基于可解释机器学习的锈蚀RC构件抗剪承载力预测模型

doi:10.13229/j.cnki.jdxbgxb.20230024

吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (11): 3231-3243.doi: 10.13229/j.cnki.jdxbgxb.20230024

• 交通运输工程·土木工程 • 上一篇

基于可解释机器学习的锈蚀RC构件抗剪承载力预测模型

戴理朝^1,²(),王冲¹,袁平¹,王磊¹()

^1.长沙理工大学土木工程学院，长沙 410114
^2.长沙理工大学南方地区桥梁长期性能提升技术国家地方联合工程实验室，长沙 410114

收稿日期:2023-01-03 出版日期:2024-11-01 发布日期:2025-04-24
通讯作者: 王磊 E-mail:lizhaod@csust.edu.cn;leiwang@csust.edu.cn
作者简介:戴理朝（1989-），男，副教授，博士. 研究方向：桥梁耐久性.E-mail：lizhaod@csust.edu.cn
基金资助:
国家重点研发计划项目(2021YFB2600900);国家自然科学基金项目(52278140);湖南省自然科学基金项目(2020JJ1006);南方地区桥梁长期性能提升技术国家地方联合工程实验室（长沙理工大学）项目(22KE02);长沙理工大学专业学位研究生实践创新与创业能力提升项目(SJCX202123)

Prediction model for shear capacity of corroded RC beams based on interpretable machine learning

Li-zhao DAI^1,²(),Chong WANG¹,Ping YUAN¹,Lei WANG¹()

^1.School of Civil Engineering，Changsha University of Science & Technology，Changsha 410114，China
^2.National-Local Joint Laboratory of Engineering Technology for Long-term Performance Enhancement of Bridges in Southern District，Changsha University of Science & Technology，Changsha 410114，China

Received:2023-01-03 Online:2024-11-01 Published:2025-04-24
Contact: Lei WANG E-mail:lizhaod@csust.edu.cn;leiwang@csust.edu.cn

摘要/Abstract

摘要：

基于机理方法推导的锈蚀钢筋混凝土（RC）构件抗剪承载力模型通常引入一系列假定与修正系数，导致计算结果精度不高和适用性有限。本文基于数据驱动，考虑黑箱模型的可信度与输入特征的合理性，选择锈蚀RC构件的几何尺寸、纵筋配筋率、箍筋屈服强度、箍筋锈蚀率、混凝土强度等关键基本特征，建立了基于可解释机器学习算法的实用模型。结果表明，所有基本特征中锈蚀率、有效高度、剪跨比与梁宽对抗剪承载力较为敏感；本预测模型阐明了锈蚀RC构件关键基本参数与抗剪承载力间显性映射关系，相较于经验模型与黑箱模型，它具有较高的透明度与预测精度。

关键词: 土木工程, RC构件, 锈蚀, 抗剪承载力, 可解释机器学习, 特征选择

Abstract:

The prediction model for shear capacity of corroded reinforced concrete （RC） beams based on the mechanism method usually introduces a series of assumptions and correction coefficients， resulting in low accuracy of calculation results and limited applicability. In the present study， based on the data drive， considering the reliability of the black box model and the rationality of the input features， the key basic characteristics of the corroded RC beams， such as geometric dimensions， longitudinal reinforcement ratio， stirrup yield strength， stirrup corrosion loss， and concrete strength， were selected. A practical model of shear capacity based on an interpretable machine learning algorithm was established. The results show that the corrosion loss， effective height， shear-to-span ratio and beam width are sensitive to the structural shear capacity. The practical model of shear capacity of corroded RC beams based on the data-driven can reveal the underlying mapping relationship between the basic features and the shear capacity. The proposed model have good applicability and prediction accuracy compared with the empirical model and black box model.

Key words: civil engineering, RC beams, corrosion, shear capacity, interpretability machine learning, feature selection

中图分类号:

TU375.1

戴理朝,王冲,袁平,王磊. 基于可解释机器学习的锈蚀RC构件抗剪承载力预测模型[J]. 吉林大学学报(工学版), 2024, 54(11): 3231-3243.

Li-zhao DAI,Chong WANG,Ping YUAN,Lei WANG. Prediction model for shear capacity of corroded RC beams based on interpretable machine learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(11): 3231-3243.

图/表 12

表1

锈蚀钢筋混凝土梁抗剪试验数据"

