Data Analytics For Engineering And Construction Project Risk Management
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Data Analytics for Engineering and Construction Project Risk Management
This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.
Foundations of Data Science for Engineering Problem Solving
Author: Parikshit Narendra Mahalle
language: en
Publisher: Springer Nature
Release Date: 2021-08-21
This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Within the overarching theme of “Managing the Digital Transformation of Construction Industry” the 23rd International Conference on Construction Applications of Virtual Reality (CONVR 2023) presented 123 high-quality contributions on the topics of: Virtual and Augmented Reality (VR/AR), Building Information Modeling (BIM), Simulation and Automation, Computer Vision, Data Science, Artificial Intelligence, Linked Data, Semantic Web, Blockchain, Digital Twins, Health & Safety and Construction site management, Green buildings, Occupant-centric design and operation, Internet of Everything. The editors trust that this publication can stimulate and inspire academics, scholars and industry experts in the field, driving innovation, growth and global collaboration among researchers and stakeholders.