Establishment of icing prediction model of asphalt pavement based on support vector regression algorithm and Bayesian optimization

Yang, En-Hui; Yang, Qinlong; Li, Jie; Zhang, Haopeng; Di, Haibo; Qiu, Yanjun

doi:10.1016/j.conbuildmat.2022.128955

Cited by 15 publications

(5 citation statements)

References 22 publications

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“…In comparison to other search algorithms, Bayesian optimization translates the search problem into an optimization problem and takes into account the previous observation space and optimization results when updating hyperparameters. TPE is an enhancement of traditional Bayesian optimization [22]. It transforms the configuration space into a non-parametric density distribution, which can be expressed using a uniform distribution, a discrete uniform distribution, or a logarithmic uniform distribution.…”

Section: Tree-structured Parzen Estimator Methods For Hyperparameter ...mentioning

confidence: 99%

Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model

Xiao,

Wang,

Liu

et al. 2023

Applied Sciences

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Faulting refers to the common and significant distress in Jointed Plain Concrete Pavement (JPCP), which has an adverse impact on the pavement roughness. Nevertheless, the existing fault prediction models continue to heavily rely on conventional linear regression techniques or basic machine learning approaches, which leaves room for improvement in training efficiency and interpretability. To enhance training efficiency and accuracy, this study developed five novel faulting prediction models. These models are based on five basic machine learning algorithms: Random Forest (RF), Additive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), and Categorical Boost (CatBoost), combined with the tree-structured Parzen estimator (TPE). The five models are TPE-RF, TPE-AdaBoost, TPE-GBDT, TPE-LightGBM, and TPE-CatBoost. In addition to selecting the best-performing model, this study incorporated the Shapley Additive Explanation (SHAP) technique and developed TPE-SHAP-CatBoost to improve the interpretability of the model’s predictions. The process involved extracting historical data on pavement performance, including 17 variables, from the Long-Term Pavement Performance (LTPP) database for 160 instances of observation. Firstly, the Boruta method was used to identify the final set of input variables. Secondly, the TPE technique, which is a Bayesian optimization method, was applied to automatically select the optimal hyperparameters for the base models. Finally, SHAP was used to provide both global and local explanations of the model’s outputs. The results indicate that the TPE-CatBoost model achieves the highest accuracy with an R2 value of 0.906. Furthermore, the TPE-SHAP-CatBoost model identified the primary factors influencing faulting by incorporating SHAP and provided explanations of the model’s results at both the global and local levels. These research findings highlight the ability of the proposed model to accurately predict faulting, providing precise and interpretable guidance for pavement maintenance while reducing workload for pavement engineers in data collection and management.

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Section: Tree-structured Parzen Estimator Methods For Hyperparameter ...mentioning

confidence: 99%

Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model

Xiao,

Wang,

Liu

et al. 2023

Applied Sciences

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“…Parameters were optimized based on varying sample weights using a hybrid swarm intelligence optimization algorithm, combining PSO and the ant colony algorithm, thereby enhancing the model's generalization capacity, as evidenced by experimental results [16]. The incorporation of Bayesian optimization into SVR facilitates automatic parameter adjustment, taking into full account the inter-related [17,18]. In the practical deployment of prediction models hinged on icing mechanisms, the accuracy is invariably contingent upon the judicious selection of parameters.…”

Section: Machine Learning Modelmentioning

confidence: 99%

“…If B f < 0.5, the beluga position is updated using Equation (11). Then, the fitness value of the new position is calculated and ranked based on the population optimization strategy of Equation (17). Then, the results are compared with those of the previous generation to find the best result.…”

Section: Ibwomentioning

confidence: 99%

Online Prediction Method of Transmission Line Icing Based on Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion and Improved Beluga Whale Optimization Algorithm–Least Squares Support Vector Regression

