The Use of Machine Learning for Sustainable and Resilient Buildings

Kaswan, Kuldeep Singh; Dhatterwal, Jagjit Singh

doi:10.1002/9781119792079.ch1

Cited by 11 publications

(2 citation statements)

References 43 publications

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“…The marriage of big data and machine learning in the realm of self-healing concrete introduces a data-driven approach to material science, enabling a deeper understanding of the complex interplay between components and their influence on concrete performance. As these technologies continue to advance, their application in optimizing concrete properties showcases a promising avenue for enhancing the resilience and longevity of infrastructure through informed decision-making and precision engineering [61].…”

Section: Future Foundations: Integrating Big Data and Machine Learnin...mentioning

confidence: 99%

A Bibliography Study of Self-Healing Concrete

Ding

2024

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This paper delves into the critical issue of concrete cracks, a common consequence of environmental factors and external forces, and their potential impact on the structural integrity of buildings. Recognizing the urgency of addressing this issue, the concept of self-healing concrete has emerged, aiming to revolutionize the construction industry by autonomously repairing cracks and minimizing the need for labor-intensive and time-consuming manual interventions. The paper employs a bibliographic analysis methodology to comprehensively explore the most significant technologies associated with self-healing concrete, identifying key research countries/regions and prominent research institutions in this field. Through an examination of scholarly articles, the analysis seeks to uncover key themes, technologies, and contributors within the self-healing concrete domain. Insights into the geographical distribution of research efforts and the roles of influential research institutions are provided, shedding light on collaborative networks and knowledge-sharing dynamics that drive innovation in this evolving field.

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Section: Future Foundations: Integrating Big Data and Machine Learnin...mentioning

confidence: 99%

A Bibliography Study of Self-Healing Concrete

Ding

2024

Preprint

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“…The role of Artificial Intelligence in construction engineering for sustainable development is reviewed by Manzoor et al [5] and Yusel et al [6]. The application of machine learning for sustainable and resilient buildings was surveyed by Kaswan and Dhatterwal [7]. Artificial Neural Networks constitute the most popular and potential technology used for optimization, decision making, and forecasting to achieve sustainability Sustainability 2022, 14, 14738 2 of 21 in construction engineering.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Sustainable Development of the Construction Industry

et al. 2022

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Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.

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