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A new computational method for structural reliability with Big Data
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School of Mechanical Engineering Guizhou University of Science Engineering Qixingguan, Bijie, 551700, China
School of Information Engineering Guizhou Minzu University Huaxi, Guiyang, 551000, China
School of Engineering University of Greenwich Kent, ME4 4TB, UK
Publication date: 2019-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(1):159–163
A new computational method for structural reliability based on big data is proposed in this paper. Firstly, the big data is collected via structural monitoring and is analyzed. The big data is then classified into different groups according to the regularities of distribution of the data. In this paper, the stress responses of a suspension bridge due to different types of vehicle are obtained. Secondly, structural reliability prediction model is established using the stress-strength interference theory under the repeated loads after the stress responses and structural strength have been comprehensively considered. In addition, structural reliability index is calculated using the first order second moment method under vehicle loads that are obeying the normal distribution. The minimum reliability among various types of stress responses is chosen as the structural reliability. Finally, the proposed method has been validated for its feasibility and effectiveness by an example.
Al-kahtani M S, Karim L. An efficient distributed algorithm for big data processing. Computer Engineering and Computer Science 2017; 42: 3149–3157,
Bellahcene T, Aberkane M. Estimation of fracture toughness of cast steel container from Charpy impact test data. Steel and Composite Structures 2017; 25(6): 639-648.
Chen Q. Evaluation and analysis of expressway traffic operation state driven by big data. 2016; Ph.D. Dissertation, Changan University, Xi'an.
Ekpiwhre E O, Tee K F, Aghagba S A, Bishop K. Risk-based inspection on highway assets with category 2 defects. International Journal of Safety and Security Engineering 2016; 6(2): 372-382,
Fang Y, Xiong J, Tee K F. An iterative hybrid random-interval structural reliability analysis. Earthquakes and Structures 2014; 7(6): 1061- 1070,
Fang Y, Xiong J, Tee K F. Time-variant structural fuzzy reliability analysis under stochastic loads applied several times. Structural Engineering and Mechanics 2015; 55(3): 525-534,
Fang Y, Wen L, Tee K F. Reliability analysis of repairable k-out-n system from time response under several times stochastic shocks. Smart Structures and Systems 2014; 14(4): 559-567,
He X, Shi W, Li W, et al. Reliability enhancement of power electronics systems by big data science. Proceeding of the CSEE 2017; 37(1): 209-221.
Jocic M, Pap E, Szakál A, Obradovic D, Konjovic Z. Managing big data using fuzzy sets by directed graph node similarity. Acta Polytechnica Hungarica 2017; 14(2): 183-200.
Kim Y J, Queiroz L B. Big data for condition evaluation of constructed bridges. Structures Engineering 2017; 141: 217–227, https://doi. org/10.1016/j.engstruct.2017.03.028.
Koseleva N, Ropaite G. Big data in building energy efficiency: understanding of big data and main challenges. Procedia Engineering 2017; 172: 544 – 549,
Ma R, Xu S, Wang D, et al. Big data based fatigue life analysis of steel box girder in large-span suspension. Journal of South China University of Technology 2017; 45(6): 66-73.
Mahmoodian M, Alani A M, Tee K F. Stochastic failure analysis of the gusset plates in the Mississippi river bridge. International Journal of Forensic Engineering 2012; 1(2): 153-166,
Mandawat A, Williams A E, Francis S A. Cardio-oncology the role of big data. Heart Failure Clin, 2017; 17: 403–408, hfc.2016.12.010.
Ouyang Q, Wu C, Huang L. Research on basic principles of applications of big data in field of safety science. China Safety Science Journal 2016; 26(11): 13-18.
Pehlivan H. Frequency analysis of GPS data for structural health monitoring observations. Structural Engineering and Mechanics 2018; 66 (2): 185-193.
Ponzo F C, Ditommaso R, Auletta G, Mossucca A. A fast method for structural health monitoring of Italian reinforced concrete strategic buildings. Bull Earthquake Engineering 2010; 8: 1421–1434,
Reder M, Yurusun NY, Melero J J. Data-driven learning framework for associating weather conditions and wind turbine failures. Reliability Engineering and System Safety 2018; 169(4): 554-569,
Rosà A, Chen L Y, Binder W. Failure analysis and prediction for Big-Data systems. IEEE Transactions on Services Computing 2017; 10(6): 983-998,
Storey V C, Song II-Y. Big data technologies and management: What conceptual modeling can do. Data & Knowledge Engineering 2017; 108: 50–67,
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