Bayesian Optimization-Based SVR and RF Models for Predicting Compaction Quality of SBS-Modified Asphalt Pavements Using Intelligent Compaction Data

This repository contains the source code for the study titled “Bayesian Optimization-Based SVR and RF Models for Predicting Compaction Quality of SBS-Modified Asphalt Pavements Using Intelligent Compaction Data.” The code implements Bayesian Optimization (BO)-tuned Support Vector Regression (SVR) and Random Forest (RF) models for predicting Intelligent Compaction Measurement Values (ICMVs) and Non-Nuclear Density Gauge (NNDG) values using field-collected data from SBS-modified asphalt pavement projects. Input variables include section length, vibratory roller passes, roller speed, vibration amplitude, and mat temperature. The framework also includes multi-output modeling, model evaluation, and SHAP-based explainable AI analysis to quantify feature importance and interpret compaction behavior.本代码仓库收录了题为《基于贝叶斯优化的SVR与RF模型：利用智能压实数据预测SBS改性沥青路面压实质量》的相关研究源代码。本代码实现了经贝叶斯优化（Bayesian Optimization, BO）调优的支持向量回归（Support Vector Regression, SVR）与随机森林（Random Forest, RF）模型，可依托SBS改性沥青路面工程的现场采集数据，预测智能压实测量值（Intelligent Compaction Measurement Values, ICMVs）与无核密度仪（Non-Nuclear Density Gauge, NNDG）检测值。输入变量涵盖路段长度、振动压路机碾压遍数、压路机行驶速度、振动振幅以及摊铺层温度。该框架还集成了多输出建模、模型评估以及基于SHAP的可解释人工智能分析，用以量化特征重要性并解析压实行为。