Interpretable gradient boosting based ensemble learning and African vultures optimization algorithm optimization for estimating deflection induced by excavation

doi:10.1007/s11709-024-1114-y

Frontiers of Structural and Civil Engineering

2024, Vol. 18

Issue (11): 1698-1712 https://doi.org/10.1007/s11709-024-1114-y

本期目录

Interpretable gradient boosting based ensemble learning and African vultures optimization algorithm optimization for estimating deflection induced by excavation

Zenglong LIANG¹, Shan LIN¹, Miao DONG¹, Xitailang CAO¹, Hongwei GUO^1,²(

), Hong ZHENG¹

¹. Key Laboratory of Urban Security and Disaster Engineering (Ministry of Education), Beijing University of Technology, Beijing 100124, China
². Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University (PolyU), Hong Kong 999077, China

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Abstract：

Intelligent construction has become an inevitable trend in the development of the construction industry. In the excavation project, using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process. An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm (AVOA) was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation. We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations. First, we exploratively analyzed these data to discover the relationship between features. We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection. The hyperparameters for all models in evaluation are optimized using AVOA, and then the optimized models are assembled into a unified framework for fairness assessment. The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well (RMSE = 1.84, MAE = 1.18, R² = 0.9993) and cross-validation (RMSE = 2.65 ± 1.54, MAE = 1.17 ± 0.23, R² = 0.998 ± 0.002). In the end, in order to improve the transparency and usefulness of the model, we constructed an interpretable model from both global and local perspectives.

Key words： African vultures optimization algorithm gradient boosting ensemble learning interpretable model wall deflection prediction

收稿日期: 2023-12-27 出版日期: 2024-11-28

Corresponding Author(s): Hongwei GUO

引用本文:

. [J]. Frontiers of Structural and Civil Engineering, 2024, 18(11): 1698-1712.
Zenglong LIANG, Shan LIN, Miao DONG, Xitailang CAO, Hongwei GUO, Hong ZHENG. Interpretable gradient boosting based ensemble learning and African vultures optimization algorithm optimization for estimating deflection induced by excavation. Front. Struct. Civ. Eng., 2024, 18(11): 1698-1712.

链接本文:

https://academic.hep.com.cn/fsce/CN/10.1007/s11709-024-1114-y
https://academic.hep.com.cn/fsce/CN/Y2024/V18/I11/1698

Fig.1

Parameter	Value
B (m)	30, 40, 50, 60
T (m)	20, 30, 35
H (m)	11, 14, 17, 20
$l n (E I / γ w h a v g 4)$	6.097, 7.313, 8.176, 8.846
$γ$ (kN/m)	15, 17, 19
$E 50 C u$	100, 200, 300, 400
$C u σ v ′$	0.21, 0.25, 0.29, 0.34

Tab.1

Fig.2

Fig.3

Fig.4

Fig.5

Tab.2

Fig.6

Fig.7

Tab.3

Tab.4

Fig.8

Fig.9

Location of the data in the dataset	Features							Wall deflection
Location of the data in the dataset	B	T	H	$c u σ v ′$	$E 50 c u$	$l n (E I / γ w h a v g 4)$	$γ$	Wall deflection
468	30	30	20	0.25	300	7.313	15	174
Tree SHAP values	−11.36	−5.08	22.85	27.06	−25.14	−13.89	43.13	174.06

Tab.5

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