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Article
Engineering, Multidisciplinary
Muhammad Zohaib Sarwar et al.
Summary: Vehicle assisted monitoring has the potential to assess the condition of existing bridges in road networks more effectively than traditional structural health monitoring methods. This paper proposes a probabilistic deep neural network framework for damage assessment, which combines measurements from fixed sensors on the bridge and on-board vehicle sensors. The proposed method shows lower uncertainties in damage detection and localization, and is robust in handling measurement noise and varying environmental conditions.
Article
Engineering, Mechanical
Xiaoming Lei et al.
Summary: This study proposes a residual attention network (RAN) that employs the attention mechanism and residual learning to improve classification efficiency and accuracy, aiming to address the impact of abnormal monitoring data on structural assessment. The hourly segmented measured data is transformed into matrix form through mutual information correlation analysis for training a deep learning model. The proposed RAN model shows excellent classification performance in identifying most anomaly data types in the test dataset and demonstrates good generalization performance with another cable-stayed bridge dataset, outperforming existing preprocessing and deep learning models in multi-classification and classification accuracy.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Kimiya Azimbeik et al.
Summary: The aim of this study is to enhance image-based, full-field displacement measurement using new template-matching and camera-calibration methods. Practical methods to increase the number of pixels in the images, using free markers and improving camera-calibration methods are also investigated.
Article
Engineering, Multidisciplinary
Jiao Yunmei et al.
Summary: This paper proposes a multi-point chain laser detection model and method for dynamic deflection of super long-span bridges to solve the problems of poor real-time performance and low precision of deflection measurement. A multi-point deflection parameter detection model based on chain laser reference and image processing is constructed, and the hardware framework of the system is designed. The measurement accuracy of the system can reach 1 mm, realizing the real-time, dynamic, and high-precision monitoring of the deflection of the long-span bridge beam.
Article
Engineering, Civil
Xiaoming Lei et al.
Journal of Bridge Engineering
(2023)
Article
Engineering, Civil
Zixiang Yue et al.
Summary: This paper introduces a digital regression model of temperature-induced deflection for cable-stayed bridges based on an LSTM network. The model improves the output accuracy and stability of the temperature-induced deflection model by considering time dependence and utilizing deep learning techniques.
ENGINEERING STRUCTURES
(2022)
Article
Engineering, Civil
Dionysius M. Siringoringo et al.
Summary: The paper describes techniques for detecting bearing malfunction directly from the seismic response of the bridge using wavelet transform time-varying identification. The accuracy and efficacy of the techniques were verified through numerical simulations using analytical and finite element models. Results demonstrate that wavelet-based features can effectively characterize the condition of the isolation bearing directly from seismic responses of girder and piers.
JOURNAL OF EARTHQUAKE ENGINEERING
(2022)
Article
Construction & Building Technology
Yi Zhou et al.
Summary: This study proposes analytical formulas for temperature-induced displacements in suspension bridges with any number of spans, taking into account tower lateral stiffness and the elastic deformation of cables caused by tension changes. The derived formulas are validated using field monitoring data from the Maanshan Yangtze River Bridge. A concise unified one-dimensional formula is further derived for practical use, enabling engineers to better understand the behavior of suspension bridges and predict thermal deformation in a simple, generalized way.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Construction & Building Technology
Ye Xia et al.
Summary: This study proposes a comprehensive data-driven framework for network-level bridge condition assessment. By analyzing periodic bridge inspection reports in China, the future condition of bridges can be predicted, and maintenance strategies can be optimized to meet economic and performance constraints.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Engineering, Civil
Bjorn T. Svendsen et al.
Summary: This study presents a data-based SHM approach for damage detection in steel bridges, using extensive experimental research to collect data under different structural conditions while considering both local and global responses. The findings demonstrate that unsupervised machine learning can effectively detect structural damage in steel bridges and perform as well as supervised machine learning methods.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2022)
Article
Engineering, Civil
Yan Wang et al.
Summary: This paper proposes a performance warning method to detect structural anomalies in bridges, which improves warning accuracy and location identification by eliminating wind and traffic effects and considering the impacts of climate and icing.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiaoming Lei et al.
