.1007/s11709-023-0959-9

Abstract: Hence, their structural resilience or ability to recover from natural events must be considered comprehensively, nonlinear time-history analyses are conducted on a three-story building in SAP2000 to monitor the structuralThe increased damping in the green roof soil is beneficial to the structural performance, i.e., it reducesAdditionally, certain configurations are more effective and beneficial to the structural response thanBased on the findings of this study, new methods of modeling and considering green roofs in structural

Keywords： 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

Frontiers of Structural and Civil Engineering 2012, Volume 6, Issue 3,   Pages 321-333 doi: 10.1007/s11709-012-0161-y

Abstract: 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.system under operational conditions and structural evaluation.The study is mainly focused on developing: integrated sensor technology, integrated structural damageidentification with operational loads monitoring, and integrated structural evaluation with results

Keywords： integrated structural health monitoring     operational conditions     vibration and guided wave    

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Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring Review

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

Engineering 2019, Volume 5, Issue 2,   Pages 199-222 doi: 10.1016/j.eng.2018.11.030

Abstract: In particular, relevant research in the fields of computer vision, machine learning, and structural engineeringThe inspection applications reviewed include identifying context such as structural components, characterizing

Keywords： Structural inspection and monitoring     Artificial intelligence     Computer vision     Machine learning     Optical    

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Title Author Date Type Operation

Digital image correlation-based structural state detection through deep learning

Journal Article

Estimation of optimum design of structural systems via machine learning

Journal Article

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

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

Journal Article

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

Journal Article

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

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

Journal Article

Estimation of flexible pavement structural capacity using machine learning techniques

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

Journal Article

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

Journal Article

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Yuequan Bao, Zhicheng Chen, Shiyin Wei, Yang Xu, Zhiyi Tang, Hui Li

Journal Article

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

Journal Article

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

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

Journal Article

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

Journal Article

Approximation of structural damping and input excitation force

Mohammad SALAVATI

Journal Article

Effects of green roof damping and configuration on structural seismic response

Journal Article

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

Xinqun ZHU, Hong HAO

Journal Article

Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

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

Journal Article