Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

PG Asteris, AD Skentou, A Bardhan, P Samui… - Cement and Concrete …, 2021 - Elsevier
This study aims to implement a hybrid ensemble surrogate machine learning technique in
predicting the compressive strength (CS) of concrete, an important parameter used for …

Artificial intelligence to model the performance of concrete mixtures and elements: a review

A Behnood, EM Golafshani - Archives of Computational Methods in …, 2022 - Springer
Concrete is the most widely used man-made material in the construction of structures,
pavements, bridges, dams, and infrastructures. Depending on the type of components and …

A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

DJ Armaghani, PG Asteris - Neural Computing and Applications, 2021 - Springer
Despite the extensive use of mortars materials in constructions over the last decades, there
is not yet a reliable and robust method, available in the literature, which can estimate its …

Prediction of concrete materials compressive strength using surrogate models

W Emad, AS Mohammed, R Kurda, K Ghafor… - Structures, 2022 - Elsevier
Using soft computing methods could be of great interest in predicting the compressive
strength of Ultra-High-Performance Fibre Reinforced Concrete (UHPFRC). Therefore, this …

Introducing stacking machine learning approaches for the prediction of rock deformation

M Koopialipoor, PG Asteris, AS Mohammed… - Transportation …, 2022 - Elsevier
Accurate and reliable predictions of rock deformations are crucial in many rock-based
projects in civil and mining engineering. In this research, a new system for the prediction of …

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

J Duan, PG Asteris, H Nguyen, XN Bui… - Engineering with …, 2021 - Springer
Recycled aggregate concrete is used as an alternative material in construction engineering,
aiming to environmental protection and sustainable development. However, the …

Prediction of cement-based mortars compressive strength using machine learning techniques

PG Asteris, M Koopialipoor, DJ Armaghani… - Neural Computing and …, 2021 - Springer
The application of artificial neural networks in mapping the mechanical characteristics of the
cement-based materials is underlined in previous investigations. However, this machine …

Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models

J Zhou, PG Asteris, DJ Armaghani, BT Pham - Soil Dynamics and …, 2020 - Elsevier
The present study aims to compare the performance of two machine learning techniques
that can unveil the relationship between the input and target variables and predict the …

Prediction of heating and cooling loads based on light gradient boosting machine algorithms

J Guo, S Yun, Y Meng, N He, D Ye, Z Zhao, L Jia… - Building and …, 2023 - Elsevier
Abstract Machine learning models have been widely used to study the prediction of heating
and cooling loads in residential buildings. However, most of these methods use the default …

Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate

H Xu, J Zhou, P G. Asteris, D Jahed Armaghani… - Applied sciences, 2019 - mdpi.com
Predicting the penetration rate is a complex and challenging task due to the interaction
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …