Data-driven pavement management for sustainable urban mobility: predicting transverse cracking in CRCP using GA-optimized SVR

Purpose The purpose of this study is to develop a predictive tool that enables urban facilities managers and transportation authorities to forecast transverse cracking in Continuously Reinforced Concrete Pavement (CRCP) with high accuracy. By integrating Genetic Algorithm (GA) with Support Vector Regression (SVR), this study seeks to enhance pavement maintenance planning, reduce infrastructure deterioration and support sustainable asset management practices in urban environments. This study contributes to data-driven decision-making aligned with Sustainable Development Goals (SDGs), particularly SDG 9 (industry, innovation and infrastructure) and SDG 11 (sustainable cities and communities). Design/methodology/approach This study uses a hybrid machine learning model combining SVR with GA optimization to predict transverse cracking in CRCP. A data set comprising 395 records from 33 pavement sections in the Long-Term Pavement Performance database is used. Predictor variables include structural, climatic and traffic-related features. The model’s performance is evaluated using five-fold cross-validation and benchmarked against traditional models such as Linear Regression and Decision Trees to assess accuracy and reliability in forecasting pavement distress. Findings The GA-SVR model achieved a mean root mean square error of 4.37 and an R² of 0.921, significantly outperforming Linear Regression and Decision Tree models. Variables such as pavement age, total thickness, precipitation and initial roughness were identified as key predictors. Three-dimensional (3D) interaction plots revealed how these factors jointly influence pavement performance over time. Residual analysis confirmed the robustness of the model, showing randomly distributed errors without systematic bias. These results demonstrate that GA-SVR is a reliable and accurate tool for forecasting pavement cracking in urban infrastructure systems. Originality/value To the best of the authors’ knowledge, this study is among the first to apply GA-optimized SVR for predicting transverse cracking in CRCP within the context of urban facilities management. This study bridges a gap between pavement engineering and smart infrastructure planning by integrating machine learning with sustainable development objectives. The model’s high accuracy and practical utility provide a novel contribution to proactive urban asset management, aligning technological innovation with citizen-focused outcomes. Its methodological framework can be adapted to other infrastructure systems, offering a scalable solution for smart city initiatives.