Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method

Kyeongjin Kim; WooSeok Kim; Junwon Seo; Yoseok Jeong; Meeju Lee; Jaeha Lee

doi:10.3390/ma15031045

Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method

Materials (Basel). 2022 Jan 28;15(3):1045. doi: 10.3390/ma15031045.

Authors

Kyeongjin Kim¹, WooSeok Kim², Junwon Seo³, Yoseok Jeong⁴, Meeju Lee^{5

6}, Jaeha Lee^{1

6}

Affiliations

¹ Major of Civil Engineering, National Korea Maritime & Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 49112, Korea.
² Department of Civil Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea.
³ Department of Civil and Environmental Engineering, South Dakota State University, Brookings, SD 57007, USA.
⁴ Department of Construction and Disaster Prevention Engineering, Kyungpook National University, 2559 Kyungsangdae-ro, Sangju 37224, Gyeongsangbuk-do, Korea.
⁵ Major of Civil and Environmental Engineering, National Korea Maritime & Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 49112, Korea.
⁶ Interdisciplinary Major of Ocean Renewable Energy Engineering, National Korea Maritime & Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 49112, Korea.

Abstract

In the present study, the amount of fragments generated and their travel distances due to vehicle collision with concrete median barrier (CMB) was analyzed and predicted. In this regard, machine learning was applied to the results of numerical analysis, which were developed by comparing with field test. The numerical model was developed using smoothed particle hydrodynamics (SPH). SPH is a mesh-free method that can be used to predict the amount of fragments and their travel distances from concrete structures under impact loading. In addition, deep neural network (DNN) and gradient boosting machine (GBM) were also employed as machine learning methods. In this study, the results of DNN, GBM, and numerical analysis were then compared with the conducted field test. Such comparisons revealed that numerical analysis generated lower error than both DNN and GBM. When prediction results of both the amount of fragments and their travel distances were considered, the result of DNN showed smaller errors than that of GBM. Therefore, in studies where machine learning is used to predict the amount of fragments and their travel distances, careful selection of an appropriate method from the various available machine learning methods such as DNN, GBM, and random forest is absolutely important.

Keywords: artificial neural network; concrete median barrier; deep neural network; fragments; gradient boosting machine; smoothed particle hydrodynamics; travel distance.

Abstract

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