|Table of Contents|

Maximum displacement response analysis of RC slabs under blast load based on machine learning method(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2026年01期
Page:
85-94
Research Field:
智能检测与建造技术专栏
Publishing date:

Info

Title:
Maximum displacement response analysis of RC slabs under blast load based on machine learning method
Author(s):
YU Xiaohui1;  CHEN Yuchen1;  DAI Kuangyu2
1. Guangxi Key Laboratory of Green Building Materials and Construction Industrialization, Guilin University of Technology, Guilin 541004, Guangxi, China; 2. School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China
Keywords:
RC slab;  blast load;  maximum displacement response;  interpretable machine learning;  PSO-XGBoost method
PACS:
TU311
DOI:
10.19815/j.jace.2024.12026
Abstract:
A displacement response database for 491 types of reinforced concrete (RC) slabs under blast loads was established based on the collection of existing experimental and numerical simulation results. Using 10 influencing factors including the length, width, thickness, compressive strength of concrete, yield strength of steel bars, reinforcement ratio, boundary conditions, type of slab, blast distance, and blast equivalent as input parameters, a total of 9 machine learning methods from 3 categories were used to establish prediction models for the maximum displacement response of RC slabs under blast loads. Using interpretable machine learning methods, the established machine learning model was explained through feature importance analysis, single factor partial dependency analysis, and interactive dependency analysis, and the importance of the influencing factors on the maximum displacement response of RC slabs under blast loads was analyzed. The results show that the prediction model based on particle swarm optimizationextreme gradient boosting (PSO XGBoost) has the highest accuracy, and the accuracy is higher than that of the equivalent single degree of freedom model recommended by the standard. Among the considered influencing factors, blast equivalent, blast distance, slab thickness and reinforcement ratio have the most significant impact on the maximum displacement response of RC slabs under blast loads. The blast resistant design of RC slabs should ensure a minimum thickness of 150 mm, a minimum reinforcement ratio of 1.5%, and a concrete strength of 50 MPa.

References:

［1］汪 维,刘光昆,汪 琴,等.四边固支方形钢筋混凝土板抗爆试验研究［J］.兵工学报,2018,39(增1):108 -113. WANG Wei, LIU Guangkun, WANG Qin, et al. Experimental research on four -sides fixed square slabs under blast loading［J］. Acta Armamentarii, 2018, 39(S1): 108 -113.

