| data analysis in construction: Data Analytics for Engineering and Construction Project Risk Management Ivan Damnjanovic, Kenneth Reinschmidt, 2019-05-23 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. |
| data analysis in construction: Data Construction and Data Analysis for Survey Research Raymond Kent, 2020-05-19 Based on the needs of business, marketing and social science students, this book provides an accessible and easy-to-follow guide to survey data analysis. The book avoids the more usual statistics-driven perspective. Rather, it provides an intuitive feel for handling survey data by emphasising that data analysis is more about understanding the research objectives, what kinds of data have been collected, how quantitative data were generated by the process of measurement, how to create a data matrix and how to transform raw data before thinking about appropriate statistical calculations. The author begins by considering what data are, the different kinds of data that surveys generate, and the tripartite format of all quantitative data. The processes of measurement and the errors that may arise are carefully explained. Coverage of the creation and analysis of a data matrix using SPSS is then provided. The author then reviews what kinds of statistical procedure are required for what kinds of data and for what purposes. The book concludes by giving a review of strategies for coping with all the kinds of practical problems that arise in survey research. Reader-friendly throughout, Data Analysis: A Guide to Market and Social Research provides numerous worked examples, end-of-chapter questions and points for further discussion to help reinforce concepts learnt. A data diskette containing the worked examples in SPSS is also provided in the back of the book. Also available is a companion website with extra features to accompany the text, please take a look by clicking below - http://www.palgrave.com/business/kent/index.asp |
| data analysis in construction: Development Research in Practice Kristoffer Bjärkefur, Luíza Cardoso de Andrade, Benjamin Daniels, Maria Ruth Jones, 2021-07-16 Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University |
| data analysis in construction: Social Network Analysis in Construction Stephen Pryke, 2012-07-23 Social Network Analysis in Construction Increasingly demanding and knowledgeable clients in construction require an approach to project management that recognises both the important role played by the client in the definition of a project and the lack of certainty that large and/or complex projects present. Having identified the importance of managing relationships, both analysts and practitioners today need a sophisticated framework and methodology for observing systems and managing the complex relationships in major construction project coalitions. Social Network Analysis in Construction shows how social network analysis (SNA) can be used to observe, monitor and analyse systems and relationships. Although this has been an established analytical technique in the US for some time, it is only now being developed in the UK. Stephen Pryke spent nearly two decades investigating major project relationships using SNA and brings together here mathematical and sociological methods, and major project relationships in a manner that will inspire both academic interest and a desire to apply these concepts and techniques to live construction projects. Case studies include projects from two of the UK’s largest property developers – the UK Ministry of Defence, and a County Council. SNA is innovative – but potentially inaccessible to project management analysts and practitioners. The author provides clear and relevant explanation and illustration of the possibilities of using SNA in a major project environment. In addition to offering the potential for sophisticated retrospective analysis of a wide range of systems associated with construction and engineering project coalitions, he also looks at how we might apply the network analysis findings to the design and management of project and supply chain networks. Postgraduate students and academic researchers in Project Management and Construction Management, as well as practitioners from professional consultancies and project management companies will find here an excellent exposition of an often inaccessible subject. |
