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Dr. Amarthaluri Tirupathaiah is an Associate Professor in the Department of Computer Science and Engineering at St. Ann’s College of Engineering and Technology, Andhra Pradesh, India. His research interests include Quantum Computing, Machine Learning, Artificial Intelligence, Data Science, and Emerging Technologies.
Construction projects are naturally susceptible to numerous hazards, including budget overruns, timeline delays, safety events, resource deficiencies, environmental uncertainty, and quality-related concerns. Conventional risk assessment techniques frequently depend on expert opinion and historical evaluation, which may prove inadequate for addressing the intricacies of contemporary construction projects. Machine Learning (ML) provides sophisticated predictive abilities by examining extensive project data to uncover concealed patterns, anticipate potential risks, and facilitate proactive decision-making. Utilising techniques like Decision Trees, Random Forests, Support Vector Machines, Artificial Neural Networks, and Deep Learning models, construction stakeholders can enhance project planning, risk management, and operational efficiency. This chapter examines the ideas, methodology, applications, and challenges of machine learning-based construction risk prediction systems, emphasising their contribution to project success and the digital transformation of the construction sector.
Quantum Machine Learning (QML) integrates the computational prowess of quantum computing with the predictive skills of machine learning to tackle intricate engineering challenges that are difficult for traditional computing systems. Utilising quantum phenomena including superposition, entanglement, and quantum parallelism, QML algorithms can analyse high-dimensional data, optimise intricate systems, and expedite learning processes. Engineering fields such as manufacturing, robotics, energy systems, materials science, telecommunications, and smart infrastructure are progressively investigating QML for improved decision-making and predictive modelling. This chapter delineates the essential principles, structures, algorithms, and practical applications of Quantum Machine Learning within the field of engineering. It also analyses contemporary obstacles, implementation frameworks, and prospective research avenues, emphasising the revolutionary capacity of QML in addressing next-generation engineering issues.