Bayesian Optimized LightGBM model for predicting the fundamental vibrational period of masonry infilled RC frames

doi:10.1007/s11709-024-1077-z

Front. Struct. Civ. Eng.

2024, Vol. 18

Issue (7) : 1084-1102 https://doi.org/10.1007/s11709-024-1077-z

Bayesian Optimized LightGBM model for predicting the fundamental vibrational period of masonry infilled RC frames

Taimur RAHMAN¹, Pengfei ZHENG²(

), Shamima SULTANA³

¹. Department of Civil Engineering, World University of Bangladesh, Dhaka 1230, Bangladesh
². School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
³. Department of Computer Science & Engineering, University of Asia Pacific, Dhaka 1205, Bangladesh

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Abstract

The precise prediction of the fundamental vibrational period for reinforced concrete (RC) buildings with infilled walls is essential for structural design, especially earthquake-resistant design. Machine learning models from previous studies, while boasting commendable accuracy in predicting the fundamental period, exhibit vulnerabilities due to lengthy training times and inherent dependence on pre-trained models, especially when engaging with continually evolving data sets. This predicament emphasizes the necessity for a model that adeptly balances predictive accuracy with robust adaptability and swift data training. The latter should include consistent re-training ability as demanded by real-time, continuously updated data sets. This research implements an optimized Light Gradient Boosting Machine (LightGBM) model, highlighting its augmented predictive capabilities, realized through the astute use of Bayesian Optimization for hyperparameter tuning on the FP4026 research data set, and illuminating its adaptability and efficiency in predictive modeling. The results show that the R² score of LightGBM model is 0.9995 and RMSE is 0.0178, while training speed is 23.2 times faster than that offered by XGBoost and 45.5 times faster than for Gradient Boosting. Furthermore, this study introduces a practical application through a streamlit-powered, web-based dashboard, enabling engineers to effortlessly utilize and augment the model, contributing data and ensuring precise fundamental period predictions, effectively bridging scholarly research and practical applications.

Keywords masonry-infilled RC frame fundamental period LightGBM FP4026 research dataset machine learning data-driven approach Bayesian Optimization

Corresponding Author(s): Pengfei ZHENG

Just Accepted Date: 13 June 2024 Online First Date: 11 July 2024 Issue Date: 06 August 2024

Cite this article:

Taimur RAHMAN,Pengfei ZHENG,Shamima SULTANA. Bayesian Optimized LightGBM model for predicting the fundamental vibrational period of masonry infilled RC frames[J]. Front. Struct. Civ. Eng., 2024, 18(7): 1084-1102.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-024-1077-z
https://academic.hep.com.cn/fsce/EN/Y2024/V18/I7/1084

Fig.1 Methodology overview.

Fig.2 Violin plots showing distribution of period (s) for various building characteristics: (a) fundamental period vs. number of storey; (b) fundamental period vs. opening percentage (%); (c) fundamental period vs. length of spans (m); (d) fundamental period vs. number of spans; (e) fundamental period vs. masonry wall stiffness, E_t (× 10⁵ kN/m).

Tab.1 Statistical properties of the training and test data

Tab.2 Hyperparameters for the LightGBM model

Fig.3 The Bayesian Optimization process for LightGBM hyperparameter tuning.

Fig.4 R² scores of different hyperparameters.

Fig.5 Sensitivity Analyses of LightGBM Hyperparameters: (a) number of leaves sensitivity analysis; (b) maximum depth sensitivity analysis; (c) learning rate sensitivity analysis; (d) number of estimators sensitivity analysis; (e) minimum child samples sensitivity analysis; (f) subsample sensitivity analysis; (g) column sample by tree sensitivity analysis; (h) regularization alpha sensitivity analysis; (i) regularization lambda sensitivity analysis.

Fig.6 Predicted vs. actual values (LightGBM model): (a) training set; (b) test set.

Fig.7 Percent Error Distribution (LightGBM model): (a) training set; (b) test set.

Model	Hyperparameter	Value
Decision Tree	splitter of nodes (splitter)	‘best’, ‘random’
	maximum tree depth (max_depth)	‘None’, 1, 3, 5, 7, 9, 11
	minimum samples for split (min_samples_split)	2, 5, 10, 20, 50
	minimum samples of leaf node (min_samples_leaf)	1, 2, 5, 10, 20, 50
	number of features (max_features)	‘None’, ‘auto’, ‘sqrt’, ‘log₂’
	minimal cost-complexity pruning (ccp_alpha)	0.0, 0.01, 0.1, 1, 10
Random Forest	number of estimators (n_estimators)	100, 500, 1000
	maximum tree depth (max_depth)	‘None’, 5, 10, 15, 20
	minimum samples for split (min_samples_split)	2, 5, 10
	minimum samples of leaf node (min_samples_leaf)	1, 2, 4
	number of features (max_features)	‘auto’, ‘sqrt’, ‘log₂’
XGBoost	number of estimators (n_estimators)	100, 500, 1000
	maximum tree depth (max_depth)	‘None’, 1, 3, 5, 7, 9, 11
	learning rate (learning_rate)	0.01, 0.1, 0.2
	minimum sum of instance weight in a child node (min_child_weight)	1, 3, 5
	minimum loss reduction (gamma)	0, 0.1, 0.2
	fraction of observations to be randomly sampled for each tree (subsample)	0.8, 1
	column sample by tree (colsample_bytree)	0.8, 1
	L1 regularization term (reg_alpha)	0, 0.1, 1
	L2 regularization term (reg_lambda)	0.1, 1, 10
Gradient Boosting	number of estimators (n_estimators)	100, 500, 1000
	maximum tree depth (max_depth)	‘None’, 1, 3, 5, 7, 9, 11
	learning rate (learning_rate)	0.01, 0.1, 0.2
	minimum samples for split (min_samples_split)	2, 5, 10
	minimum samples of leaf node (min_samples_leaf)	1, 3, 5
	number of features (max_features)	‘None’, ‘sqrt’, ‘log₂’
	fraction of observations to be randomly sampled for each tree (subsample)	0.8, 1
	regularization parameter (alpha)	0.1, 0.5, 0.9
	minimal cost-complexity pruning (ccp_alpha)	0, 0.1, 1
CatBoost	number of iterations (iterations)	100, 500, 1000
	maximum tree depth (depth)	3, 5, 7, 9, 11
	learning rate (learning_rate)	0.01, 0.1, 0.2
	L2 regularization (l2_leaf_reg)	1, 3, 5, 7, 9
	border count (border_count)	32, 64, 128
	bagging temperature (bagging_temperature)	0, 0.5, 1
	random strength (random_strength)	0.2, 0.5, 0.8
NGBoost	number of estimators (n_estimators)	100, 500, 1000
	learning rate (learning_rate)	0.01, 0.1, 0.2
	mini-batch fraction (minibatch_frac)	0.5, 0.7, 1.0
	natural gradient (natural_gradient)	‘True’, ‘False’
	verbosity (verbose)	‘True’, ‘False’

Tab.3 Hyperparameters for the predictive models

Fig.8 Predicted vs. actual values and Percent Error Distribution for the compared models: (a) predicted vs. actual values (Decision Tree model); (b) Percent Error Distribution (Decision Tree model); (c) predicted vs. actual values (Random Forest model); (d) Percent Error Distribution (Random Forest model); (e) predicted vs. actual values (XGBoost model); (f) Percent Error Distribution (XGBoost model); (g) predicted vs. actual values (Gradient Boosting model); (h) Percent Error Distribution (Gradient Boosting model); (i) predicted vs. actual values (CatBoost model); (j) Percent Error Distribution (CatBoost model); (k) predicted vs. actual values (NGBoost model); (l) Percent Error Distribution (NGBoost model); (m) predicted vs. actual values (LightGBM model); (n) Percent Error Distribution (LightGBM model).

Tab.4 Performance evaluation of different models

Fig.9 Model performance comparison on evaluation metrics.

Fig.10 Fundamental periods dashboard overview: (a) dashboard homepage; (b) predicted period (s) calculation; (c) data contribution.

Fig.11 Workflow of the user interaction process on the dashboard.

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