Summary: This study develops a deep reinforcement learning-based framework to optimize the life-cycle maintenance strategies of regional bridges. The framework is able to generate multiple optimal maintenance strategies that match budget constraints and maximize the cost-effectiveness of maintenance actions.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Construction & Building Technology
Zhen Sun et al.
Summary: This paper proposes a machine-learning-based method to predict fatigue damage in long-span suspension bridges using monitored parameters of temperature, wind, train, and roadway loads. The results show that the method achieves high accuracy and provides valuable information for maintenance work.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Engineering, Multidisciplinary
Alireza Entezami et al.
Summary: This article proposes a novel non-parametric anomaly detection method for early damage detection in structural health monitoring. It uses machine learning concepts and the theory of empirical machine learning to define a new damage index for decision-making. The method is validated and compared with other anomaly detection techniques using dynamic and statistical features of two full-scale bridges.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Article
Green & Sustainable Science & Technology
Odey Alshboul et al.
Summary: Accurate prediction of green building costs is crucial for decision-making and management. This study presents machine learning-based algorithms for cost prediction and evaluates their accuracy.
Article
Engineering, Civil
D. P. P. Meddage et al.
Summary: This study utilized explainable machine learning to elucidate the inner workings of machine learning models and found that SHAP technique could reveal the positive and negative contributions of different factors to predictions, confirming the causality of the model's predictions.
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
(2022)
Article
Engineering, Multidisciplinary
Zhanxiong Ma et al.
Summary: A noncontact cable tension force estimation technique was developed using an integrated vision and inertial measurement system (VIS) to improve estimation accuracy by considering the movement of the VIS. The feasibility of this technique was demonstrated through laboratory and field tests, with a discrepancy of less than 1.3% compared to accelerometer estimates.
Article
Engineering, Civil
Sunjoong Kim et al.
Summary: Long-span bridges are vulnerable to wind-induced vibration, and accurate VIV assessments are crucial for ensuring vibrational serviceability. A general framework is proposed to introduce ML algorithms for VIV prediction with limited information.
ENGINEERING STRUCTURES
(2022)
Article
Engineering, Civil
Xiaoming Lei et al.
Summary: The study proposed a multi-level vulnerability assessment framework to predict the deterioration of regional bridges and comprehensively assess the service performance of bridge networks. Global and local network vulnerabilities were revealed through edge-level, path-level, and network-level analysis.
ENGINEERING STRUCTURES
(2022)
Article
Construction & Building Technology
Zhen Sun et al.
Summary: In this paper, a method combining machine learning is developed to evaluate the effectiveness of tuned mass dampers (TMDs) in controlling wind-induced vibration in long-span bridges. Seven machine learning techniques were adopted to generate predictive models using structural health monitoring data. The proposed method is reliable, and it was found that wind velocity is the most important parameter for TMD effectiveness.
JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES
(2022)
Article
Engineering, Multidisciplinary
Boxiao Ju et al.
Summary: This study evaluates the availability of GNSS PPP and PPP-IAR technologies for bridge structural health monitoring. The results show that PPP-IAR has a faster convergence speed and higher accuracy, making it suitable for displacement identification at different levels and vertical displacement monitoring caused by vehicle loading.
Article
Engineering, Multidisciplinary
Zhiming Zhang et al.
Summary: This study combines data-driven and physics-based structural health monitoring methods using physics-guided machine learning to improve damage identification performance. Physics-guided machine learning utilizes observed feature data with correct labels and physical model output of unlabeled instances.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Engineering, Multidisciplinary
Jun Teng et al.
Summary: This study investigates the mechanism of temperature effects on structural frequency through correlation analysis, numerical simulation, and neural network techniques, finding that temperature leads to variation in beam mode frequency and has minimal impact on arch mode frequency.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Engineering, Multidisciplinary
Xiaoming Lei et al.
