Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms
The ground vibration caused by rock blasting is an extremely hazardous outcome of the
blasting operation. Blasting activity has detrimental effects on both the ecology and the …
blasting operation. Blasting activity has detrimental effects on both the ecology and the …
Enhancing engineering optimization using hybrid sine cosine algorithm with Roulette wheel selection and opposition-based learning
VHS Pham, NT Nguyen Dang, VN Nguyen - Scientific Reports, 2024 - nature.com
Meta-heuristic algorithms distinguish themselves from conventional optimization methods
owing to their intrinsic adaptability and straightforward implementation. Among them, the …
owing to their intrinsic adaptability and straightforward implementation. Among them, the …
[HTML][HTML] Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses
Ultra-high-performance concrete (UHPC) is a cutting-edge and advanced construction
material known for its exceptional mechanical properties and durability. Recently, machine …
material known for its exceptional mechanical properties and durability. Recently, machine …
[HTML][HTML] Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric …
The construction industry is making efforts to reduce the environmental impact of cement
production in concrete by incorporating alternative and supplementary cementitious …
production in concrete by incorporating alternative and supplementary cementitious …
[HTML][HTML] BIM integration with XAI using LIME and MOO for automated green building energy performance analysis
Achieving sustainable green building design is essential to reducing our environmental
impact and enhancing energy efficiency. Traditional methods often depend heavily on …
impact and enhancing energy efficiency. Traditional methods often depend heavily on …
Mean block size prediction in rock blast fragmentation using TPE-tree-based model approach with SHapley Additive exPlanations
The optimum fragmentation size measures the quality of a blasting operation. Boulders or
large fragments can result in more costs because they need secondary blasting, while small …
large fragments can result in more costs because they need secondary blasting, while small …
A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis
The precise prediction of construction costs during the initial phase of a construction project
is crucial for ensuring the project's success. Identifying the parameters that influence project …
is crucial for ensuring the project's success. Identifying the parameters that influence project …
Enhancing the predictive accuracy of recycled aggregate concrete's strength using machine learning and statistical approaches: a review
Recycled aggregate concrete (RAC) has emerged as a sustainable alternative in the
construction industry, reducing environmental impacts. However, predicting the mechanical …
construction industry, reducing environmental impacts. However, predicting the mechanical …
Evaluation and Prediction of Blast-Induced Ground Vibrations: A Gaussian Process Regression (GPR) Approach
Ground vibration is one of the most hazardous outcomes of blasting. It has a negative impact
both on the environment and the human population near to the blasting area. To evaluate …
both on the environment and the human population near to the blasting area. To evaluate …
A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis
Nowadays, recycled aggregate concrete (RAC) has been most extensively applied in the
construction industry as a sustainable resource to decrease carbon dioxide emissions and …
construction industry as a sustainable resource to decrease carbon dioxide emissions and …