Review of recent progress on the compressive behavior of masonry prisms
Masonry prisms have been widely used in research and quality control of masonry
structures, as they are simplified models that can represent the interaction between different …
structures, as they are simplified models that can represent the interaction between different …
Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
The use of wind energy plays a vital role in society owing to its economic and environmental
importance. Knowing the wind power generation within a specific time window is useful for …
importance. Knowing the wind power generation within a specific time window is useful for …
Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms
In this study, artificial intelligence algorithms are proposed for estimating the compressive
strength of hollow concrete block masonry prisms, including neural networks (ANN) …
strength of hollow concrete block masonry prisms, including neural networks (ANN) …
Compressive strength prediction of lightweight concrete: Machine learning models
Concrete is the most commonly used construction material. The physical properties of
concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre …
concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre …
Process-oriented guidelines for systematic improvement of supervised learning research in construction engineering
A limited assessment of the development process and various stages of machine learning
(ML) based solutions for construction engineering (CE) problems are available in the …
(ML) based solutions for construction engineering (CE) problems are available in the …
[HTML][HTML] Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms
Abstract Three-dimensional (3D) printing in the construction industry is growing rapidly due
to its inherent advantages, including intricate geometries, reduced waste, accelerated …
to its inherent advantages, including intricate geometries, reduced waste, accelerated …
Ensemble machine learning-based models for estimating the transfer length of strands in PSC beams
This study aims to develop four ensemble machine learning (ML) models, including Random
Forest (RF), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), and Extreme …
Forest (RF), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), and Extreme …
A soft-computing-based modeling approach for predicting acid resistance of waste-derived cementitious composites
This research aimed to build estimation models for the compressive strength (CS) of cement
mortar containing eggshell and glass powder after the acid attack using machine learning …
mortar containing eggshell and glass powder after the acid attack using machine learning …
Structure optimization of ensemble learning methods and seasonal decomposition approaches to energy price forecasting in Latin America: A case study about …
The energy price influences the interest in investment, which leads to economic
development. An estimate of the future energy price can support the planning of industrial …
development. An estimate of the future energy price can support the planning of industrial …
Prediction of masonry prism strength using machine learning technique: Effect of dimension and strength parameters
The compressive strength of masonry must be determined to design masonry constructions.
Although the compressive strength of masonry mostly depends on the compressive strength …
Although the compressive strength of masonry mostly depends on the compressive strength …