A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Spatial-Temporal Deterioration of Multiple Highway Assets: A Correlational Study
Highway infrastructure systems include a variety of assets: pavements, bridges, shoulders, culverts, signs, and pavement markings. Neighboring assets that are under the identical environment might be relatively deteriorated; therefore, there might be a correlation between the condition of neighboring assets. Additionally, the condition of each asset might impact that of neighboring assets. Most studies on deterioration prediction only focused on one asset type and investigated its deterioration independently. Therefore, the possible inter-relation between the condition of multiple neighboring assets and their mutual impacts are often ignored in the past research studies. We implemented an exploratory analysis to investigate the possible spatial and temporal correlation of the condition of multiple asset items for a case study of roadway in Virginia. We considered a range of asset classes in our study, including pavements, shoulders, and ditches. The dataset consists of inspection points during four years of inspection records. In our analysis. we found the locations with the highest probability of defects in multiple asset items. Then, we investigated the dependency between the density of defects in adjacent assets. The results indicate the spatial and temporal inter-relation and correlation between the condition of different asset types. By investigating weather, traffic, and maintenance history at susceptible locations to degradation, the results of this study can provide insight into the recognition of main factors in the deterioration of multiple assets. In addition, modeling the mutual impacts of different assets helps to improve the robustness of condition prediction approaches.
Spatial-Temporal Deterioration of Multiple Highway Assets: A Correlational Study
Highway infrastructure systems include a variety of assets: pavements, bridges, shoulders, culverts, signs, and pavement markings. Neighboring assets that are under the identical environment might be relatively deteriorated; therefore, there might be a correlation between the condition of neighboring assets. Additionally, the condition of each asset might impact that of neighboring assets. Most studies on deterioration prediction only focused on one asset type and investigated its deterioration independently. Therefore, the possible inter-relation between the condition of multiple neighboring assets and their mutual impacts are often ignored in the past research studies. We implemented an exploratory analysis to investigate the possible spatial and temporal correlation of the condition of multiple asset items for a case study of roadway in Virginia. We considered a range of asset classes in our study, including pavements, shoulders, and ditches. The dataset consists of inspection points during four years of inspection records. In our analysis. we found the locations with the highest probability of defects in multiple asset items. Then, we investigated the dependency between the density of defects in adjacent assets. The results indicate the spatial and temporal inter-relation and correlation between the condition of different asset types. By investigating weather, traffic, and maintenance history at susceptible locations to degradation, the results of this study can provide insight into the recognition of main factors in the deterioration of multiple assets. In addition, modeling the mutual impacts of different assets helps to improve the robustness of condition prediction approaches.
Spatial-Temporal Deterioration of Multiple Highway Assets: A Correlational Study
Karimzadeh, Arash (author) / Sabeti, Sepehr (author) / Burde, Adrian (author) / Tabkhi, Hamed (author) / Shoghli, Omidreza (author)
Construction Research Congress 2020 ; 2020 ; Tempe, Arizona
Construction Research Congress 2020 ; 741-750
2020-11-09
Conference paper
Electronic Resource
English
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