Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength

A Kaveh, N Khavaninzadeh - Structures, 2023 - Elsevier
In recent years, artificial neural network (ANN) is one of the popular and effective machine
learning models that can be used to accurately predict fiber reinforced polymer (FRP) …

[HTML][HTML] Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

BA Salami, M Iqbal, A Abdulraheem, FE Jalal… - Cement and Concrete …, 2022 - Elsevier
Foamed concrete is special not only in terms of its unique properties, but also in terms of its
challenging compositional mixture design, which necessitates multiple experimental trials …

Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis

MH Nguyen, HB Ly - Construction and Building Materials, 2023 - Elsevier
Fiber-reinforced self-compacting concrete (FRSCC), a great combination of self-compacting
concrete (SCC) and fiber, plays a vital role as a potential construction material. Improving …

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

R Kazemi - Engineering Reports, 2023 - Wiley Online Library
Advanced concrete technology is the science of efficient, cost‐effective, and safe design in
civil engineering projects. Engineers and concrete designers are generally faced with the …

Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models

MH Nguyen, SH Trinh, HB Ly - Construction and Building Materials, 2023 - Elsevier
The utilization of recycled brick aggregate concrete (RBC) is an area of active research, in
which further investigation is needed to develop accurate models for predicting the behavior …

Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques

A Ashrafian, E Panahi, S Salehi, M Karoglou… - Structures, 2023 - Elsevier
The critical challenge for the cement production industry is the high emission of greenhouse
gases. For the sustainability in a cycling economy context civil and environmental engineers …

A novel hybrid adaptive boosting approach for evaluating properties of sustainable materials: A case of concrete containing waste foundry sand

AR Ghanizadeh, AT Amlashi, S Dessouky - Journal of Building Engineering, 2023 - Elsevier
Ensemble learning (EL) has gained popularity in recent investigations because of its higher
prediction accuracy than conventional machine learning (ML) methods. Regressors and EL …

[HTML][HTML] A comparative analysis of machine learning models in prediction of mortar compressive strength

R Gayathri, SU Rani, L Čepová, M Rajesh, K Kalita - Processes, 2022 - mdpi.com
Predicting the mechanical properties of cement-based mortars is essential in understanding
the life and functioning of structures. Machine learning (ML) algorithms in this regard can be …

[HTML][HTML] Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses

A Kashem, R Karim, SC Malo, P Das, SD Datta… - Case Studies in …, 2024 - Elsevier
Ultra-high-performance concrete (UHPC) is a cutting-edge and advanced construction
material known for its exceptional mechanical properties and durability. Recently, machine …

Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete

X Mei, C Li, Q Sheng, Z Cui, J Zhou… - Mechanics of Advanced …, 2023 - Taylor & Francis
This study, proposes the use of a novel rubber-sand concrete (RSC) material, which
comprises rubber particles, sand, and cement, as an aseismic material in practical …