［2］ MENDONA F B, URGESSA G S, ALMEIDA L E N, et al. Damage diagram of blast test results for determining reinforced concrete slab response for varying scaled distance,concrete strength and reinforcement ratio［J］. Anais da Academia Brasileira de Ciências, 2021, 93: e20200511.?
［3］ TIAN S Z, YAN Q S, DU X L, et al. Experimental and numerical studies on the dynamic response of precast concrete slabs under blast load［J］. Journal of Building Engineering, 2023, 70: 106425.?
［4］ 徐文凤.爆炸荷载作用下钢筋混凝土板的损伤演化规律及动力响应分析［D］.郑州:郑州大学,2022. XU Wenfeng. Damage evolution process and dynamic response analysis of reinforced concrete slabs under blast loading［D］. Zhengzhou: Zhengzhou University, 2022. ［5］ 龚顺风,邓 欢,朱升波,等.近爆作用下钢筋混凝土板动态破坏的数值模拟研究［J］.振动与冲击,2012,31(2):20 -24,100. GONG Shunfeng, DENG Huan, ZHU Shengbo, et al. Numerical simulation for dynamic failure of a reinforced concrete slab under close -in explosion［J］. Journal of Vibration and Shock, 2012, 31(2): 20 -24, 100.
［6］ FELDGUN V R, YANKELEVSKY D Z, KARINSKI Y S. A nonlinear SDOF model for blast response simulation of elastic thin rectangular plates［J］. International Journal of Impact Engineering, 2016, 88: 172 -188.?
［7］ Structures to resist the effects of accidental explosions, with change 2: UFC 3 -340 -02［S］. Washington DC: US Department of Defense, 2008.
［8］ STRUMBELJ E, KONONENKO I .Explaining prediction models and individual predictions with feature contributions［J］. Knowledge and Information Systems, 2014, 41(3): 647 -665.?
［9］ 陈曦泽,贾俊峰,白玉磊,等.基于XGBoost -SHAP的钢管混凝土柱轴向承载力预测模型［J］.浙江大学学报(工学版),2023,57(6):1061 -1070. CHEN Xize, JIA Junfeng, BAI Yulei, et al. Prediction model of axial bearing capacity of concrete -filled steel tube columns based on XGBoost -SHAP［J］. Journal of Zhejiang University (Engineering Science), 2023, 57(6): 1061 -1070.
［10］ 冯德成,吴 刚.混凝土结构基本性能的可解释机器学习建模方法［J］.建筑结构学报,2022,43(4):228 -238. FENG Decheng, WU Gang. Interpretable machine learning -based modeling approach for fundamental properties of concrete structures［J］. Journal of Building Structures, 2022, 43(4): 228 -238.
［11］ HUANG P, DAI K Y, YU X H. Machine learning approach for investigating compressive strength of self -compacting concrete containing supplementary cementitious materials and recycled aggregate［J］. Journal of Building Engineering, 2023, 79: 107904.
［12］ ZHAO C F, ZHU Y F, ZHOU Z H. Machine learning -based approaches for predicting the dynamic response of RC slabs under blast loads［J］. Engineering Structures, 2022, 273: 115104.
［13］ ALMUSTAFA M K, NEHDI M L. Machine learning model for predicting structural response of RC slabs exposed to blast loading［J］. Engineering Structures, 2020, 221: 111109.
［14］ 朱玉富,赵春风,周志航.基于机器学习的钢筋混凝土板在爆炸作用下的最大位移预测模型［J］.高压物理学报,2023,37(2):92 -106. ZHU Yufu, ZHAO Chunfeng, ZHOU Zhihang. Prediction model of maximum displacement for RC slabs under blast load based on machine learning［J］. Chinese Journal of High Pressure Physics, 2023, 37(2): 92 -106.?
［15］ HUANG J C, KO K M, SHU M H, et al. Application and comparison of several machine learning algorithms and their integration models in regression problems［J］. Neural Computing and Applications, 2020, 32(10): 5461 -5469.
［16］ AFSHAR A, NOURI G, GHAZVINEH S, et al. Machine -learning applications in structural response prediction: a review［J］. Practice Periodical on Structural Design and Construction, 2024, 29(3): 03124002.
［17］ BREIMAN L. Random forests［J］. Machine Learning, 2001, 45(1): 5 -32.
［18］ JIANG R, JIANG L, HU Y, et al. A simplified method for estimating the fundamental period of masonry infilled reinforced concrete frames［J］. Structural Engineering and Mechanics, 2020, 74(6): 821 -832.
［19］FENG D C , WANG W J , MANGALATHU S, et al. Interpretable XGBoost -SHAP machine -learning model for shear strength prediction of squat RC walls［J］. Journal of structural engineering, 2021, 147(11): 04021173.
［20］ ELBELTAGI E, HEGAZY T, GRIERSON D. Comparison among five evolutionary -based optimization algorithms［J］. Advanced Engineering Informatics, 2005, 19(1): 43 -53.
［21］ LUNDBERG S M, ERION G, CHEN H, et al. From local explanations to global understanding with explainable AI for trees［J］. Nature Machine Intelligence, 2020, 2(1): 56 -67.
［22］ CHICCO D, WARRENS M J, JURMAN G. The coefficient of determination R -squared is more informative than SMAPE,MAE,MAPE,MSE and RMSE in regression analysis evaluation［J］. PeerJ Computer Science, 2021, 7: e623.
［23］ PENG Q, ZHOU D Y, WU H, et al. Experimental and numerical studies on dynamic behaviors of RC slabs under long -duration near -planar explosion loadings［J］. International Journal of Impact Engineering, 2022, 160: 104085.
［24］ MENDONCA F B, URGESSA G, IHA K, et al. Comparison of predicted and experimental behaviour of RC slabs subjected to blast using SDOF analysis［J］. Defence Science Journal, 2018, 68(2): 138.
［25］ JONES J, WU C, OEHLERS D J, et al. Finite difference analysis of simply supported RC slabs for blast loadings［J］. Engineering Structures, 2009, 31(12): 2825 -2832.

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Last Update: 2026-01-20