Vehicle intrusion detection in highway work zones using inertial sensors and lightweight deep learning

Automation in Construction, Год журнала: 2025, Номер 176, С. 106291 - 106291

Опубликована: Май 22, 2025

Язык: Английский

Predictive Models with Applicable Graphical User Interface (GUI) for the Compressive Performance of Quaternary Blended Plastic-Derived Sustainable Mortar

Buildings, Год журнала: 2025, Номер 15(11), С. 1932 - 1932

Опубликована: Июнь 3, 2025

Machine learning (ML) models in material science and construction engineering have significantly improved predictive accuracy decision making. However, the practical implementation of these often requires technical expertise, limiting their accessibility for engineers practitioners. A user-friendly graphical user interface (GUI) can be an essential tool to bridge this gap. In study, a sustainable approach improve compressive strength (C.S) plastic-based mortar mixes (PMMs) by replacing cement with industrial waste materials was investigated using ML such as support vector machine, AdaBoost regressor, extreme gradient boosting. The significance key mix parameters further analyzed SHapley Additive exPlanations (SHAPs) interpret influence input variables on model predictions. To enhance usability real-world application models, GUI developed provide accessible platform predicting C.S PMMs based proportions. demonstrated strong correlations experimental results, insights from SHAP analysis data-driven design strategies. serves scalable system, encouraging adoption ML-based approaches engineering.

Язык: Английский

Machine learning models for predicting the compressive strength of cement-based mortar materials: Hyper tuning and optimization

Structures, Год журнала: 2024, Номер 71, С. 107931 - 107931

Опубликована: Дек. 10, 2024

Язык: Английский