artificial intelligence in civil engineering literature review

Artificial Intelligence and Deep Learning in Civil Engineering

• First Online: 16 June 2023

Cite this chapter

• Ayla Ocak 4 ,
• Sinan Melih Nigdeli 4 ,
• Gebrail Bekdaş 4 &
• Ümit Işıkdağ 5  

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 480))

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2 Citations

Artificial intelligence is a variety of software developed that imitates the human brain to perform the tasks that the human brain can do. Aiming to minimize human intervention, this software has a wide range of content that deals with many problems such as perception, problem-solving, information transfer, planning, natural language processing, and so on. As a purpose-oriented method that can be used in many disciplines, it is preferred with high success rates, especially in the solution of engineering problems. Its sub-branches include machine learning, in which machines are trained to extract information from the available data. Machine learning and deep learning methods, which express more specific learning, make it possible to create a powerful predictive model. In this study, deep learning methods, which are a sub-branch of artificial intelligence and artificial intelligence, and the studies in which these methods are used in civil engineering are explained.

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Ayla Ocak, Sinan Melih Nigdeli & Gebrail Bekdaş

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Ocak, A., Nigdeli, S.M., Bekdaş, G., Işıkdağ, Ü. (2023). Artificial Intelligence and Deep Learning in Civil Engineering. In: Bekdaş, G., Nigdeli, S.M. (eds) Hybrid Metaheuristics in Structural Engineering. Studies in Systems, Decision and Control, vol 480. Springer, Cham. https://doi.org/10.1007/978-3-031-34728-3_13

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DOI : https://doi.org/10.1007/978-3-031-34728-3_13

Published : 16 June 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-34727-6

Online ISBN : 978-3-031-34728-3

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

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INTEGRATING ARTIFICIAL INTELLIGENCE INTO CIVIL ENGINEERING PRACTICE: OPPORTUNITIES AND IMPLICATIONS

2024, IAEME PUBLICAITON

This paper explores the integration of artificial intelligence (AI) into civil engineering practice, focusing on the opportunities and implications inherent in this transformation. Through an examination of current applications, emerging trends, and case studies, it highlights the potential of AI to enhance efficiency, innovation, and sustainability across the project lifecycle. However, it also discusses the ethical, technical, and regulatory challenges associated with AI adoption, emphasizing the importance of responsible implementation and ongoing skills development. Ultimately, this research underscores the need for collaborative efforts among stakeholders to maximize the benefits of AI while addressing its complexities in the civil engineering domain.

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The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Developmen...

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Artificial intelligence (AI) is a powerful technology with a range of capabilities, which are beginning to become apparent in all industries nowadays. The increased popularity of AI in the construction industry, however, is rather limited in comparison to other industry sectors. Moreover, despite AI being a hot topic in built environment research, there are limited review studies that investigate the reasons for the low-level AI adoption in the construction industry. This study aims to reduce this gap by identifying the adoption challenges of AI, along with the opportunities offered, for the construction industry. To achieve the aim, the study adopts a systematic literature review approach using the PRISMA protocol. In addition, the systematic review of the literature focuses on the planning, design, and construction stages of the construction project lifecycle. The results of the review reveal that (a) AI is particularly beneficial in the planning stage as the success of construction projects depends on accurate events, risks, and cost forecasting; (b) the major opportunity in adopting AI is to reduce the time spent on repetitive tasks by using big data analytics and improving the work processes; and (c) the biggest challenge to incorporate AI on a construction site is the fragmented nature of the industry, which has resulted in issues of data acquisition and retention. The findings of the study inform a range of parties that operate in the construction industry concerning the opportunities and challenges of AI adaptability and help increase the market acceptance of AI practices.

