A public infrastructure management system should be able to identify the physical condition of infrastructure assets continuously for maintenance and repairs at the right time. However, it is a daunting task to timely monitor numerous existing infrastructure assets with limited monitoring resources. As an alternative data collection method, participatory sensing has increasingly gained attention to collect large amounts of data over large areas with little incremental costs based on citizen's mobile devices. However, it is hard for citizens to assess the condition of infrastructure assets in detail due to the lack of expertise and subjectivity in manual assessment. To address these issues, this study investigates an infrastructure condition assessment method using fuzzy inference to facilitate the evaluation for infrastructure assets by citizens. Fuzzy inference, founded on fuzzy sets and logic, is adopted to emulate the expert reasoning process of evaluating the condition of infrastructure assets. To validate the proposed method, case studies were conducted to evaluate utility poles' condition in urban areas. The fuzzy inference system was able to reduce the condition evaluation error by 20.57%.
|Title of host publication||Construction Research Congress 2020|
|Subtitle of host publication||Computer Applications - Selected Papers from the Construction Research Congress 2020|
|Editors||Pingbo Tang, David Grau, Mounir El Asmar|
|Publisher||American Society of Civil Engineers (ASCE)|
|Number of pages||7|
|Publication status||Published - 2020|
|Event||Construction Research Congress 2020: Computer Applications - Tempe, United States|
Duration: 2020 Mar 8 → 2020 Mar 10
|Name||Construction Research Congress 2020: Computer Applications - Selected Papers from the Construction Research Congress 2020|
|Conference||Construction Research Congress 2020: Computer Applications|
|Period||20/3/8 → 20/3/10|
Bibliographical noteFunding Information:
This study is supported by the research fund from the Department of Civil and Environmental Engineering and Engineering Mechanics at the University of Dayton.
© 2020 American Society of Civil Engineers.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Building and Construction