A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Learning from Class-Imbalanced Bridge and Weather Data for Supporting Bridge Deterioration Prediction
Abstract Evaluating the impact of learning from weather data, in addition to bridge data, on the performance of bridge deterioration prediction is critical for identifying the right data needed for better prediction for enhanced bridge maintenance decision making. However, the majority of the studies in the bridge domain did not consider such evaluation. For those that conducted the evaluation, their evaluation results usually varied. There is, thus, a need for re-evaluating whether the use of weather data could improve the prediction performance. However, conducting the evaluation is challenging because of class imbalance problems in the bridge domain. Therefore, prior to the evaluation, conducting data sampling to alleviate/eliminate such problems is necessary. To address these needs, this paper offers a pilot evaluation study for better evaluating the impact of learning from weather data on bridge deterioration prediction. To conduct the evaluation, a sampling method was used to deal with the data imbalance problems, and a deep neural network model was developed to predict the condition ratings of decks, superstructures, and substructures. A number of alternative sampling methods were tested and the prediction performances—with and without weather data—were compared. The preliminary experimental results indicated that: (1) the random over-sampling method outperformed the other alternatives; and (2) the change in the prediction performance after further learning from the weather data was only marginal.
Learning from Class-Imbalanced Bridge and Weather Data for Supporting Bridge Deterioration Prediction
Abstract Evaluating the impact of learning from weather data, in addition to bridge data, on the performance of bridge deterioration prediction is critical for identifying the right data needed for better prediction for enhanced bridge maintenance decision making. However, the majority of the studies in the bridge domain did not consider such evaluation. For those that conducted the evaluation, their evaluation results usually varied. There is, thus, a need for re-evaluating whether the use of weather data could improve the prediction performance. However, conducting the evaluation is challenging because of class imbalance problems in the bridge domain. Therefore, prior to the evaluation, conducting data sampling to alleviate/eliminate such problems is necessary. To address these needs, this paper offers a pilot evaluation study for better evaluating the impact of learning from weather data on bridge deterioration prediction. To conduct the evaluation, a sampling method was used to deal with the data imbalance problems, and a deep neural network model was developed to predict the condition ratings of decks, superstructures, and substructures. A number of alternative sampling methods were tested and the prediction performances—with and without weather data—were compared. The preliminary experimental results indicated that: (1) the random over-sampling method outperformed the other alternatives; and (2) the change in the prediction performance after further learning from the weather data was only marginal.
Learning from Class-Imbalanced Bridge and Weather Data for Supporting Bridge Deterioration Prediction
Liu, Kaijian (author) / El-Gohary, Nora (author)
2018-10-04
8 pages
Article/Chapter (Book)
Electronic Resource
English
Weather and bridge data , Bridge deterioration prediction , Data imbalance problems , Deep neural networks Engineering , Building Construction and Design , Data Mining and Knowledge Discovery , Building Repair and Maintenance , Computer-Aided Engineering (CAD, CAE) and Design , Light Construction, Steel Construction, Timber Construction , Construction Management
| British Library Conference Proceedings | 2021