Machine Learning-Based Modeling for Structural Engineering - A Comprehensive Survey and Applications Overview

Aug 3, 2026

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Modeling and simulation in structural engineering often demand substantial computational resources, and runtime grows rapidly when spatial and temporal scales expand. Rising needs for high-fidelity models, together with advances in artificial intelligence, increased data availability, and greater computing power, have accelerated the adoption of machine learning to enhance simulation accuracy and practicality. The survey presents methodologies for computationally demanding tasks including structural system identification, structural design, and prediction, highlighting deep neural network methods and other ML approaches. The review covers state-of-the-art applications across computational mechanics, structural health monitoring, design and manufacturing, stress and failure analysis, material modeling, and optimization.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages36

Reading Time1 h 31 min

AccessFree to download

Introduction

Machine Learning in Structural Engineering

ML System Components and Data Pipelines

Feature Representation and Dimensionality

Survey Scope and Applications

Structural System Identification

Structural Design and Prediction

buildings Review Machine Learning-Based Modeling for Structural Engineering: A Comprehensive Survey and Applications Overview Bassey Etim 1, Alia Al-Ghosoun 2, Jamil Renno 3, * , Mohammed Seaid 4 and M. Shadi Mohamed 1 Citation: Etim, B.; Al-Ghosoun, A.; Renno, J.; Seaid, M.; Mohamed, M.S. Machine Learning-Based Modeling for Structural Engineering: A Comprehensive Survey and Applications Overview. Buildings 2024, 14, 3515. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/buildings14113515](10.3390/buildings14113515) Academic Editor: Mijia Yang Received: 4 September 2024 Revised: 8 October 2024 Accepted: 16 October 2024 Published: 3 November 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 School of Energy, Geoscience, Infrastructure and Society, Institute for Infrastructure & Environment, Heriot-Watt University, Edinburgh EH14 4AS, UK; [be2007@hw.ac.uk](be2007@hw.ac.uk) (B.E.); [m.s.mohamed@hw.ac.uk](m.s.mohamed@hw.ac.uk) (M.S.M.) 2 Mechatronics Engineering Department, Philadelphia University, Amman 19392, Jordan; [aalghsoun@philadelphia.edu.jo](aalghsoun@philadelphia.edu.jo) 3 Department of Mechanical & Industrial Engineering, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar 4 Department of Engineering, University of Durham, South Road, Durham DH1 3LE, UK; [m.seaid@durham.ac.uk](m.seaid@durham.ac.uk) * [Correspondence: jamil.renno@qu.edu.qa](Correspondence: jamil.renno@qu.edu.qa) Abstract: Modeling and simulation have been extensively used to solve a wide range of problems in structural engineering. However, many simulations require significant computational resources, resulting in exponentially increasing computational time as the spatial and temporal scales of the models increase. This is particularly relevant as the demand for higher fidelity models and simulations increases. Recently, the rapid developments in artificial intelligence technologies, coupled with the wide availability of computational resources and data, have driven the extensive adoption of machine learning techniques to improve the computational accuracy and precision of simulations, which enhances their practicality and potential. In this paper, we present a comprehensive survey of the methodologies and techniques used in this context to solve computationally demanding problems, such as structural system identification, structural design, and prediction applications. Specialized deep neural network algorithms, such as the enhanced probabilistic neural network, have been the subject of numerous articles. However, other machine learning algorithms, including neural dynamic classification and dynamic ensemble learning, have shown significant potential for major advancements in specific applications of structural engineering. Our objective in this paper is to provide a state-of-the-art review of machine learning-based modeling in structural engineering, along with its applications in the following areas: (i) computational mechanics,(ii) structural health monitoring,(iii) structural design and manufacturing,(iv) stress analysis,(v) failure analysis,(vi) material modeling and design, and (vii) optimization problems. We aim to offer a comprehensive overview and provide perspectives on these powerful techniques, which have the potential to become alternatives to conventional modeling methods. Keywords: machine learning; computational mechanics; structural health monitoring; structural design and manufacturing; stress analysis; failure analysis; material modeling and design; optimization problems 1. Introduction Machine learning (ML) is a key artificial intelligence technology tha

FAQs

Why are machine learning techniques increasingly used in structural engineering simulations?

Many simulations require large computational resources, and computation time escalates as model scales grow. Machine learning helps improve accuracy and precision while leveraging readily available data and computing resources.

What types of structural engineering problems does the survey focus on?

The paper covers computationally demanding tasks such as structural system identification, structural design, and prediction applications.

Which application areas are included in the state-of-the-art review?

The review organizes applications into computational mechanics, structural health monitoring, structural design and manufacturing, stress analysis, failure analysis, material modeling and design, and optimization problems.

Aug 3, 2026

Upload by Noah