| data analysis in construction: Delay Analysis in Construction Contracts P. John Keane, Anthony F. Caletka, 2015-06-29 The most significant unanticipated costs on many construction projects are the financial impacts associated with delay and disruption to the works. Assessing these, and establishing a causal link from each delay event to its effect, contractual liability and the damages experienced as a direct result of each event, can be difficult and complex. This book is a practical guide to the process of delay analysis and includes an in-depth review of the primary methods of delay analysis, together with the assumptions that underlie the precise calculations required in any quantitative delay analysis. The techniques discussed can be used on projects of any size, under all forms of construction contract, both domestic and international. The authors discuss not only delay analysis techniques, but also their appropriateness under given circumstances, demonstrating how combined approaches may be applied where necessary. They also consider problematic issues including ‘who owns the float’, concurrent delay, early completion programmes, and disruption. The book has been brought fully up to date, including references to the latest publications from the CIOB, AACEI and SCL, as well as current case law. Broad in scope, the book discusses the different delay analysis approaches likely to be encountered on national and international projects, and features practical worked examples and case studies demonstrating the techniques commonly used by experienced practitioners. This is an invaluable resource to programmers and schedulers, delay analysts, contractors, architects, engineers and surveyors. It will also be of interest to clients’ professional advisors managing extension of time or delay claims, as well as construction lawyers who require a better understanding of the underlying assumptions on which many quantitative delay analyses are based. Reviews of First Edition John Keane and Anthony Caletka are pukka analysts in that tricky area of delays, programming and extension of time. I highly recommend their book Delay Analysis in Construction Contracts. Buy the book. (Building Magazine, February 2009) The book′s stated purpose is to provide a practical guide for those interested in schedule delay analysis. It provides a good in–depth review of the most common delay analysis techniques.... An excellent book, full of practical tips for the reader and very timely in its publication. It is well worth the cost and a good read for anyone involved in schedule delay analysis. (Cost Engineering, February 2009) It achieves in spades its stated aim of being a practical guide for contractors, contract administrators, programmers and delay analysts, as well as construction lawyers who require a better understanding of the underlying assumptions on which many quantitative delay analyses are based. (Construction Law Journal, 2009) |
| data analysis in construction: Planning, Construction, and Statistical Analysis of Comparative Experiments Francis G. Giesbrecht, Marcia L. Gumpertz, 2011-09-26 A valuable guide to conducting experiments and analyzing dataacross a wide range of applications Experimental design is an important component of the scientificmethod. This book provides guidance on planning efficientinvestigations. It compiles designs for a wide range ofexperimental situations not previously found in accessible form.Focusing on applications in the physical, engineering, biological,and social sciences, Planning, Construction, and StatisticalAnalysis of Comparative Experiments is a valuable guide todesigning experiments and correctly analyzing and interpreting theresults. The authors draw on their years of experience in theclassroom and as statistical consultants to research programs oncampus, in government, and in industry. The object is always tostrike the right balance between mathematical necessities andpractical constraints. Serving both as a textbook for students of intermediatestatistics and a hands-on reference for active researchers, thetext includes: A wide range of applications, including agricultural sciences,animal and biomedical sciences, and industrial engineeringstudies General formulas for estimation and hypothesis testing,presented in a unified and simplified manner Guidelines for evaluating the power and efficiency of designsthat are not perfectly balanced New developments in the design of fractional factorials withnon-prime numbers of levels in mixed-level fractionalfactorials Detailed coverage on the construction of plans and therelationship among categories of designs Thorough coverage of balanced, lattice, cyclic, and alphadesigns Strategies for sequences of fractional factorials Data sets and SAS® code on a companion web site An ideal handbook for the investigator planning a researchprogram, the text comes complete with detailed plans of experimentsand alternative approaches for added flexibility. |
| data analysis in construction: Data Analytics for Engineering and Construction Project Risk Management Ivan Damnjanovic, Kenneth Reinschmidt, 2020 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. |
