Predictive models for concrete properties using machine learning and deep learning approaches: A review

MM Moein, A Saradar, K Rahmati… - Journal of Building …, 2023 - Elsevier
Concrete is one of the most widely used materials in various civil engineering applications.
Its global production rate is increasing to meet demand. Mechanical properties of concrete …

The phenomenon of cracking in cement concretes and reinforced concrete structures: the mechanism of cracks formation, causes of their initiation, types and places of …

GL Golewski - Buildings, 2023 - mdpi.com
Cracks and cavities belong to two basic forms of damage to the concrete structure, which
may reduce the load-bearing capacity and tightness of the structure and lead to failures and …

Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences

MZ Naser, AH Alavi - Architecture, Structures and Construction, 2023 - Springer
Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …

Machine learning prediction of mechanical properties of concrete: Critical review

WB Chaabene, M Flah, ML Nehdi - Construction and Building Materials, 2020 - Elsevier
Accurate prediction of the mechanical properties of concrete has been a concern since
these properties are often required by design codes. The emergence of new concrete …

Emerging artificial intelligence methods in structural engineering

H Salehi, R Burgueño - Engineering structures, 2018 - Elsevier
Artificial intelligence (AI) is proving to be an efficient alternative approach to classical
modeling techniques. AI refers to the branch of computer science that develops machines …

Machine learning techniques for structural health monitoring of heritage buildings: A state-of-the-art review and case studies

M Mishra - Journal of Cultural Heritage, 2021 - Elsevier
This paper performed a systematic review of the various machine learning (ML) techniques
applied to assess the health condition of heritage buildings. More robust predictive models …

A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm

Q Han, C Gui, J Xu, G Lacidogna - Construction and Building Materials, 2019 - Elsevier
The prediction results of high-performance concrete compressive strength (HPCCS) based
on machine learning methods are seriously influenced by input variables and model …

Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves

A Behnood, EM Golafshani - Journal of cleaner production, 2018 - Elsevier
The use of silica fume as a partial replacement for Ordinary Portland Cement provides a
wide variety of benefits such as reduced pressure on natural resources, reduced CO 2 …

A sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a Monte Carlo simulation

DV Dao, H Adeli, HB Ly, LM Le, VM Le, TT Le… - Sustainability, 2020 - mdpi.com
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI)
techniques, namely Gaussian Process Regression (GPR) with five different kernels …

Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm

A Behnood, V Behnood, MM Gharehveran… - … and Building Materials, 2017 - Elsevier
Compressive strength of concrete is one the parameters required in many design codes. A
reliable prediction of it can save in time and cost by quickly generating the needed design …