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Life Cycle Assessment of Bridges Using Bayesian Networks and Fuzzy Mathematics

1
Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 Valencia, Spain
2
Civil and Environmental Engineering Graduate Program (PPGEng), University of Passo Fundo, Passo Fundo CEP 99052-900, Brazil
*
Author to whom correspondence should be addressed.
Academic Editors: Montserrat Zamorano, Javier Ordóñez and Raffaele Marotta
Appl. Sci. 2021, 11(11), 4916; https://doi.org/10.3390/app11114916
Received: 26 April 2021 / Revised: 14 May 2021 / Accepted: 20 May 2021 / Published: 27 May 2021
At present, reducing the impact of the construction industry on the environment is the key to achieving sustainable development. Countries all over the world are using software systems for bridge environmental impact assessment. However, due to the complexity and discreteness of environmental factors in the construction industry, they are difficult to update and determine quickly, and there is a phenomenon of data missing in the database. Most of the lost data are optimized by Monte Carlo simulation, which greatly reduces the reliability and accuracy of the research results. This paper uses Bayesian advanced fuzzy mathematics theory to solve this problem. In the research, a Bayesian fuzzy mathematics evaluation and a multi-level sensitivity priority discrimination model are established, and the weights and membership degrees of influencing factors were defined to achieve comprehensive coverage of influencing factors. With the support of theoretical modelling, software analysis and fuzzy mathematics theory are used to comprehensively evaluate all the influencing factors of the five influencing stages in the entire life cycle of the bridge structure. The results show that the material manufacturing, maintenance, and operation of the bridge still produce environmental pollution; the main source of the emissions exceeds 53% of the total emissions. The effective impact factor reaches 3.01. At the end of the article, a big data sensitivity model was established. Through big data innovation and optimization analysis, traffic pollution emissions were reduced by 330 tonnes. Modeling of the comprehensive research model; application; clearly confirms the effectiveness and practicality of the Bayesian network fuzzy number comprehensive evaluation model in dealing with uncertain factors in the evaluation of the sustainable development of the construction industry. The research results have made important contributions to the realization of the sustainable development goals of the construction industry. View Full-Text
Keywords: construction industry; environmental; impact factor; analysis; contribution construction industry; environmental; impact factor; analysis; contribution
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MDPI and ACS Style

Zhou, Z.-W.; Alcalá, J.; Kripka, M.; Yepes, V. Life Cycle Assessment of Bridges Using Bayesian Networks and Fuzzy Mathematics. Appl. Sci. 2021, 11, 4916. https://doi.org/10.3390/app11114916

AMA Style

Zhou Z-W, Alcalá J, Kripka M, Yepes V. Life Cycle Assessment of Bridges Using Bayesian Networks and Fuzzy Mathematics. Applied Sciences. 2021; 11(11):4916. https://doi.org/10.3390/app11114916

Chicago/Turabian Style

Zhou, Zhi-Wu; Alcalá, Julián; Kripka, Moacir; Yepes, Víctor. 2021. "Life Cycle Assessment of Bridges Using Bayesian Networks and Fuzzy Mathematics" Appl. Sci. 11, no. 11: 4916. https://doi.org/10.3390/app11114916

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