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

    Peisong Gong Affiliation
    ; Haixiang Guo Affiliation
    ; Yuanyue Huang Affiliation
    ; Shengyu Guo Affiliation

Abstract

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.

Keyword : safety risk evaluation, construction scheme, deep foundation, imbalanced data set, ensemble learning algorithm, machine learning

How to Cite
Gong, P., Guo, H., Huang, Y., & Guo, S. (2020). Safety risk evaluations of deep foundation construction schemes based on imbalanced data sets. Journal of Civil Engineering and Management, 26(4), 380-395. https://doi.org/10.3846/jcem.2020.12321
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Apr 20, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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