A review on optimization techniques used in civil engineering material and structure design

Rajput, Sarvesh P. S.; Datta, Suprabeet

doi:10.1016/j.matpr.2020.02.305

Cited by 13 publications

(12 citation statements)

References 85 publications

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“…Since the second World War, with the rapid advances made in computational methods, optimisation techniques based on mathematical programming have been increasingly deployed in the field of civil engineering (Topping, 1983). Optimisation refers to acquiring the best outcome under specific conditions (Rajput & Datta, 2019), and optimization problems are evident in many disciplines, including operations research, computing, engineering and economics. Optimisation techniques consist of a powerful set of tools that can be deployed to help the effective management of a company's resources, and can be seen as an artificial intelligence (AI) tool.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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Artificial intelligence techniques are at the centre of a major shift in business today. They have a very broad array of applications within businesses, including that of optimisation for risk reduction in civil engineering projects. This is an active area of research, which has started to see real-world applications over the last few decades. It is still hindered by the extreme complexity of civil engineering problems and the computing power necessary to tackle these, but the economic and other benefits of these emerging technologies are too important to ignore. With that in mind, this chapter reviews the current state of research and real-world practice of optimisation techniques and artificial intelligence in risk reduction in this field. It also examines related promising techniques and their future potential.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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“…Optimization refers to acquiring the best outcome under specific conditions [7]. In the field of civil engineering, optimization can be executed in each step of a project life cycle such as design, construction, operation, and maintenance.…”

Section: Introductionmentioning

confidence: 99%

Structural Optimization in Civil Engineering: A Literature Review

Mei

Wang

2021

Buildings

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Since tremendous resources are consumed in the architecture, engineering, and construction (AEC) industry, the sustainability and efficiency in this field have received increasing concern in the past few decades. With the advent and development of computational tools and information technologies, structural optimization based on mathematical computation has become one of the most commonly used methods for the sustainable and efficient design in the field of civil engineering. However, despite the wide attention of researchers, there has not been a critical review of the recent research progresses on structural optimization yet. Therefore, the main objective of this paper is to comprehensively review the previous research on structural optimization, provide a thorough analysis on the optimization objectives and their temporal and spatial trends, optimization process, and summarize the current research limitations and recommendations of future work. The paper first introduces the significance of sustainability and efficiency in the AEC industry as well as the background of this review work. Then, relevant articles are retrieved and selected, followed by a statistical analysis of the selected articles. Thereafter, the selected articles are analyzed regarding the optimization objectives and their temporal and spatial trends. The four major steps in the structural optimization process, including structural analysis and modelling, formulation of optimization problems, optimization techniques, and computational tools and design platforms, are also reviewed and discussed in detail based on the collected articles. Finally, research gaps of the current works and potential directions of future works are proposed. This paper critically reviews the achievements and limitations of the current research on structural optimization, which provide guidelines for future research on structural optimization in the field of civil engineering.

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“…As the real-world engineering designs grow in complexity, the needs for new optimization techniques and friendly frameworks to implement them become more needed than before. In recent decades, global optimization techniques have been extensively applied to various domains, including but not limited to engineering [1,2], machine learning [3], finance [4], health care [5], and many others. In this literature review, we classify optimization algorithms into four major categories relevant to our proposed research: evolutionary algorithms (EA), neural-based algorithms, hybrid neuroevolution, and gradient-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Section 4 presents NEORL application to variety of energy optimization problems that belong to nuclear power and fuel cell energy production. The reader can also find additional applications of NEORL in the webpage 2 . Discussions and conclusions of this work are highlighted in section 5.…”

Section: Introductionmentioning

confidence: 99%

NEORL: NeuroEvolution Optimization with Reinforcement Learning

Radaideh¹,

Katelin²,

Seurin³

et al. 2021

Preprint

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We present an open-source Python framework for NeuroEvolution Optimization with Reinforcement Learning (NEORL) developed at the Massachusetts Institute of Technology. NEORL offers a global optimization interface of state-of-the-art algorithms in the field of evolutionary computation, neural networks through reinforcement learning, and hybrid neuroevolution algorithms. NEORL features diverse set of algorithms, user-friendly interface, parallel computing support, automatic hyperparameter tuning, detailed documentation, and demonstration of applications in mathematical and real-world engineering optimization. NEORL encompasses various optimization problems from combinatorial, continuous, mixed discrete/continuous, to high-dimensional, expensive, and constrained engineering optimization. NEORL is tested in variety of engineering applications relevant to low carbon energy research in addressing solutions to climate change. The examples include nuclear reactor control and fuel cell power production. The results demonstrate NEORL competitiveness against other algorithms and optimization frameworks in the literature, and a potential tool to solve large-scale optimization problems. More examples and benchmarking of NEORL can be found here: https://neorl.readthedocs.io/en/latest/index.html

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A review on optimization techniques used in civil engineering material and structure design

Cited by 13 publications

References 85 publications

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Structural Optimization in Civil Engineering: A Literature Review

NEORL: NeuroEvolution Optimization with Reinforcement Learning

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