A review of physics-based machine learning in civil engineering

Vadyala, Shashank Reddy; Betgeri, Sai Nethra; Matthews, John C.; Matthews, Elizabeth

doi:10.1016/j.rineng.2021.100316

Cited by 108 publications

(43 citation statements)

References 109 publications

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“…This sum is fed as an argument to the activation function, which each node implements internally. The value the part receives for that argument is the neuron's output for the current inputs and weights [11], [13].…”

Section: Neural Networkmentioning

confidence: 99%

A Selective Survey Review of Computational Intelligence Applications in the Primary Subdomains of Civil Engineering Specializations

Demertzis¹,

Demertzis²,

Iliadis³

2023

Preprint

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Advanced state-of-the-art technologies, mainly computational intelligence including Machine/Deep Learning and Fuzzy Computing, through applied research, can provide added value to modern science and, in general, to entrepreneurship and the economy. Artificial intelligence is the field of computer science that attempts to model concepts such as learning, adaptability and perception to synthesize intelligent behavior in solving complex problems, utilizing elements of adaptation to the environment and philosophical reasoning. About the science of civil engineering and, in general, the construction industry, which is one of the most important in economic entrepreneurship, both in terms of the size of the workforce employed and the amount of capital invested, the penetration of artificial intelligence is possible to change industry business models, eliminate costly mistakes, reduce Jobsite injuries and generally make large-scale engineering projects more efficient. The purpose of the paper is to present the recent research on artificial intelligence methods (machine-deep learning, computer vision, natural language processing, fuzzy systems, robotics, etc.) and the corresponding related technologies related to it (extensive data analysis, blockchain, cloud computing, internet of things, augmented reality), in the fields of application of civil engineering science, including structural engineering, geotechnical engineering, hydraulics and water resources management, marine and coastal technology, transport and transportation infrastructure, planning and technical project management, critical infrastructure security and disaster mitigation..

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Section: Neural Networkmentioning

confidence: 99%

A Selective Survey Review of Computational Intelligence Applications in the Primary Subdomains of Civil Engineering Specializations

Demertzis¹,

Demertzis²,

Iliadis³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…There exist different types of ANNs, each one particularly efficient or suited to solve specific types of problems. For instance, convolutional neural networks (CNN) (Aloysius and Geetha, 2017) and physical informed neural networks (PINN) (Vadyala et al, 2022), just to mention a couple. However, the essence methodology and the scalability properties remain the same for all the different kinds.…”

Section: Machine Learningmentioning

confidence: 99%

Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps

Solorzano

Plevris

2022

Front. Built Environ.

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The modeling and simulation of structural systems is a task that requires high precision and reliable results to ensure the stability and safety of construction projects of all kinds. For many years now, structural engineers have relied on hard computing strategies for solving engineering problems, such as the application of the Finite Element Method (FEM) for structural analysis. However, despite the great success of FEM, as the complexity and difficulty of modern constructions increases, the numerical procedures required for their appropriated design become much harder to process using traditional methods. Therefore, other alternatives such as Computational Intelligence (CI) techniques are gaining substantial popularity among professionals and researchers in the field. In this study, a data-driven bibliometric analysis is presented with the aim to investigate the current research directions and the applications of CI-based methodologies for the simulation and modeling of structures. The presented study is centered on a self-mined database of nearly 8000 publications from 1990 to 2022 with topics related to the aforementioned field. The database is processed to create various two-dimensional bibliometric maps and analyze the relevant research metrics. From the maps, some of the trending topics and research gaps are identified based on an analysis of the keywords. Similarly, the most contributing authors and their collaborations are assessed through an analysis of the corresponding citations. Finally, based on the discovered research directions, various recent publications are selected from the literature and discussed in detail to set examples of innovative CI-based applications for the modeling and simulation of structures. The full methodology that is used to obtain the data and generate the bibliometric maps is presented in detail as a means to provide a clearer interpretation of the bibliometric analysis results.

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“…Through learning the introduced data and improving the algorithms that are embedded in AI-based technologies, a fundamental transformation in the modelling and simulation mindset was reached. There have been various applications of AI used in different industries, such as energy [64][65][66][67][68][69][70], transportation [71], medicine [72][73][74][75], and various other natural sciences [76][77][78]. Furthermore, the use and implementation of traditional modelling methods have been enhanced by collaborating with AI-based machine learning tools [79][80][81].…”

Section: Artificial Intelligence (Ai) and Machine Learning (Ml)mentioning

confidence: 99%

Artificial Intelligence-Based Machine Learning toward the Solution of Climate-Friendly Hydrogen Fuel Cell Electric Vehicles

Peksen¹

2022

Vehicles

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The rapid conversion of conventional powertrain technologies to climate-neutral new energy vehicles requires the ramping of electrification. The popularity of fuel cell electric vehicles with improved fuel economy has raised great attention for many years. Their use of green hydrogen is proposed to be a promising clean way to fill the energy gap and maintain a zero-emission ecosystem. Their complex architecture is influenced by complex multiphysics interactions, driving patterns, and environmental conditions that put a multitude of power requirements and boundary conditions around the vehicle subsystems, including the fuel cell system, the electric motor, battery, and the vehicle itself. Understanding its optimal fuel economy requires a systematic assessment of these interactions. Artificial intelligence-based machine learning methods have been emerging technologies showing great potential for accelerated data analysis and aid in a thorough understanding of complex systems. The present study investigates the fuel economy peaks during an NEDC in fuel cell electric vehicles. An innovative approach combining traditional multiphysics analyses, design of experiments, and machine learning is an effective blend for accelerated data supply and analysis that accurately predicts the fuel consumption peaks in fuel cell electric vehicles. The trained and validated models show very accurate results with less than 1% error.

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A review of physics-based machine learning in civil engineering

Cited by 108 publications

References 109 publications

A Selective Survey Review of Computational Intelligence Applications in the Primary Subdomains of Civil Engineering Specializations

A Selective Survey Review of Computational Intelligence Applications in the Primary Subdomains of Civil Engineering Specializations

Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps

Artificial Intelligence-Based Machine Learning toward the Solution of Climate-Friendly Hydrogen Fuel Cell Electric Vehicles

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