| data analysis in construction: Access Data Analysis Cookbook Ken Bluttman, Wayne S. Freeze, 2007-05-14 If you have large quantities of data in a Microsoft Access database, and need to study that data in depth, this book is a data cruncher's dream. Access Data Analysis Cookbook offers practical recipes to solve a variety of common problems that users have with extracting Access data and performing calculations on it. Each recipe includes a discussion on how and why the solution works. Whether you use Access 2007 or an earlier version, this book will teach you new methods to query data, different ways to move data in and out of Access, how to calculate answers to financial and investment issues, and more. Learn how to apply statistics to summarize business information, how to jump beyond SQL by manipulating data with VBA, how to process dates and times, and even how to reach into the Excel data analysis toolkit. Recipes demonstrate ways to: Develop basic and sophisticated queries Apply aggregate functions, custom functions, regular expressions, and crosstabs Apply queries to perform non-passive activities such as inserting, updating, and deleting data Create and manipulate tables and queries programmatically Manage text-based data, including methods to isolate parts of a string and ways to work with numbers that are stored as text Use arrays, read and write to the Windows registry, encrypt data, and use transaction processing Use the FileSystemObject, use XML with XSLT, communicate with SQL Server, and exchange data with other Office products Find answers from time-based data, such as how to add time, count elapsed time, work with leap years, and how to manage time zones in your calculations Deal with business and finance problems, including methods for calculating depreciation, loan paybacks, and Return on Investment (ROI) Explore statistical techniques, such as frequency, variance, kurtosis, linear regression, combinations and permutations Access Data Analysis Cookbook is a one-stop-shop for extracting nuggets of valuable information from your database, and anyone with Access experience will benefit from these tips and techniques, including seasoned developers. If you want to use your data, and not just store it, you'll find this guide indispensable. |
| data analysis in construction: Research Methods for Construction Richard F. Fellows, Anita M. M. Liu, 2015-07-07 Research Methods for Construction will help you instil rigour into your problem-solving, and into your reports and publications. It will be of value to construction, surveying, architecture and civil engineering students undertaking research, whether for bachelors and masters degree dissertations, or for masters and doctoral research degree theses. Now in its Fourth Edition, this remains one of the few books to provide guidance on research formulation, methodologies, and methods specifically for construction students. Three main sections – Producing a Proposal, Executing the Research and Reporting the Results discuss the key issues in research and examine the primary approaches, both qualitative and quantitative. The methods adopted for scientific and engineering experiments, model building and simulations are discussed, as well as those employed for research into management, social and economic issues. The authors examine the requirements for data and analysis, including the important statistical considerations and a range of qualitative techniques that enable construction researchers to appreciate what needs to be evaluated in devising how research may be carried out effectively and efficiently. This new edition has been updated to reflect current debates and concerns, including ethical issues, legislation and codes of practice concerning the collection, processing, storage, use and disposal of data. Pressures of time and funding to carry out the empirical work all too often lead to a lack of attention to how the study should be done and why. The authors address the importance of explaining the philosophical approach adopted (ontology, epistemology) and the consequent methodology. They advocate close scrutiny of the methods available for appropriateness, both academically and practically. The fundamental theme of the book remains to facilitate a researcher’s informed and justified selection of a philosophical paradigm and of appropriate methods to execute the research. |
| data analysis in construction: IEIS 2020 Menggang Li, Gábor Bohács, Guowei Hua, Daqing Gong, Xiaopu Shang, 2022-04-04 This book contains selected papers of the International Conference on Industrial Economics Systems and Industrial Security Engineering (IEIS 2020), which is co-organized by Beijing Jiaotong University, Budapest University of Technology and Economics, in July 25–28 2020. This book aims to provide new research methods, theories and applications from various areas of industrial economics and engineering. In detail the included scientific papers analyze and describe communication processes in the fields of industrial economics, industrial system, industrial security and engineering and other related areas. The variety of papers delivers added value for both scholars and practitioners. |