Li,

Liao,

Cui

et al. 2024

ASI

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Due to the prevalent challenges of inadequate accuracy, unstandardized parameters, and suboptimal efficiency with regard to icing prediction, this study introduces an innovative online method for icing prediction based on Robust STL–BTSF and IBWO–LSSVR. Firstly, this study adopts the Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion (Robust STL–BTSF) approach, which is demonstrably effective for short-term and limited sample data preprocessing. Subsequently, injecting a multi-faceted enhancement approach to the Beluga Whale Optimization algorithm (BWO), which integrates a nonlinear balancing factor, a population optimization strategy, a whale fall mechanism, and an ascendant elite learning scheme. Then, using the Improved BWO (IBWO) above to optimize the key hyperparameters of Least Squares Support Vector Regression (LSSVR), a superior offline predictive part is constructed based on this approach. In addition, an Incremental Online Learning algorithm (IOL) is imported. Integrating the two parts, the advanced online icing prediction model for transmission lines is built. Finally, simulations based on actual icing data unequivocally demonstrate that the proposed method markedly enhances both the accuracy and speed of predictions, thereby presenting a sophisticated solution for the icing prediction on the transmission lines.

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“…Support vector machines (SVMs) represent supervised machine learning methodologies that are remarkably suited to addressing both classification (SVC) and regression (SVR) tasks, even in the presence of a limited dataset size and high dimensionality problems [52]. SVMs are widely used in pavement engineering and have been successfully implemented in recent years for different predictive modeling purposes [39,53].…”

Section: Support Vector Machine Modelingmentioning

confidence: 99%

“…Two of the well-established ML algorithms implemented in these methodologies are related to support vector machines (SVMs) and decision trees (DTs); the former were proposed by Vapnik [37] and are able to accomplish supervised learning tasks. They have found many interesting applications within the asphalt pavement field by successfully predicting the international roughness index [38], or the icing phenomenon and its influencing factors [39]. The latter have been deeply explored in their boosting variants, namely LightGBM [40], XGBoost [41] and CatBoost [42], for the prediction of the rutting depth of asphalt concrete containing reclaimed asphalt pavement (RAP) [43].…”

Section: Introductionmentioning

confidence: 99%

Laboratory Investigation and Machine Learning Modeling of Road Pavement Asphalt Mixtures Prepared with Construction and Demolition Waste and RAP

Rondinella,

Oreto,

Abbondati

et al. 2023

Sustainability

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Due to the decreasing availability of virgin materials coupled with an increased awareness of environmental sustainability issues, many researchers have focused their efforts on investigating innovative technological solutions in the civil engineering domain. This paper aims to evaluate the suitability of construction and demolition waste (C and DW) and reclaimed asphalt pavement (RAP) reused within asphalt mixtures (AMs) prepared for the binder layer of road pavements. Both hot and cold mixing methodologies were investigated. The technical assessment was based on the volumetric and mechanical suitability, according to saturated surface dry voids (SSDV) and indirect tensile strength (ITS) tests carried out at 10 °C, respectively. Laboratory findings showed that all the hot AMs matched the desired target SSDV at the design gyrations number at different optimum bitumen content levels, alternatively showing a non-significant variation or a significant increase in ITS compared to conventional hot mix asphalt. Conversely, the cold AMs with cement and emulsion bitumen showed a greater volume of voids and moisture sensitivity, and lower temperature susceptibility compared to hot AMs, reaching, on average, 11% lower ITS when using coarse C and DW aggregates and 43% lower ITS when using filler from C and DW. These volumetric and mechanical properties were modeled by means of support vector machines and categorical boosting (CatBoost) machine learning algorithms. The results proved to be satisfactory, with CatBoost determination coefficients R2 referring to SSDV and ITS equal to 0.8678 and 0.9916, respectively. This allowed for the mechanical performance of these sustainable mixtures to be predicted with high accuracy and implemented within conventional mix design procedures.

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Establishment of icing prediction model of asphalt pavement based on support vector regression algorithm and Bayesian optimization

Cited by 15 publications

References 22 publications

Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model

Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model

Online Prediction Method of Transmission Line Icing Based on Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion and Improved Beluga Whale Optimization Algorithm–Least Squares Support Vector Regression

Laboratory Investigation and Machine Learning Modeling of Road Pavement Asphalt Mixtures Prepared with Construction and Demolition Waste and RAP

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