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Machine Learning (ML), a subset of Artificial Intelligence (AI), is gaining popularity in the architectural, engineering, and construction (AEC) sector. This systematic study aims to investigate the roles of AI and ML in improving construction processes and developing more sustainable communities. This study intends to determine the various roles played by AI and ML in the development of sustainable communities and construction practices via an in-depth assessment of the current literature. Furthermore, it intends to predict future research trends and practical applications of AI and ML in the built environment. Following the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, this study highlights the roles that AI and ML technologies play in building sustainable communities, both indoors and out. In the interior environment, they contribute to energy management by optimizing energy usage, finding inefficiencies, and recommending modifications to minimize consumpt...

Proc. 27th Annual Conference of the International Group for Lean Construction (IGLC)

Christian N N A E M E K A Egwim , Eren Demir

In recent years, there has been a surge in the global digitization of corporate processes and concepts such as digital technology development which is growing at such a quick pace that the construction industry is struggling to catch up with latest developments. A formidable digital technology, artificial intelligence (AI), is recognized as an essential element within the paradigm of digital transformation, having been widely adopted across different industries. Also, AI is anticipated to open a slew of new possibilities for how construction projects are designed and built. To obtain a better knowledge of the trend and trajectory of research concerning AI technology application in the construction industry, this research presents an exhaustive systematic review of seventy articles toward AI applicability to the entire lifecycle of the construction value chain identified via the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The review’s findings show foremostly that AI technologies are mostly used in facility management, creating a huge opportunity for the industry to profit by allowing facility managers to take proactive action. Secondly, it shows the potential for design expansion as a key benefit according to most of the selected literature. Finally, it found data augmentation as one of the quickest prospects for technical improvement. This knowledge will assist construction companies across the world in recognizing the efficiency and productivity advantages that AI technologies can provide while helping them make smarter technology investment decisions.

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Artificial intelligence is the branch of computer science that attempts to model cognitive processes such as learning, adaptability and perception to generate intelligent behavior capable of solving complex problems with environmental adaptation and deductive reasoning. Applied research of cutting-edge technologies, primarily computational intelligence, including machine/deep learning and fuzzy computing, can add value to modern science and, more generally, to entrepreneurship and the economy. Regarding the science of civil engineering and, more generally, 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 use of artificial intelligence can change industry business models, eliminate costly mistakes, reduce jobsite injuries and make large engineering projects more efficient. The purpose of this paper is to discuss recent research on artificial intelligence methods (machine and deep learning, computer vision, natural language processing, fuzzy systems, etc.) and their related technologies (extensive data analysis, blockchain, cloud computing, internet of things and augmented reality) in the fields of application of civil engineering science, such as structural engineering, geotechnical engineering, hydraulics and water resources. This review examines the benefits and limitations of using computational intelligence in civil engineering and the challenges researchers and practitioners face in implementing these techniques. The manuscript is targeted at a technical audience, such as researchers or practitioners in civil engineering or computational intelligence, and also intended for a broader audience such as policymakers or the general public who are interested in the civil engineering domain.

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Computer Science > Machine Learning

Title: generative ai for architectural design: a literature review.

Abstract: Generative Artificial Intelligence (AI) has pioneered new methodological paradigms in architectural design, significantly expanding the innovative potential and efficiency of the design process. This paper explores the extensive applications of generative AI technologies in architectural design, a trend that has benefited from the rapid development of deep generative models. This article provides a comprehensive review of the basic principles of generative AI and large-scale models and highlights the applications in the generation of 2D images, videos, and 3D models. In addition, by reviewing the latest literature from 2020, this paper scrutinizes the impact of generative AI technologies at different stages of architectural design, from generating initial architectural 3D forms to producing final architectural imagery. The marked trend of research growth indicates an increasing inclination within the architectural design community towards embracing generative AI, thereby catalyzing a shared enthusiasm for research. These research cases and methodologies have not only proven to enhance efficiency and innovation significantly but have also posed challenges to the conventional boundaries of architectural creativity. Finally, we point out new directions for design innovation and articulate fresh trajectories for applying generative AI in the architectural domain. This article provides the first comprehensive literature review about generative AI for architectural design, and we believe this work can facilitate more research work on this significant topic in architecture.

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