Majosta DATA-DRIVEN CONSTRUCTION: Navigating the Data Age in the Construction Industry

SUMMARIZED DESCRIPTION OF PARTS OF THE BOOK This book is organized around the concept of transforming data in the value chain: from data collection and quality assurance to analytical processing and extracting valuable practical solutions using modern tools and methodologies. Part 1: Digital Evolution in Construction - Traces the historical transformation of data management from clay tablets to modern digital systems, analyzing the emergence of modular systems and the growing importance of information digitalization in the context of industrial revolutions. Part 2: Information Challenges for the Construction Industry - explores the problems of data fragmentation, 'information silos', the impact of the HiPPO approach on decision making and the limitations of proprietary formats, suggesting consideration of the move to AI and LLM ecosystems. Part 3: Data Systematization in Construction - forms a typology of construction data, describes methods of its organization, integration with corporate systems and discusses the creation of competence centers for standardization of information processes. Part 4: Data Quality Assurance - reveals methodologies for turning disparate information into quality, structured data, including data extraction from various sources, validation, and modeling using LLM. Part 5: Cost and Time Calculations - covers digitalization of cost and scheduling calculations, automation of obtaining volumes from CAD (BIM) models, 4D-8D modeling technologies, and ESG calculations for construction projects. Part 6: CAD and BIM - Critically analyzes the evolution of design technologies, system interoperability issues, trends toward open data formats, and the prospects for applying artificial intelligence to design. Part 7: Data Analytics and Automation - examines the principles of information visualization, key performance indicators, ETL processes, workflow orchestration tools, and the application of language models to automate routine tasks. Part 8: Data Storage and Management - explores data storage formats, data warehouse and data lake concepts, data management principles, and new approaches including vector databases and the DataOps and VectorOps methodologies. Part 9: Big Data and Machine Learning - focuses on the transition to objective analysis based on historical data, the Internet of Things on construction sites, and the application of machine learning algorithms to predict project costs and timelines. Part 10: The Construction Industry in the Age of Digital Data - presents a look at the future of the construction industry, analyzing the shift from causal analysis to working with correlations, the concept of "Uberizing" construction, and strategies for digital transformation.