Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring
Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of his...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/23/6894 |
| _version_ | 1797545899900010496 |
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| author | Nicola-Ann Stevens Myra Lydon Adele H. Marshall Su Taylor |
| author_facet | Nicola-Ann Stevens Myra Lydon Adele H. Marshall Su Taylor |
| author_sort | Nicola-Ann Stevens |
| collection | DOAJ |
| description | Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring. |
| first_indexed | 2024-03-10T14:22:27Z |
| format | Article |
| id | doaj.art-874bec22284b4de097efe56de6fae024 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:22:27Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-874bec22284b4de097efe56de6fae0242023-11-20T23:17:41ZengMDPI AGSensors1424-82202020-12-012023689410.3390/s20236894Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health MonitoringNicola-Ann Stevens0Myra Lydon1Adele H. Marshall2Su Taylor3School of Natural and Built Environment, Queen’s University Belfast, David Keir Building, Belfast BT9 5AG, UKSchool of Natural and Built Environment, Queen’s University Belfast, David Keir Building, Belfast BT9 5AG, UKSchool of Mathematics and Physics, Queen’s University Belfast, University Rd, Belfast BT7 1NN, UKSchool of Natural and Built Environment, Queen’s University Belfast, David Keir Building, Belfast BT9 5AG, UKMachine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.https://www.mdpi.com/1424-8220/20/23/6894structural health monitoringbridge management systemssurvival analysisMarkov chains |
| spellingShingle | Nicola-Ann Stevens Myra Lydon Adele H. Marshall Su Taylor Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring Sensors structural health monitoring bridge management systems survival analysis Markov chains |
| title | Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring |
| title_full | Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring |
| title_fullStr | Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring |
| title_full_unstemmed | Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring |
| title_short | Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring |
| title_sort | identification of bridge key performance indicators using survival analysis for future network wide structural health monitoring |
| topic | structural health monitoring bridge management systems survival analysis Markov chains |
| url | https://www.mdpi.com/1424-8220/20/23/6894 |
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