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

朱玉富; 赵春风; 周志航

doi:10.11858/gywlxb.20220667

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

doi: 10.11858/gywlxb.20220667

合肥工业大学土木与水利工程学院, 安徽合肥　230009

基金项目: 国家重点实验室开放基金（GZ21112）

详细信息

作者简介:
朱玉富（1997－），男，硕士研究生，主要从事机器学习与钢筋混凝土板抗爆分析研究.E-mail: 2020110589@mail.hfut.edu.cn

通讯作者:
赵春风（1983－），男，博士，教授，主要从事工程结构抗震与减震、组合结构抗爆研究.E-mail: zhaowindy@hfut.edu.cn

中图分类号: O383
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出版历程
- 收稿日期: 2022-09-29
- 修回日期: 2022-11-10
- 录用日期: 2022-11-11
- 网络出版日期: 2023-04-23
- 刊出日期: 2023-04-05

Prediction Model of Maximum Displacement for RC Slabsunder Blast Load Based on Machine Learning

School of Civil Engineering, Hefei University of Technology, Hefei 230009, Anhui, China

摘要

摘要: 钢筋混凝土（reinforced concrete，RC）板作为工程结构的主要受力构件，在遭受意外爆炸或恐怖袭击时极易发生破坏，甚至引起结构的整体倒塌，因此，了解和预测混凝土板在爆炸作用下的动力响应，对增强工程结构的抗爆防护能力、减轻生命和财产经济损失具有非常重要的意义。收集整理了国内外文献中普通RC板爆炸试验和基于试验进行参数化分析的数值模拟数据，采用机器学习回归算法中的支持向量机和高斯过程回归两种算法等对近场爆炸作用下RC板的最大位移进行预测；运用改进的偏差-方差分解原理对模型的泛化性能进行分析，同时将机器学习模型与现有的预测方法进行对比；最后，采用置换特征重要性和Sobol全局敏感性分析方法，从局部和整体对模型特征进行解释，增加模型的可靠性。结果表明：支持向量机和高斯过程回归两种机器学习方法的泛化性能都较好，并且高斯过程回归算法的预测效果优于支持向量机算法。对比现有预测方法发现，机器学习方法更优，具有较高的预测精度和计算效率，且得出了不同输入参数对模型输出结果的影响，实现了对输出结果的可解释性，进一步验证了其可靠性。研究结果可为机器学习在爆炸领域的应用提供参考。
- 机器学习 /
- 钢筋混凝土板 /
- 爆炸荷载 /
- Sobol全局敏感性分析 /
- 偏差-方差分解
Abstract: As the main force components of engineering structures, reinforced concrete slab are prone to damage when it is subjected to terrorist attacks or accidental explosions, and even cause the overall collapse of the structure. Therefore, it is of great significance to understand and predict the dynamic response of concrete slab under the action of explosions to enhance the anti-explosion protection ability of engineering structure and reduce the economic loss of life and property. In this paper, the numerical simulation data of ordinary reinforced concrete slab explosion test and parametric analysis based on the test in the literature in China and abroad are collected. The support vector machine and Gaussian process regression algorithms in machine learning regression algorithm are used to predict the maximum displacement of reinforced concrete slab under near-field explosion. The generalization performance of the model is analyzed by using the improved deviation-variance decomposition principle. At the same time, the machine learning model is compared with the existing prediction methods. Finally, the replacement feature importance and Sobol global sensitivity analysis method are used to explain the model from the local and global to increase the reliability of the model. The above results show that the generalization performance of the two machine learning methods is better, but the prediction effect of the Gaussian process regression algorithm is better than that of the support vector machine algorithm. At the same time, compared with the existing prediction methods, it is found that the machine learning method is better, with higher prediction accuracy and computational efficiency. The influence of different input parameters on the output results of the model is obtained, which realizes the interpretability of the output results and further increases its reliability.
- machine learning /
- reinforced concrete slab /
- blast load /
- sobol global sensitivity analysis /
- deviation-variance decomposition

HTML全文

图 1 RC板在爆炸作用下位移预测流程

Figure 1. Displacement prediction process of reinforced concrete slab under explosion

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图 2 爆炸作用下RC板的布置示意图和变量选取示意图

Figure 2. Layout diagram and variable selection diagram of reinforced concrete slab under blast load

