Machine learning for structural engineering: A state-of-the-art review

HT Thai - Structures, 2022 - Elsevier
Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …

State-of-the-art AI-based computational analysis in civil engineering

C Wang, L Song, Z Yuan, J Fan - Journal of Industrial Information …, 2023 - Elsevier
With the informatization of the building and infrastructure industry, conventional analysis
methods are gradually proving inadequate in meeting the demands of the new era, such as …

Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach

J Rahman, KS Ahmed, NI Khan, K Islam… - Engineering …, 2021 - Elsevier
The incorporation of steel fibers in a concrete mix enhances the shear capacity of reinforced
concrete beams and a comprehensive understanding of this phenomenon is imperative to …

Machine learning prediction of compressive strength for phase change materials integrated cementitious composites

A Marani, ML Nehdi - Construction and Building Materials, 2020 - Elsevier
Incorporating phase change materials (PCMs) into cementitious composites has recently
attracted paramount interest. While it can enhance thermal characteristics and energy …

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

G Zhang, ZH Ali, MS Aldlemy, MH Mussa… - Engineering with …, 2022 - Springer
The design and sustainability of reinforced concrete deep beam are still the main issues in
the sector of structural engineering despite the existence of modern advancements in this …

Prediction of maximum pitting corrosion depth in oil and gas pipelines

MEAB Seghier, B Keshtegar, KF Tee, T Zayed… - Engineering Failure …, 2020 - Elsevier
Avoiding failures of corroded steel structures are critical in offshore oil and gas operations.
An accurate prediction of maximum depth of pitting corrosion in oil and gas pipelines has …

Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model

A Ashrafian, F Shokri, MJT Amiri, ZM Yaseen… - … and Building Materials, 2020 - Elsevier
Accurate prediction of compressive strength (fc) is one of the crucial problems in the
concrete industry. In this study, novel self-adaptive and formula-based model called …

A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer

SQ Salih, ARA Alsewari - Neural Computing and Applications, 2020 - Springer
Metaheuristic algorithms have received much attention recently for solving different
optimization and engineering problems. Most of these methods were inspired by nature or …

Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network

G Pazouki, EM Golafshani, A Behnood - Structural Concrete, 2022 - Wiley Online Library
The use of Class F fly ash (CFFA) as a partial replacement of cement in the concrete mixture
can provide a wide variety benefits such as improving the mechanical properties, reducing …

Prediction of the residual flexural strength of fiber reinforced concrete using artificial neural networks

M Congro, VM de Alencar Monteiro… - … and Building Materials, 2021 - Elsevier
The work in hand proposes Artificial Neural Networks (ANN) to predict the residual strength
of fiber reinforced concrete under bending load. A database containing experimental and …