| data analysis in construction: Data-Driven Design and Construction Randy Deutsch, 2015-10-12 “In this comprehensive book, Professor Randy Deutsch has unlocked and laid bare the twenty-first century codice nascosto of architecture. It is data. Big data. Data as driver. . .This book offers us the chance to become informed and knowledgeable pursuers of data and the opportunities it offers to making architecture a wonderful, useful, and smart art form.” —From the Foreword by James Timberlake, FAIA Written for architects, engineers, contractors, owners, and educators, and based on today’s technology and practices, Data-Driven Design and Construction: 25 Strategies for Capturing, Applying and Analyzing Building Data addresses how innovative individuals and firms are using data to remain competitive while advancing their practices. seeks to address and rectify a gap in our learning, by explaining to architects, engineers, contractors and owners—and students of these fields—how to acquire and use data to make more informed decisions. documents how data-driven design is the new frontier of the convergence between BIM and architectural computational analyses and associated tools. is a book of adaptable strategies you and your organization can apply today to make the most of the data you have at your fingertips. Data-Driven Design and Construction was written to help design practitioners and their project teams make better use of BIM, and leverage data throughout the building lifecycle. |
| data analysis in construction: Construction Analytics Mohsen Shahandashti, Bahram Abediniangerabi, Ehsan Zahed, Sooin Kim, 2023-04-24 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. |
| data analysis in construction: Data Analytics in Project Management Seweryn Spalek, J. Davidson Frame, Yanping Chen, Carl Pritchard, Alfonso Bucero, Werner Meyer, Ryan Legard, Michael Bragen, Klas Skogmar, Deanne Larson, Bert Brijs, 2019-01-01 Data Analytics in Project Management. Data analytics plays a crucial role in business analytics. Without a rigid approach to analyzing data, there is no way to glean insights from it. Business analytics ensures the expected value of change while that change is implemented by projects in the business environment. Due to the significant increase in the number of projects and the amount of data associated with them, it is crucial to understand the areas in which data analytics can be applied in project management. This book addresses data analytics in relation to key areas, approaches, and methods in project management. It examines: • Risk management • The role of the project management office (PMO) • Planning and resource management • Project portfolio management • Earned value method (EVM) • Big Data • Software support • Data mining • Decision-making • Agile project management Data analytics in project management is of increasing importance and extremely challenging. There is rapid multiplication of data volumes, and, at the same time, the structure of the data is more complex. Digging through exabytes and zettabytes of data is a technological challenge in and of itself. How project management creates value through data analytics is crucial. Data Analytics in Project Management addresses the most common issues of applying data analytics in project management. The book supports theory with numerous examples and case studies and is a resource for academics and practitioners alike. It is a thought-provoking examination of data analytics applications that is valuable for projects today and those in the future. |
| data analysis in construction: Construction Cost Analysis and Estimating Phillip F. Ostwald, 2001 This work provides principles & techniques for the evaluation of construction design, emphasizing the importance of strong analysis skills & exploring estimation. It aims to provide readers with a balanced & cohesive overview of these two areas. |
| data analysis in construction: Feature Extraction, Construction and Selection Huan Liu, Hiroshi Motoda, 2012-12-06 There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem. |
| data analysis in construction: Quantitative Risk Management and Decision Making in Construction Amarjit Singh, 2017 Singh introduces valuable techniques for weighing and evaluating alternatives in decision making with a focus on risk analysis for identifying, quantifying, and mitigating risks associated with construction projects. |
| data analysis in construction: Advances and Technologies in Building Construction and Structural Analysis Alireza Kaboli, Sara Shirowzhan, 2021-12-22 This Edited Volume “Advances and Technologies in Building Construction and Structural Analysis” is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of advances and technologies in building construction and structural analysis. The book comprises single chapters authored by various researchers and edited by an expert active in the alternative medicine research area. All chapters are complete in themselves but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on advances and technologies in building construction and structural analysis and opening new possible research paths for further novel developments. |
| data analysis in construction: Megaproject Risk Analysis and Simulation Prince Boateng, Zhen Chen, Stephen O. Ogunlana, 2017-04-28 Providing new knowledge on risk analysis and simulation for megaprojects, this book is essential reading for both academics and practitioners. Its focus is on technical descriptions of a newly developed dynamic systems approach to megaproject risk analysis and simulation. |
