A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Safety risk evaluations of deep foundation construction schemes based on imbalanced data sets
Safety risk evaluations of deep foundation construction schemes are important to ensure safety. However, the amount of knowledge on these evaluations is large, and the historical data of deep foundation engineering is imbalanced. Some adverse factors influence the quality and efficiency of evaluations using traditional manual evaluation tools. Machine learning guarantees the quality of imbalanced data classifications. In this study, three strategies are proposed to improve the classification accuracy of imbalanced data sets. First, data set information redundancy is reduced using a binary particle swarm optimization algorithm. Then, a classification algorithm is modified using an Adaboost-enhanced support vector machine classifier. Finally, a new classification evaluation standard, namely, the area under the ROC curve, is adopted to ensure the classifier to be impartial to the minority. A transverse comparison experiment using multiple classification algorithms shows that the proposed integrated classification algorithm can overcome difficulties associated with correctly classifying minority samples in imbalanced data sets. The algorithm can also improve construction safety management evaluations, relieve the pressure from the lack of experienced experts accompanying rapid infrastructure construction, and facilitate knowledge reuse in the field of architecture, engineering, and construction.
Safety risk evaluations of deep foundation construction schemes based on imbalanced data sets
Safety risk evaluations of deep foundation construction schemes are important to ensure safety. However, the amount of knowledge on these evaluations is large, and the historical data of deep foundation engineering is imbalanced. Some adverse factors influence the quality and efficiency of evaluations using traditional manual evaluation tools. Machine learning guarantees the quality of imbalanced data classifications. In this study, three strategies are proposed to improve the classification accuracy of imbalanced data sets. First, data set information redundancy is reduced using a binary particle swarm optimization algorithm. Then, a classification algorithm is modified using an Adaboost-enhanced support vector machine classifier. Finally, a new classification evaluation standard, namely, the area under the ROC curve, is adopted to ensure the classifier to be impartial to the minority. A transverse comparison experiment using multiple classification algorithms shows that the proposed integrated classification algorithm can overcome difficulties associated with correctly classifying minority samples in imbalanced data sets. The algorithm can also improve construction safety management evaluations, relieve the pressure from the lack of experienced experts accompanying rapid infrastructure construction, and facilitate knowledge reuse in the field of architecture, engineering, and construction.
Safety risk evaluations of deep foundation construction schemes based on imbalanced data sets
Gong, Peisong (author) / Guo, Haixiang (author) / Huang, Yuanyue (author) / Guo, Shengyu (author)
2020-04-20
doi:10.3846/jcem.2020.12321
Journal of Civil Engineering and Management; Vol 26 No 4 (2020); 380-395 ; 1822-3605 ; 1392-3730
Article (Journal)
Electronic Resource
English
Safety risk evaluations of deep foundation construction schemes based on imbalanced data sets
| BASE | 2020
Safety risk evaluations of deep foundation construction schemes based on imbalanced data sets
| DOAJ | 2020
Deep foundation pit construction safety risk assessment method and system
| European Patent Office | 2020
Deep foundation pit construction safety risk monitoring method, device, equipment and medium
| European Patent Office | 2021
Safety protection device for deep foundation pit construction
| European Patent Office | 2023