[HTML][HTML] Data-driven evolutionary programming for evaluating the mechanical properties of concrete containing plastic waste.
Plastic waste (PW) has emerged as a global environmental concern due to its detrimental
impact on ecosystems and human health. Traditional concrete heavily relies on natural …
impact on ecosystems and human health. Traditional concrete heavily relies on natural …
[HTML][HTML] Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants
Airborne contaminants pose significant environmental and health challenges. Titanium
dioxide (TiO 2) has emerged as a leading photocatalyst in the degradation of air …
dioxide (TiO 2) has emerged as a leading photocatalyst in the degradation of air …
[HTML][HTML] Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete
In recent years, the construction industry has been striving to make production faster and
handle more complex architectural designs. Waste reduction, geometric freedom, lower …
handle more complex architectural designs. Waste reduction, geometric freedom, lower …
Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches
Water quality indexes (WQI) are pivotal in assessing aquatic systems. Conventional
modeling approaches rely on extensive datasets with numerous unspecified inputs, leading …
modeling approaches rely on extensive datasets with numerous unspecified inputs, leading …
A Review of Concrete Carbonation Depth Evaluation Models
X Wang, Q Yang, X Peng, F Qin - Coatings, 2024 - mdpi.com
Carbonation is one of the critical issues affecting the durability of reinforced concrete.
Evaluating the depth of concrete carbonation is of great significance for ensuring the quality …
Evaluating the depth of concrete carbonation is of great significance for ensuring the quality …
[HTML][HTML] Explainable machine learning methods for predicting water treatment plant features under varying weather conditions
Accurately predicting key features in WWTPs is essential for optimizing plant performance
and minimizing operational costs. This study assesses the potential of various machine …
and minimizing operational costs. This study assesses the potential of various machine …
[HTML][HTML] Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models
The construction sector is a major contributor to global greenhouse gas emissions. Using
recycled and waste materials in concrete is a practical solution to address environmental …
recycled and waste materials in concrete is a practical solution to address environmental …
Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches
Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly
enhance the electrical conductivity and strength of cementitious composites, contributing to …
enhance the electrical conductivity and strength of cementitious composites, contributing to …
[HTML][HTML] Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms
Contemporary infrastructure requires structural elements with enhanced mechanical
strength and durability. Integrating nanomaterials into concrete is a promising solution to …
strength and durability. Integrating nanomaterials into concrete is a promising solution to …
Optimized prediction modeling of micropollutant removal efficiency in forward osmosis membrane systems using explainable machine learning algorithms
This study investigated the feasibility of using machine learning (ML)-based models to
simulate the behavior of micropollutants (MPs) in the forward osmosis (FO) membrane water …
simulate the behavior of micropollutants (MPs) in the forward osmosis (FO) membrane water …