Environmental Effects in Life Cycle Assessment of Machine-Vision-Driven Spall Repair Material Estimation for Sustainable Road Maintenance
Portland cement concrete is widely used in road construction due to its durability and minimal maintenance needs. However, its susceptibility to spall highlights the drawbacks of conventional repair methods, including cost inefficiencies, delays, environmental impacts, and safety risks from road clo...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/2/162 |
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| author | Junhwi Cho Shanelle Aira Rodrigazo Hwang-Hee Kim Su-Jin Lee Chan Gi Park Jaeheum Yeon |
| author_facet | Junhwi Cho Shanelle Aira Rodrigazo Hwang-Hee Kim Su-Jin Lee Chan Gi Park Jaeheum Yeon |
| author_sort | Junhwi Cho |
| collection | DOAJ |
| description | Portland cement concrete is widely used in road construction due to its durability and minimal maintenance needs. However, its susceptibility to spall highlights the drawbacks of conventional repair methods, including cost inefficiencies, delays, environmental impacts, and safety risks from road closures. To address these challenges, this study evaluated the environmental benefits of a spall detection and repair method employing artificial-intelligence-based computer vision technology. By utilizing machine vision techniques, this approach detects spall damage without road closures and automates the calculation of repair areas and material requirements through a proprietary estimation program. Environmental impact assessments were conducted using life cycle assessment across three frameworks, TRACI, ReCiPe, and ILCD, to compare this method with conventional practices. The results revealed a 79% reduction in the overall environmental impacts, including significant decreases in global warming due to shorter road closures and reduced material waste. Resource usage improved through optimized processes, and air pollution decreased, with lower emissions of smog and particulates. This study highlights the potential of machine-vision-driven repair material quantity takeoff as a more efficient and sustainable alternative. The results of this study will help institutional engineers and practitioners adopt sustainable strategies for green infrastructure repair and integrate them into various infrastructure maintenance practices to contribute to the development of sustainable urban environments. |
| format | Article |
| id | doaj-art-1d2d5c3b29354844999584dc6d40f6c4 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-1d2d5c3b29354844999584dc6d40f6c42025-01-24T13:26:00ZengMDPI AGBuildings2075-53092025-01-0115216210.3390/buildings15020162Environmental Effects in Life Cycle Assessment of Machine-Vision-Driven Spall Repair Material Estimation for Sustainable Road MaintenanceJunhwi Cho0Shanelle Aira Rodrigazo1Hwang-Hee Kim2Su-Jin Lee3Chan Gi Park4Jaeheum Yeon5Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaResearch Center, Contecheng Co., Ltd., Yongin 16942, Republic of KoreaDepartment of Architectural Engineering, Keimyung University, Daegu 42601, Republic of KoreaDepartment of Regional Construction Engineering, Kongju National University, Yesan 32439, Republic of KoreaDepartment of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaPortland cement concrete is widely used in road construction due to its durability and minimal maintenance needs. However, its susceptibility to spall highlights the drawbacks of conventional repair methods, including cost inefficiencies, delays, environmental impacts, and safety risks from road closures. To address these challenges, this study evaluated the environmental benefits of a spall detection and repair method employing artificial-intelligence-based computer vision technology. By utilizing machine vision techniques, this approach detects spall damage without road closures and automates the calculation of repair areas and material requirements through a proprietary estimation program. Environmental impact assessments were conducted using life cycle assessment across three frameworks, TRACI, ReCiPe, and ILCD, to compare this method with conventional practices. The results revealed a 79% reduction in the overall environmental impacts, including significant decreases in global warming due to shorter road closures and reduced material waste. Resource usage improved through optimized processes, and air pollution decreased, with lower emissions of smog and particulates. This study highlights the potential of machine-vision-driven repair material quantity takeoff as a more efficient and sustainable alternative. The results of this study will help institutional engineers and practitioners adopt sustainable strategies for green infrastructure repair and integrate them into various infrastructure maintenance practices to contribute to the development of sustainable urban environments.https://www.mdpi.com/2075-5309/15/2/162life cycle assessmentspall repair material quantity takeoffroad maintenancesustainable infrastructureenvironmentally sustainable |
| spellingShingle | Junhwi Cho Shanelle Aira Rodrigazo Hwang-Hee Kim Su-Jin Lee Chan Gi Park Jaeheum Yeon Environmental Effects in Life Cycle Assessment of Machine-Vision-Driven Spall Repair Material Estimation for Sustainable Road Maintenance Buildings life cycle assessment spall repair material quantity takeoff road maintenance sustainable infrastructure environmentally sustainable |
| title | Environmental Effects in Life Cycle Assessment of Machine-Vision-Driven Spall Repair Material Estimation for Sustainable Road Maintenance |
| title_full | Environmental Effects in Life Cycle Assessment of Machine-Vision-Driven Spall Repair Material Estimation for Sustainable Road Maintenance |
| title_fullStr | Environmental Effects in Life Cycle Assessment of Machine-Vision-Driven Spall Repair Material Estimation for Sustainable Road Maintenance |
| title_full_unstemmed | Environmental Effects in Life Cycle Assessment of Machine-Vision-Driven Spall Repair Material Estimation for Sustainable Road Maintenance |
| title_short | Environmental Effects in Life Cycle Assessment of Machine-Vision-Driven Spall Repair Material Estimation for Sustainable Road Maintenance |
| title_sort | environmental effects in life cycle assessment of machine vision driven spall repair material estimation for sustainable road maintenance |
| topic | life cycle assessment spall repair material quantity takeoff road maintenance sustainable infrastructure environmentally sustainable |
| url | https://www.mdpi.com/2075-5309/15/2/162 |
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