Summary: This study introduces a deep convolutional generative adversarial network for reconstructing lost data in structural health monitoring. The generator is trained to extract features from the data set and reconstruct lost signals using responses from remaining functional sensors, while the discriminator provides feedback to improve reconstruction accuracy. Training the model involves using reconstruction loss and adversarial loss to handle low-frequency and high-frequency features of the signals, demonstrating effectiveness through case studies.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Engineering, Multidisciplinary
Y. C. Ni et al.
Summary: This paper presents the operational modal analysis of a single-tower cable-stayed bridge based on data collected from a SHM system. A fast Bayesian FFT method was used to efficiently determine modal parameters and uncertainty. The study investigated the relationship between modal parameters and temperature, and developed a prediction model incorporating 24-hour data to analyze the probability density function of natural frequency and damping ratio for the bridge deck and cables.
Article
Engineering, Multidisciplinary
Ruijie Xi et al.
Summary: The study demonstrates that integrated GPS/BDS/GLONASS provides the highest precision in bridge monitoring, especially with high cutoff elevations due to the availability of more satellites and strongest satellite geometry. Further experiments conducted show that incorporating GPS/BDS/GLONASS at a cutoff elevation of 40 degrees allows for extremely accurate extraction of displacement and vibration signals in bridge monitoring applications.
Article
Construction & Building Technology
De-Cheng Feng et al.
Summary: In this study, an advanced machine learning model was trained and interpreted for estimating the shear strengths of squat RC walls, utilizing a database of 434 samples. The model strategically combined the XGBoost algorithm for predictive modeling and the SHAP algorithm for analyzing factor importance. This setup achieved a high level of accuracy in shear strength estimation and provided physical and quantitative interpretations of the input-output dependencies.
JOURNAL OF STRUCTURAL ENGINEERING
(2021)
Article
Engineering, Civil
Zhen Sun et al.
Summary: Efficient and reliable load-carrying capacity evaluation of deficient bridges is crucial for maintenance and management. A new method based on displacement under moving vehicles was proposed and verified through numerical simulations and full-scale implementation, showing that it is more effective and economical than conventional methods.
ENGINEERING STRUCTURES
(2021)
Article
Engineering, Multidisciplinary
Hyeon Cheol Jo et al.
Summary: This paper proposes a method for evaluating cable tension using multiple digital images, and the reliability of this method is verified through field tests. The tension evaluated using images is consistent with directly measured values, providing a cost-effective option for determining cable tension in cable-stayed bridges compared to existing systems.
Article
Engineering, Multidisciplinary
J. Rene Vazquez-Ontiveros et al.
Summary: This study presents a novel SHM technique by integrating the PPP-GNSS measurement technology and a probabilistic approach, with validation on the El Carrizo Bridge. Results show the accuracy of PPP-GNSS measurement technology and the potential benefits of the probabilistic approach.
Article
Engineering, Multidisciplinary
Shunlong Li et al.
Summary: A novel detection method using spatiotemporal graph convolutional networks was developed in this study to analyze cable forces from different cable dynamometers, providing an effective way to diagnose structural changes and damages in bridges.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Engineering, Civil
De-Cheng Feng et al.
Summary: This paper introduces a practical and comprehensive implementation of ensemble methods for predicting the shear strength of deep beams with or without web reinforcements. The study utilizes four typical ensemble machine learning models and demonstrates their superior performance over traditional machine learning methods in predicting accuracy and discrepancy.
ENGINEERING STRUCTURES
(2021)
Article
Construction & Building Technology
M. Z. Naser
Summary: The resistance towards adopting AI/ML in structural engineering stems from the lack of transparency in these technologies and the contrast with traditional methods favored by the industry and education. While engineers tend to chase good metrics when adopting AI/ML, forced goodness may lead to false inference.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Engineering, Multidisciplinary
Jinsong Zhu et al.
Summary: This paper proposes an accurate approach for obtaining spatiotemporal information of vehicle loads on bridges based on 3D bounding box reconstruction using computer vision. A deep convolutional neural network and the You Only Look Once detector are used to detect vehicles and establish the relationship between 2D and 3D bounding box of the vehicle. Through multiple objects tracking, the spatiotemporal information of the vehicle loads is obtained, showing accuracy and reliability in a bridge vehicle loads identification system on a cable-stayed bridge in operation.
