This text covers R program coding for the implementation of two essential data analytics for practical construction problems. The first part of this book explains time series basics, models, and forecasting approaches in the context of the construction industry, accompanied by practical examples in construction. The second part describes the concept of investment valuation for construction projects and provides both deterministic and probabilistic techniques to conduct investment valuation on construction projects. R code scripts are provided in this book for solving practical problems in the construction industry. This book is also equipped with an R Package entitled "cdar" to provide the necessary functions for performing investment valuation. The book maximizes students' understanding of the necessary theoretical background of data analytics, and explains the implementation of data analytics techniques to solve the actual problems in the construction industry.
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Zielgruppe
Illustrationen
Maße
Höhe: 241 mm
Breite: 160 mm
Dicke: 16 mm
Gewicht
ISBN-13
978-3-031-27291-2 (9783031272912)
DOI
10.1007/978-3-031-27292-9
Schweitzer Klassifikation
Dr. Mohsen Shahandashti is an associate professor in the Department of Civil Engineering at the University of Texas at Arlington (UTA). He joined UTA immediately after receiving Ph.D. from the Georgia Institute of Technology. He is a licensed Professional Engineer (PE) in Civil Engineering in Texas. He has published 49 peer-reviewed journal articles in the leading infrastructure and construction journals and has received several awards, including the 2019 AASHTO High-Value Research "Sweet Sixteen" Award for leading the TxDOT Research Project "Exploring Rapid Repair Methods for Embankment Slope Failure" and the best paper award in the category of Optimization and Computational Techniques from the International Associations for Automation and Robotics in Construction.
Dr. Bahram Abediniangerabi is a senior data scientist working in the banking industry. He obtained his Ph.D. in Civil Engineering with a focus on Construction Data Analytics from the University of Texas atArlington in 2019. He is involved in several research projects related to time series forecasting, natural language processing, and machine learning applications in construction. His interest is mainly focused on open-source projects related to Conversational AI, social media analytics, and quantitative finance. He has published several research papers in various areas, such as construction time series forecasting, investment valuation under uncertainty, and building energy use prediction.
Dr. Ehsan Zahed Ph.D., P.E. is a multidisciplinary professional engineer with demonstrated experience in Civil Engineering practice, research, and education. He obtained a B.Sc. degree in Water Science and Technology and an M.Sc. degree in Hydraulic Structures focusing on river engineering and the hydraulic design of channels, dams, and bridges, and then received his master's and Ph.D. degrees in Construction Engineering and Management from the University of Texas at Arlington. Dr. Zahed has published several technical reports and scientific papers in various areas, including but not limited to the financial and economic analytics of civil-infrastructure projects, Investment valuation of construction projects under uncertainty, transportation asset management, and evaluation of innovative transportation systems, such as underground freight transportation systems.
Sooin Kim is a research scientist, a Ph.D. candidate, and an adjunct faculty in the Department of Civil Engineering at the University of Texas at Arlington. She is actively involved in research related to construction data analytics and infrastructure resilience using advanced econometrics and statistical approaches with her academic background in Economics. She has designed and implemented econometrics and machine learning methodologies for cost estimating, financing, and market forecasting. She also develops data-analytical frameworks to investigate geospatial and temporal post-disaster market changes and extends the frameworks to a larger extent considering disaster mitigation policies and socioeconomic variables with recent advances in econometrics, computer sciences, and operations research.
