Safety risk evaluations of deep foundation construction schemes based on imbalanced data sets

Journal of Civil Engineering and Management

Haixiang Guo¹  Peisong Gong²  Shengyu Guo²  Yuanyue Huang² 

[1] Mineral Resource Strategy and Policy Research Center, China University of Geosciences (WUHAN), Wuhan, China;School of Economics and Management, China University of Geosciences (WUHAN), Wuhan, China;

关键词: safety risk evaluation;    construction scheme;    deep foundation;    imbalanced data set;    ensemble learning algorithm;    machine learning;   

DOI  :  10.3846/jcem.2020.12321

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown