Health assessment of the wharf based on evidential reasoning rule considering optimal sensor placement

Abstract To achieve an accurate structural health assessment, data collected by multiple sensors needs to be fused effectively. When the number of sensors is limited, it is necessary to determine the reasonable position of sensors and adopt the adequate fusion method. Therefore, a structural health assessment method based on evidential reasoning (ER) rule considering the optimal sensor placement (OSP) is proposed in this paper. In particularly, the discrete integer coding covariance matrix adaptive evolution strategy (D-CMAES) algorithm is developed to determine the scheme of OSP based on the finite element modal analysis (FEMA). Furthermore, in order to select adequate sensors whose data will be fused by the ER rule, a strategy for determining the weight of the ER rule is proposed according to the perception probability. The effectiveness of the proposed method is verified by a case study about the health assessment of the LNG wharf in Hainan, China.

[1]  Byung Kwan Oh,et al.  Optimal architecture of a convolutional neural network to estimate structural responses for safety evaluation of the structures , 2021 .

[2]  Yi-Qing Ni,et al.  A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data , 2020 .

[3]  Ernest Bernat,et al.  Aided Diagnosis of Structural Pathologies with an Expert System , 2013 .

[4]  Ye Xia,et al.  Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection , 2020 .

[5]  Yi Zhang,et al.  Long-term bridge health monitoring and performance assessment based on a Bayesian approach , 2018 .

[6]  Jian-Bo Yang,et al.  On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[7]  Mashrur Chowdhury,et al.  Integration of Structural Health Monitoring and Intelligent Transportation Systems for Bridge Condition Assessment: Current Status and Future Direction , 2016, IEEE Transactions on Intelligent Transportation Systems.

[8]  Dong-Ling Xu,et al.  Evidential reasoning rule for evidence combination , 2013, Artif. Intell..

[9]  Charles R. Farrar,et al.  A probabilistic risk-based decision framework for structural health monitoring , 2021, Mechanical Systems and Signal Processing.

[10]  Chang-Hua Hu,et al.  A New Evidential Reasoning-Based Method for Online Safety Assessment of Complex Systems , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[11]  Xiaoxiang Han.  Mobile Node Deployment based on Improved Probability Model and Dynamic Particle Swarm Algorithm , 2014, J. Networks.

[12]  Suseela Alla,et al.  Integrated methodology of structural health monitoring for civil structures , 2020 .

[13]  Peter Walley,et al.  Measures of Uncertainty in Expert Systems , 1996, Artif. Intell..

[14]  Ying Huang,et al.  Sensor optimization using a genetic algorithm for structural health monitoring in harsh environments , 2016 .

[15]  Chang-Hua Hu,et al.  A New Evidential Reasoning Rule-Based Safety Assessment Method With Sensor Reliability for Complex Systems , 2020, IEEE Transactions on Cybernetics.

[16]  Miriam A. M. Capretz,et al.  A systematic review of convolutional neural network-based structural condition assessment techniques , 2021 .

[17]  Qi Li,et al.  SHM-based F-AHP bridge rating system with application to Tsing Ma Bridge , 2011 .

[18]  Ayan Sadhu,et al.  A literature review of next‐generation smart sensing technology in structural health monitoring , 2019, Structural Control and Health Monitoring.

[19]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[20]  Brinda Chanv,et al.  Structural health monitoring system using IOT and wireless technologies , 2017, 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT).

[21]  Jian-Bo Yang,et al.  Environmental impact assessment using the evidential reasoning approach , 2006, Eur. J. Oper. Res..

[22]  Jian-Bo Yang,et al.  Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties , 2001, Eur. J. Oper. Res..

[23]  Jianchun Xing,et al.  A New Optimal Sensor Placement Strategy Based on Modified Modal Assurance Criterion and Improved Adaptive Genetic Algorithm for Structural Health Monitoring , 2015 .

[24]  Clark R. Dohrmann,et al.  A modal test design strategy for model correlation , 1994 .

[25]  Joseph L. Rose,et al.  Ultrasonic Sensor Placement Optimization in Structural Health Monitoring Using Evolutionary Strategy , 2006 .

[26]  Fan Zhang,et al.  Dynamically Optimized Sensor Deployment Based on Game Theory , 2018, J. Syst. Sci. Complex..

[27]  Xu Bo,et al.  Comprehensive evaluation methods for dam service status , 2012 .

[28]  Hashem Shariatmadar,et al.  Condition Assessment of Civil Structures for Structural Health Monitoring Using Supervised Learning Classification Methods , 2020 .

[29]  Aamar Danish,et al.  Health assessment based on dynamic characteristics of reinforced concrete beam using realtime wireless structural health monitoring sensor , 2020 .

[30]  Hamed Ahmadzade,et al.  Convergence in Distribution for Uncertain Random Sequences with Dependent Random Variables , 2020, Journal of Systems Science and Complexity.

[31]  Peng Pan,et al.  Rapid Structural Safety Assessment Using a Deep Neural Network , 2020, Journal of Earthquake Engineering.

[32]  Carlos Ferregut,et al.  Toward an expert system for damage assessment of structural concrete elements , 1995, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.