{"title":"Knowledge-driven stochastic reliable modeling for steel bridge deck condition rating prediction","authors":"Sahar Hasan, E. Elwakil","doi":"10.1080/24705314.2020.1862965","DOIUrl":null,"url":null,"abstract":"ABSTRACT The structurally deficient bridges increased from 6.2% to 7% of total bridges in California state. With this percentage, 7%; California state occupies one of the top states for bridges in “poor„ condition. Steel bridges represent about 11% of its bridge networks, so determining the condition rating objectively instead of subjectively is crucial. This paper aims to help significantly optimize the maintenance process by providing a rational basis for making decisions. This paper has integrated knowledge, stochastic analysis, Regression technique, and modeling to help the highway agencies to make a more reliable decision for future maintenance based on predicted conditions. Stochastic Regression models have been built using a training dataset extracted from the National Bridge Inventory (NBI) database for California State steel bridges, considering structural and operational parameters. A validation test has been performed using a new real dataset to measure observed data's correspondence to the predicted values. The results of Average Validity Percentage (85.6%) and Coefficient of Determination (R2 = 91.5%) show that the models' accuracy, the power, and scalability of integrating the knowledge-driven models are acceptable. The integrated developed models provide the infrastructure authority with actionable insights for smarter planning and maintenance decisions as better future outcomes.","PeriodicalId":43844,"journal":{"name":"Journal of Structural Integrity and Maintenance","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24705314.2020.1862965","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Structural Integrity and Maintenance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/24705314.2020.1862965","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 2
Abstract
ABSTRACT The structurally deficient bridges increased from 6.2% to 7% of total bridges in California state. With this percentage, 7%; California state occupies one of the top states for bridges in “poor„ condition. Steel bridges represent about 11% of its bridge networks, so determining the condition rating objectively instead of subjectively is crucial. This paper aims to help significantly optimize the maintenance process by providing a rational basis for making decisions. This paper has integrated knowledge, stochastic analysis, Regression technique, and modeling to help the highway agencies to make a more reliable decision for future maintenance based on predicted conditions. Stochastic Regression models have been built using a training dataset extracted from the National Bridge Inventory (NBI) database for California State steel bridges, considering structural and operational parameters. A validation test has been performed using a new real dataset to measure observed data's correspondence to the predicted values. The results of Average Validity Percentage (85.6%) and Coefficient of Determination (R2 = 91.5%) show that the models' accuracy, the power, and scalability of integrating the knowledge-driven models are acceptable. The integrated developed models provide the infrastructure authority with actionable insights for smarter planning and maintenance decisions as better future outcomes.