Yihai Jia
Research Subjects
Yihai Jia has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Chuanxing Wang
Center for Advanced Functional Nanomaterials, Guangdong Tsingda Innovation Research Institute, Dongguan 523808, China
1 shared publication - Fengyang Wang
School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China
1 shared publication - Kuizhen Fang
Department of Civil Engineering, Tsinghua University, Beijing 100084, China
1 shared publication - Mingyu Wang
School of Environment and Civil Engineering, Dongguan University of Technology & Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for Urban Lifeline Engineering, Dongguan 523808, China
1 shared publication - Qile Ding
School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China
1 shared publication - Weifa He
Center for Advanced Functional Nanomaterials, Guangdong Tsingda Innovation Research Institute, Dongguan 523808, China
1 shared publication
Latest Publications
- Open Access
Interpretation of Dominant Features Governing Compressive Strength in One-Part Geopolymer
One-part geopolymers (OPG) offer a low-carbon alternative to Portland cement, yet mix design remains largely empirical. This study couples machine learning with SHAP (Shapley Additive Explanations) to quantify how mix and curing factors govern performance in Ca-containing OPG. We trained six regressors—Random Forest, ExtraTrees, SVR, Ridge, KNN, and XGBoost—on a compiled dataset and selected XGBoost as the primary model based on prediction accuracy. Models were built separately for four targets: compressive strength at 3, 7, 14, and 28 days. SHAP analysis reveals four dominant variables across targets—Slag, Na2O, Ms, and the water-to-binder ratio (w/b)—while the sand-to-binder ratio (s/b), temperature, and humidity are secondary within the tested ranges. Strength evolution follows a reaction–densification logic: at 3 days, Slag dominates as Ca accelerates C–(N)–A–S–H formation; at 7–14 days, Na2O leads as alkalinity/soluble silicate controls dissolution–gelation; by 28 days, Slag and Na2O jointly set the strength ceiling, with w/b continuously regulating porosity. Interactions are strongest for Slag × Na2O (Ca–alkalinity synergy). These results provide actionable guidance: prioritize Slag and Na2O while controlling w/b for strength. The XGBoost+SHAP workflow offers transparent, data-driven decision support for OPG mix optimization and can be extended with broader datasets and formal validation to enhance generalization.
Open AccessInterpretation of Dominant Features Governing Compressive Strength in One-Part Geopolymer
One-part geopolymers (OPG) offer a low-carbon alternative to Portland cement, yet mix design remains largely empirical. This study couples machine learning with SHAP (Shapley Additive Explanations) to quantify how mix and curing factors govern performance in Ca-containing OPG. We trained six regressors—Random Forest, ExtraTrees, SVR, Ridge, KNN, and XGBoost—on a compiled dataset and selected XGBoost as the primary model based on prediction accuracy. Models were built separately for four targets: compressive strength at 3, 7, 14, and 28 days. SHAP analysis reveals four dominant variables across targets—Slag, Na2O, Ms, and the water-to-binder ratio (w/b)—while the sand-to-binder ratio (s/b), temperature, and humidity are secondary within the tested ranges. Strength evolution follows a reaction–densification logic: at 3 days, Slag dominates as Ca accelerates C–(N)–A–S–H formation; at 7–14 days, Na2O leads as alkalinity/soluble silicate controls dissolution–gelation; by 28 days, Slag and Na2O jointly set the strength ceiling, with w/b continuously regulating porosity. Interactions are strongest for Slag × Na2O (Ca–alkalinity synergy). These results provide actionable guidance: prioritize Slag and Na2O while controlling w/b for strength. The XGBoost+SHAP workflow offers transparent, data-driven decision support for OPG mix optimization and can be extended with broader datasets and formal validation to enhance generalization.