S. Clonts, Lia Cooley, P. Freitag, Bryan R. Soltis
{"title":"弗吉尼亚大桥老化因素","authors":"S. Clonts, Lia Cooley, P. Freitag, Bryan R. Soltis","doi":"10.1109/SIEDS.2019.8735618","DOIUrl":null,"url":null,"abstract":"Over four thousand bridges in the Virginia Department of Transportation's (VDOT) inventory are structurally deficient or obsolete. This project aimed to determine relevant bride deterioration factors in Virginia while providing a historical overview of Virginia's bridge ratings and deterioration rates. The team specifically analyzed the differences in the impact of deterioration factors by Virginia districts and bridge structure types. Using VDOT's inspection data from their Bridge Resource Management (BrM) system, we analyzed Virginia responsible bridge inspections from 2000–2015. Then we worked with a team from VDOT's Structure and Bridge Division to establish the most important factors that constitute an inspection record. We used a random forest algorithm to determine variable importance and relationships between important variables. We created 27 models in total which determined the relative influence of bridge-specific and environmental factors on bridges' overall condition ratings, as well as the bridge component condition ratings. Our models gave us an understanding of the relative importance of all factors analyzed across all bridge types. With 28 variables, the full model was 84.6% accurate on the test set. Our team further analyzed how ratings differ by district and bridge structure type. District trends were especially important to understand overall state consistency. Results confirmed factors such as bridge age, daily traffic, and relative location were influential in determining condition ratings between different districts and structure types. Limitations in analysis include inaccurate data for inspection ratings and bridge characteristics. Analysis is also ongoing, limiting the current definitive conclusions we can propose.","PeriodicalId":265421,"journal":{"name":"2019 Systems and Information Engineering Design Symposium (SIEDS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Virginia Bridge Deterioration Factors\",\"authors\":\"S. Clonts, Lia Cooley, P. Freitag, Bryan R. Soltis\",\"doi\":\"10.1109/SIEDS.2019.8735618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Over four thousand bridges in the Virginia Department of Transportation's (VDOT) inventory are structurally deficient or obsolete. This project aimed to determine relevant bride deterioration factors in Virginia while providing a historical overview of Virginia's bridge ratings and deterioration rates. The team specifically analyzed the differences in the impact of deterioration factors by Virginia districts and bridge structure types. Using VDOT's inspection data from their Bridge Resource Management (BrM) system, we analyzed Virginia responsible bridge inspections from 2000–2015. Then we worked with a team from VDOT's Structure and Bridge Division to establish the most important factors that constitute an inspection record. We used a random forest algorithm to determine variable importance and relationships between important variables. We created 27 models in total which determined the relative influence of bridge-specific and environmental factors on bridges' overall condition ratings, as well as the bridge component condition ratings. Our models gave us an understanding of the relative importance of all factors analyzed across all bridge types. With 28 variables, the full model was 84.6% accurate on the test set. Our team further analyzed how ratings differ by district and bridge structure type. District trends were especially important to understand overall state consistency. Results confirmed factors such as bridge age, daily traffic, and relative location were influential in determining condition ratings between different districts and structure types. Limitations in analysis include inaccurate data for inspection ratings and bridge characteristics. Analysis is also ongoing, limiting the current definitive conclusions we can propose.\",\"PeriodicalId\":265421,\"journal\":{\"name\":\"2019 Systems and Information Engineering Design Symposium (SIEDS)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Systems and Information Engineering Design Symposium (SIEDS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIEDS.2019.8735618\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Systems and Information Engineering Design Symposium (SIEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIEDS.2019.8735618","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Over four thousand bridges in the Virginia Department of Transportation's (VDOT) inventory are structurally deficient or obsolete. This project aimed to determine relevant bride deterioration factors in Virginia while providing a historical overview of Virginia's bridge ratings and deterioration rates. The team specifically analyzed the differences in the impact of deterioration factors by Virginia districts and bridge structure types. Using VDOT's inspection data from their Bridge Resource Management (BrM) system, we analyzed Virginia responsible bridge inspections from 2000–2015. Then we worked with a team from VDOT's Structure and Bridge Division to establish the most important factors that constitute an inspection record. We used a random forest algorithm to determine variable importance and relationships between important variables. We created 27 models in total which determined the relative influence of bridge-specific and environmental factors on bridges' overall condition ratings, as well as the bridge component condition ratings. Our models gave us an understanding of the relative importance of all factors analyzed across all bridge types. With 28 variables, the full model was 84.6% accurate on the test set. Our team further analyzed how ratings differ by district and bridge structure type. District trends were especially important to understand overall state consistency. Results confirmed factors such as bridge age, daily traffic, and relative location were influential in determining condition ratings between different districts and structure types. Limitations in analysis include inaccurate data for inspection ratings and bridge characteristics. Analysis is also ongoing, limiting the current definitive conclusions we can propose.