学者	$f c$ /MPa	$b$ /mm	$h 0$ /mm	$ρ l$ /mm	$ρ v$ /%	$f y v$ /kN	$A s v$ /mm²	$s$ /mm	$λ$	$η l$ /%	$η w$ /%	$V u$ /kN	加载方式
薛昕等^［3］	15.8~18.8	120	220	2.17	0.39	300	57	120	1.5~3.2	0~17.3	0~16.7	70.5~124.3	单点
柳世涛^［27］	19.5~23.9	200	265	2.15	0.14~0.25	339~524	57~101	150~250	2~3.5	0~12.1	0~60.1	93.8~181.5	双点
Xia等^［28］	12.97	120	200	2.62	0.48~0.56	321~463	57~101	100~150	1.5	0	0~54.2	85.2~138.2	双点
霍飞格^［29］	16.8~17.6	250	475	1.63	0.08~0.13	331~337	43~66	200	1.74	0	0~36.3	205.8~275.6	双点
赵冰等^［30］	9.5	100	175	1.94	0.44	324	66	150	1.5~2.5	0	4~23.3	41.9~53.0	双点
杨晓明等^［31］	15.8~18.7	120	220	2.17	0.39	300	57	120	1.5~3.2	0~17.3	0~16.8	70.5~123.3	双点
李冰等^［32］	18.3~26.7	150	150	2.68	0.19~0.25	444.15	57	150~200	2.0~3.0	0~26.2	0~32.7	52.1~78.2	双点
赵羽习等^［9］	11.9	150	155	2.26~2.79	0.19~0.45	331.52	66~101	100~200	2.2~3.1	0~26.0	0~9.2	40.0~72.0	单点
李学田等^［33］	18.1	150	175	2.3	0.25	275	57	150	1.5~2.2	0~3.4	0~2.8	75.9~131.0	单点
霍艳华^［4］	9.2~12.8	100	175	1.94	0.44	324	66	150	1.5~2.5	0~20.0	3.0~30.8	41.9~53.0	双点
祝建军^［34］	17.9	100	175	2.06	0	0	0	0	1.5~2.5	0~12.5	0	19.3~44.2	双点
Higgins等^［35］	17.6~20.0	254	521	1.90	0.16~0.20	585	101	203~305	2.04	0	0~42.5	324.0~475.2	双点
王小惠等^［36］	17.0	150	170	1.58	0	0	0	0	3.97	1.0~4.7	0	24.4~36.3	双点
徐善华等^［8］	15.1~16.3	120	200	1.92	0.32	275	57	150	1~2	0	0~38.7	57.7~146.8	双点
Rodriguez等^［2］	14.3	150	170	1.77	0.22	626	57	85~170	4.7	19.0~31.9	0~93.8	27.7~38.6	双点

表1

图1

图2

表2

图3

图4

图5

表3

各训练模型的特征重要性排序"

训练模型	特征重要性排序
训练模型	1	2	3	4	5	6	7	8	9	10	11
RF	$h 0$	$b$	$λ$	$f c$	$f y v$	$ρ v$	$η w$	$A s v$	$ρ l$	$s$	$η l$
RF	46.3%	14.7%	12.7%	6.9%	6.6%	4.4%	2.9%	2.0%	1.9%	0.9%	0.7%
XGBoost	$h 0$	$b$	$λ$	$η w$	$f c$	$η l$	$ρ l$	$f y v$	$ρ v$	$s$	$A s v$
XGBoost	32.6%	24.7%	21.4%	4.6%	3.8%	3.2%	3.2%	3.1%	1.7%	1.1%	0.6%
SVR	$h 0$	$λ$	$b$	$ρ l$	$η w$	$A s v$	$f y v$	$η l$	$s$	$ρ v$	$f c$
SVR	24.8%	20.3%	19.1%	7.0%	5.8%	5.1%	4.5%	3.7%	3.6%	3.2%	2.9%

表3

图6

表4

RC构件抗剪承载力计算模型"

作者	计算模型
李士斌等^［1］	集中荷载下的梁： $V c s = 1.75 f t b c h 0 c 1 + λ + f y v c A s v c h 0 c s$ ，矩形截面梁： $V c s = 0.7 f t b c h 0 c + 1.25 f y v A s v h 0 c s$
Ahmed K^［47］	$V c s = 0.17 λ f c' b e f f h 0 + A v, e f f f y v h 0 s$
赵羽习等^［9］	$V c s = P v V c s 0, P v = 1.0, ?????????????? η w ≤ 10 % 1.17 - 1.7 η w, η w > 10 %, V C S 0 = b h 0 λ C s h 0 1 - 0.5 C s h 0 f c' + 0.5 ρ v f y v 1 - C s h 0 2 λ 2$
霍艳华^［4］	$V c s = φ 1.75 1 + λ f t b h 0 + γ A s v f y v h 0 s, φ = 1.0, ????????????????? η l ≤ 5 % 1.098 - 1.96 η l, η l > 5 %, γ = 1 - 1.059 η w$

表4

表5

RC构件抗剪承载力各计算模型评价指标"

评价指标	李士斌等^［1］	Ahmed K^［47］	赵羽习等^［9］	霍艳华^［4］	本文模型	SVR	MLP	XGBoost	RF
$μ$	1.58	0.94	2.13	1.99	0.89	0.96	0.96	0.96	0.92
RMSE	61.46	49.37	92.07	68.5	25.6	13.3	14.6	14.9	20.1
MAE	41.88	37.7	65.13	47.53	17.8	8.8	9.9	10.1	13.0
MAPE/%	36.50	64.61	57.91	40.52	20.68	10.57	12.56	10.84	14.77
R2	0.844	0.898	0.653	0.807	0.948	0.985	0.982	0.982	0.966

表5

图7

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数据集	模型	指标
数据集	模型	R2	MAE	MAPE/%	RMSE
训练结果	RF	0.990	4.102	4.626	7.17
	XGBoost	0.998	1.367	1.572	2.90
	MLP	0.995	3.285	3.940	5.204
	SVR	0.993	3.003	3.438	6.252
测试结果	RF	0.966	12.995	14.774	20.133
	XGBoost	0.982	10.063	10.836	14.887
	MLP	0.982	9.850	12.557	14.634
	SVR	0.985	8.834	10.568	13.344

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基于可解释机器学习的锈蚀RC构件抗剪承载力预测模型

Prediction model for shear capacity of corroded RC beams based on interpretable machine learning

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