Separation of Temperature-Induced Response for Bridge Long-Term Monitoring Data Using Local Outlier Correction and Savitzky–Golay Convolution Smoothing

This study proposed a separation method to identify the temperature-induced response from the long-term monitoring data with noise and other action-induced effects. In the proposed method, the original measured data are transformed using the local outlier factor (LOF), and the threshold of the LOF is determined by minimizing the variance of the modified data. The Savitzky–Golay convolution smoothing is also utilized to filter the noise of the modified data. Furthermore, this study proposes an optimization algorithm, namely the AOHHO, which hybridizes the Aquila Optimizer (AO) and the Harris Hawks Optimization (HHO) to identify the optimal value of the threshold of the LOF. The AOHHO employs the exploration ability of the AO and the exploitation ability of the HHO. Four benchmark functions illustrate that the proposed AOHHO owns a stronger search ability than the other four metaheuristic algorithms. A numerical example and in situ measured data are utilized to evaluate the performances of the proposed separation method. The results show that the separation accuracy of the proposed method is better than the wavelet-based method and is based on machine learning methods in different time windows. The maximum separation errors of the two methods are about 2.2 times and 5.1 times that of the proposed method, respectively.

[1]  Qiao Huang,et al.  An anomaly pattern detection for bridge structural response considering time-varying temperature coefficients , 2022, Structures.

[2]  W. Zhang,et al.  Structural Damage Identification Based on Variable-Length Elements and An Improved Genetic Algorithm for Railway Bridges , 2022, Applied Sciences.

[3]  Hua Liu,et al.  Sparse Bayesian Identification of Temperature-Displacement Model for Performance Assessment and Early Warning of Bridge Bearings , 2022, Journal of Structural Engineering.

[4]  A. Bayraktar,et al.  Long-term strain behavior of in-service cable-stayed bridges under temperature variations , 2022, Journal of Civil Structural Health Monitoring.

[5]  You-liang Ding,et al.  Digital prediction model of temperature-induced deflection for cable-stayed bridges based on learning of response-only data , 2022, Journal of Civil Structural Health Monitoring.

[6]  You-liang Ding,et al.  Mechanics-Guided optimization of an LSTM network for Real-Time modeling of Temperature-Induced deflection of a Cable-Stayed bridge , 2021, Engineering Structures.

[7]  Branko Glisic,et al.  Prediction of long-term strain in concrete structure using convolutional neural networks, air temperature and time stamp of measurements , 2021 .

[8]  Dalia Yousri,et al.  Aquila Optimizer: A novel meta-heuristic optimization algorithm , 2021, Comput. Ind. Eng..

[9]  Ming Liu,et al.  Structural health monitoring research under varying temperature condition: a review , 2020 .

[10]  Túlio Nogueira Bittencourt,et al.  Dynamic analysis of the train-bridge system considering the non-linear behaviour of the track-deck interface , 2020 .

[11]  Suresh Chandra Satapathy,et al.  Social group optimization algorithm for civil engineering structural health monitoring , 2020 .

[12]  Yuan Ren,et al.  Thermal response separation for bridge long-term monitoring systems using multi-resolution wavelet-based methodologies , 2020 .

[13]  Tianqi Zhang,et al.  Single-channel blind source separation for vibration signals based on TVF-EMD and improved SCA , 2020, IET Signal Process..

[14]  Xiang Xu,et al.  Anomaly detection for large span bridges during operational phase using structural health monitoring data , 2020, Smart Materials and Structures.

[15]  José C. Riquelme,et al.  Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting , 2020, Applied Sciences.

[16]  Yi-Qing Ni,et al.  A vision-based system for long-distance remote monitoring of dynamic displacement: experimental verification on a supertall structure , 2019 .

[17]  Yunhua Li,et al.  Survey and study on intelligent monitoring and health management for large civil structure , 2019, International Journal of Intelligent Robotics and Applications.

[18]  Hao Wang,et al.  Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model , 2019, Engineering Structures.

[19]  Hossam Faris,et al.  Harris hawks optimization: Algorithm and applications , 2019, Future Gener. Comput. Syst..

[20]  Qiao Huang,et al.  Modeling and Separation of Thermal Effects from Cable-Stayed Bridge Response , 2019, Journal of Bridge Engineering.

[21]  Junjie Li,et al.  Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms , 2019, Adv. Eng. Softw..

[22]  Xiang Xu,et al.  Analysis of Temperature-induced Deflection of Cable-stayed Bridge Based on BP Neural Network , 2019, IOP Conference Series: Earth and Environmental Science.

[23]  Satish Nagarajaiah,et al.  Behavior Analysis and Early Warning of Girder Deflections of a Steel-Truss Arch Railway Bridge under the Effects of Temperature and Trains: Case Study , 2019, Journal of Bridge Engineering.

[24]  Xijun Ye,et al.  An Integrated Machine Learning Algorithm for Separating the Long-Term Deflection Data of Prestressed Concrete Bridges , 2018, Sensors.

[25]  Alain Le Duff,et al.  A model-based approach for statistical assessment of detection and localization performance of guided wave–based imaging techniques , 2018 .

[26]  Yanjie Zhu,et al.  Thermal strain extraction methodologies for bridge structural condition assessment , 2018, Smart Materials and Structures.

[27]  Yi-Qing Ni,et al.  Bayesian multi-task learning methodology for reconstruction of structural health monitoring data , 2018, Structural Health Monitoring.

[28]  Duan Dengping,et al.  Strain measurement errors with digital image correlation due to the Savitzky–Golay filter-based method , 2018, Measurement Science and Technology.

[29]  Yi Zhou,et al.  Insights into temperature effects on structural deformation of a cable-stayed bridge based on structural health monitoring , 2018, Structural Health Monitoring.

[30]  Ge Zhang,et al.  Analysis of structural responses of bridges based on long-term structural health monitoring , 2018 .

[31]  Prakash Kripakaran,et al.  Data-driven approaches for measurement interpretation: analysing integrated thermal and vehicular response in bridge structural health monitoring , 2017, Adv. Eng. Informatics.

[32]  Zilong Zou,et al.  Utilization of structural health monitoring in long‐span bridges: Case studies , 2017 .

[33]  James M. W. Brownjohn,et al.  Operational deformations in long-span bridges , 2015, Design, Assessment, Monitoring and Maintenance of Bridges and Infrastructure Networks.

[34]  Franklin Moon,et al.  Temperature-based structural health monitoring baseline for long-span bridges , 2015 .

[35]  Prakash Kripakaran,et al.  Predicting thermal response of bridges using regression models derived from measurement histories , 2014 .

[36]  James M. W. Brownjohn,et al.  Long-term monitoring and data analysis of the Tamar Bridge , 2013 .

[37]  Z. R. Lu,et al.  Identification of both structural damages in bridge deck and vehicular parameters using measured dynamic responses , 2011 .

[38]  Dan M. Frangopol,et al.  Structural Health Monitoring and Reliability Estimation: Long Span Truss Bridge Application With Environmental Monitoring Data , 2008 .

[39]  H. Hao,et al.  Long term vibration monitoring of an RC slab: Temperature and humidity effect , 2006 .

[40]  Ian N. Robertson,et al.  Prediction of vertical deflections for a long-span prestressed concrete bridge structure , 2005 .

[41]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[42]  Zhou Xiao-jun A NEW METHOD TO SEPARATE TEMPERATURE EFFECT FROM LONG-TERM STRUCTURAL HEALTH MONITORING DATA , 2010 .