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

2024, Vol. 18

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

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.

Keywords African vultures optimization algorithm gradient boosting ensemble learning interpretable model wall deflection prediction

Corresponding Author(s): Hongwei GUO

Online First Date: 05 November 2024 Issue Date: 28 November 2024

Cite this article:

Zenglong LIANG,Shan LIN,Miao DONG, et al. Interpretable gradient boosting based ensemble learning and African vultures optimization algorithm optimization for estimating deflection induced by excavation[J]. Front. Struct. Civ. Eng., 2024, 18(11): 1698-1712.

URL:

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

Fig.1 Schematic graph of data flow in the whole ML model for excavation.

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 Settings for each parameter

Fig.2 Diaphragm wall deflection induced by braced excavations.

Fig.3 The box plot and the normal distribution of observation,distinguished by features: (a)

B

; (b)

T

; (c)

H

; (d)

C u σ v ′

; (e)

E 50 C u

; (f)

l n (E I / γ w h a v g 4)

; (g)

γ

Fig.4 Heatmap for mutual information.

Fig.5 Schematic diagram of GBA algorithm flow.

Tab.2 Performance of the predictive models on the test data set

Fig.6 Residuals plots for predictive models with AVOA optimization. Differentiation by model: (a) AVOA-CatBoost model; (b) AVOA-LightGBM model; (c) AVOA-SRR model; (d) AVOR-LR model.

Fig.7 Regression plots for predictive models with AVOA optimization. Differentiation by model: (a) AVOA-CatBoost model; (b) AVOA-LightGBM model; (c) AVOA-SRR model; (d) AVOR-LR model.

Tab.3 Model performance yielded from past literature

Tab.4 10-repeated 10-fold cross-validation (RMSE)

Fig.8

S 1

and

S T

of the AVOA-CatBoost model.

Fig.9 Global Tree SHAP value of AVOA-CatBoost.

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 Details about the 468th sample and Tree SHAP explanation results

Fig.10 Local Tree SHAP Value of AVOA-CatBoost in 468th sample.

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