Article
Engineering, Multidisciplinary
Shanshan Yu et al.
Summary: Two vision-based methods for remote bridge displacement monitoring were developed, utilizing cameras to detect motions of bridge lights or placed on the bridge deck. Both methods have shown efficacy in indoor tests and a field experiment on a cable-stayed bridge, with potential applications in safety evaluation of various bridges and large-scale structures.
Article
Engineering, Multidisciplinary
Jia He et al.
Summary: An extended Kalman filter-based substructural identification with unknown interface forces (EKF-SSIUIF) approach is proposed in this paper to simultaneously identify the parameters, interface forces, and unknown excitation of a substructure. A projection matrix is introduced to modify the observation equation and recursive solution for the extended substructural states is derived. Data fusion technique is employed to avoid low-frequency drift, and a weighted global iteration procedure is introduced for convergence and stability of the identified results. The effectiveness of the proposed approach is numerically demonstrated through linear substructures and a substructure with local nonlinearity.
Article
Construction & Building Technology
Sujith Mangalathu et al.
Summary: The study evaluates the efficiency of machine learning-based approaches in establishing accurate prediction models for the punching shear strength of flat slabs. The extreme gradient boosting model is shown to have the highest coefficient of determination and lowest mean square error estimate, outperforming existing code provisions and empirical models. The SHapley Additive explanation method is employed to explain the importance and contribution of factors influencing punching shear strength.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Construction & Building Technology
Hassan Sarmadi et al.
Summary: The article proposes a novel ensemble learning method to address the major challenge of environmental variability in structural health monitoring. By using different Mahalanobis distance metrics at multiple levels, the method aims to adapt to environmental changes and improve damage detection capability. The performance and effectiveness of the method are validated using modal features of real bridge structures, demonstrating superior results compared to state-of-the-art techniques.
STRUCTURAL CONTROL & HEALTH MONITORING
(2021)
Article
Engineering, Civil
Hao Wang et al.
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
(2020)
Article
Engineering, Civil
Yi Zhou et al.
ENGINEERING STRUCTURES
(2020)
Article
Construction & Building Technology
Hanwei Zhao et al.
STRUCTURAL CONTROL & HEALTH MONITORING
(2020)
Article
Engineering, Multidisciplinary
Jianhua Hu et al.
Article
Construction & Building Technology
Ye Xia et al.
STRUCTURAL CONTROL & HEALTH MONITORING
(2020)
Article
Engineering, Multidisciplinary
Jiwei Zhong et al.
Article
Computer Science, Interdisciplinary Applications
Yong Huang et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2019)
Article
Engineering, Multidisciplinary
Guowen Zhang et al.
Article
Engineering, Civil
Piyush Garg et al.
JOURNAL OF BRIDGE ENGINEERING
(2019)
Article
Construction & Building Technology
Jin Zhao et al.
STRUCTURAL CONTROL & HEALTH MONITORING
(2019)
Article
Construction & Building Technology
Hao Wang et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2018)
Article
Engineering, Civil
Shujin Laima et al.
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
(2018)
Article
Construction & Building Technology
Hao Wang et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2018)
Review
Construction & Building Technology
Zhen Sun et al.
STRUCTURAL CONTROL & HEALTH MONITORING
(2017)
Article
Engineering, Multidisciplinary
Thanh-Canh Huynh et al.
Article
Engineering, Civil
Tong Guo et al.
JOURNAL OF BRIDGE ENGINEERING
(2015)
Article
Computer Science, Artificial Intelligence
Erik Strumbelj et al.
KNOWLEDGE AND INFORMATION SYSTEMS
(2014)
Article
Construction & Building Technology
Shunlong Li et al.
STRUCTURAL CONTROL & HEALTH MONITORING
(2014)
Article
Engineering, Civil
Dionysius M. Siringoringo et al.
ENGINEERING STRUCTURES
(2006)
Article
Instruments & Instrumentation
P Omenzetter et al.
SMART MATERIALS AND STRUCTURES
(2006)
Article
Engineering, Civil
ZX Li et al.
ENGINEERING STRUCTURES
(2003)