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图 3 定量特征和输出变量的统计分布

Figure 3. Statistical distribution of quantitative characteristics and output variables

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图 4 线性支持向量回归机示意图

Figure 4. Diagram of linear support vector regression

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图 5 偏差与方差的图形解释

Figure 5. Graphical illustration of bias and variance

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图 6 不同比例样本训练集的偏差-标准差堆叠图

Figure 6. Bias-standard deviation stack graph of samples with different training set proportions

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图 7 不同算法的预测值与试验值对比：(a) GPR，(b) SVR

Figure 7. Comparison of predicted values and experimental values of different algorithms: (a) GPR, (b) SVR

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图 8 两种算法的预测响应与残差：(a) GPR，(b) SVR

Figure 8. Prediction response and residual of two algorithms: (a) GPR, (b) SVR

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图 9 GPR算法排列特征重要性：(a) 特征重要性排序，(b)特征重要性占比

Figure 9. Permutation feature importance of GPR algorithm: (a) feature importance, (b) proportion of feature importance

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图 10 敏感性分析流程

Figure 10. Sensitivity analysis process

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图 11 爆炸作用下RC板的最大位移敏感性分析结果

Figure 11. Maximum displacement sensitivity analysis results of reinforced concrete slab under blast load

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表 1 数据集数值型特征的统计描述

Table 1. Statistical description of numerical feature of data sets

Variance	Feature/Output	Mean/Count	SD	Max	Min	1/4 Q	1/2 Q	3/4 Q
X₁	Length/m	1.66	0.84	6.00	0.75	1.00	1.38	2.00
X₂	Width/m	1.56	0.78	3.00	0.75	1.00	1.20	2.00
X₃	Thickness/m	0.09	0.04	0.20	0.03	0.05	0.10	0.10
X₄	Compressive strength/MPa	37.50	8.39	63.00	20.00	30.00	39.50	40.45
X₅	Steel yield strength/MPa	385.13	95.66	600.00	235.00	335.00	400.00	425.00
X₆	Reinforcement ratio/%	1.14	0.93	6.12	0.20	0.49	0.84	1.34
X₇	Explosion distance/m	0.80	0.84	5.00	0.10	0.40	0.60	0.89
X₈	TNT charge mass/kg	3.27	4.38	20.00	0.01	0.36	1.58	3.42
X₉	Boundary conditions	(B₁)58,(B₂)79,(B₃)5,(B₄)45,(B₅)29,(B₆)14,(B₇)13,(B₈)2,(B₉)20
X₁₀	One-way/Two-way	(One-way slab)98/(Two-way slab)162
Y	Displacement/mm	34.55	31.59	142.00	1.16	12.23	22.45	43.54

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表 2 ML方法与现有方法对比

Table 2. Comparison of ML model with existing methods

Case	Existing method detail	Maximum displecement/mm			Error of existing method/%	Error of ML/%
Case	Existing method detail	Exp.	Existing method	ML	Error of existing method/%	Error of ML/%
1	LS-DYNA-mesh, 5 mm^[20]	25.7	25.10	25.27	2.33	1.67
2	LS-DYNA- mesh, 10 mm^[20]	25.7	35.20	25.27	36.96	1.67
3	LS-DYNA- mesh, 20 mm^[20]	25.7	35.60	25.27	38.52	1.67
4	SDOF^[12]	1.8	2.02	2.66	12.22	47.74
5	SDOF^[12]	10.5	10.51	10.59	0.10	0.90
6	SDOF^[12]	13.9	15.09	12.99	8.56	6.57
7	SDOF^[12]	38.9	37.69	37.80	3.11	2.83
8	Medium-structure interaction theory^[15]	4.8	4.19	5.51	12.71	14.69
9	Medium-structure interaction theory^[15]	8.4	7.37	7.09	12.26	15.60
10	Medium-structure interaction theory^[15]	10.2	11.80	8.67	15.69	14.98
11	LS-DYNA-mesh, 5 mm^[13]	9.0	8.40	8.15	6.67	9.49
12	LS-DYNA-mesh, 5 mm^[13]	23.1	21.30	18.76	7.79	18.79
13	LS-DYNA-mesh, 5 mm^[13]	5.1	5.70	9.78	11.76	91.72
14	LS-DYNA-mesh, 5 mm^[13]	9.9	10.50	10.34	6.06	4.43

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