| data analysis in construction: Data Mining and Business Analytics with R Johannes Ledolter, 2013-05-28 Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences. |
| data analysis in construction: RSMeans Cost Data RSMeans, 2012-02-21 RSMeans Cost Data RSMeans Cost Data for Students RSMeans Cost Data, Student Edition provides a thorough introduction to cost estimating in a self-contained print and online package. With clear explanations and a hands-on, example-driven approach, it is the ideal reference for students and new professionals who need to learn how to perform cost estimating for building construction. Features include: Commercial and residential construction cost data in print and online formats Complete how-to guidance on the essentials of cost estimating A supplemental website with plans, specifications, problem sets, and a full sample estimate With more than 930 Location Factors in the United States and Canada, the data includes up-to-date system prices for more than 100 standard assemblies and in-place costs for thousands of alternates—making it easy to customize budget estimates and compare system costs. UNIT PRICES (organized in MasterFormatTM 2010) 1 General Requirements 2 Existing Conditions 3 Concrete 4 Masonry 5 Metals 6 Woods, Plastics & Composites 7 Thermal & Moisture Protection 8 Openings 9 Finishes 10 Specialties 11 Equipment 12 Furnishings 13 Special Construction 14 Conveying Equipment 21 Fire Suppression 22 Plumbing 23 Heating, Ventilating & Air Conditioning 26 Electrical 27 Communications 28 Electronic Safety & Security 31 Earthwork 32 Exterior Improvements 33 Utilities ASSEMBLIES A Substructure B Shell C Interiors D Services E Equipment & Furnishings F Special Construction G Building Site Work REFERENCE INFORMATION Equipment Rental Costs Crews Cost Indexes Reference Tables Square Foot Costs RSMeans is the leading source of cost data for construction in North America. Visit rsmeans.com to learn more. |
| data analysis in construction: Data Analytics Applied to the Mining Industry Ali Soofastaei, 2020-11-12 Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors |
| data analysis in construction: A Practical Approach to Microarray Data Analysis Daniel P. Berrar, Werner Dubitzky, Martin Granzow, 2002-12-31 In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships. |
| data analysis in construction: Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate Jing Li, Weisheng Lu, Yi Peng, Hongping Yuan, Daikun Wang, 2023-08-07 This book presents the proceedings of CRIOCM 2022 (27th International Conference on Advancement of Construction Management and Real Estate), sharing the latest developments in real estate and construction management around the globe. The conference was organized by the Chinese Research Institute of Construction Management (CRIOCM) working in close collaboration with The Chinese University of Hong Kong. Written by international academics and professionals, the book discusses the latest achievements, research findings, and advances in frontier disciplines in the field of construction management and real estate. Covering a wide range of topics, including spatial planning and land use innovation, integration and application of BIM and GIS, low-carbon built environment, post-pandemic resilient cities development, housing and social governance, real estate market and urban policy, real estate finance and economics, intelligent construction and smart city, built environment for healthy living, and construction management in the post-COVID-19 era, the discussions provide valuable insights into the implementation of advanced construction project management and real estate market in China and abroad. The book offers an outstanding resource for academics and professionals |
| data analysis in construction: Schedule Delay Analysis American Society of Civil Engineers, 2017 Standard ANSI/ASCE/CI 67-17 presents 35 guiding principles that can be used on construction projects to assess responsibility for delays and to calculate associated damages. |
| data analysis in construction: Real Estate Analysis in the Information Age Kimberly Winson-Geideman, Andy Krause, Clifford A. Lipscomb, Nick Evangelopoulos, 2017-11-09 The creation, accumulation, and use of copious amounts of data are driving rapid change across a wide variety of industries and academic disciplines. This ‘Big Data’ phenomenon is the result of recent developments in computational technology and improved data gathering techniques that have led to substantial innovation in the collection, storage, management, and analysis of data. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling focuses on the real estate discipline, guiding researchers and practitioners alike on the use of data-centric methods and analysis from applied and theoretical perspectives. In it, the authors detail the integration of Big Data into conventional real estate research and analysis. The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material. The running theme is the construction of efficient, transparent, and reproducible research through the systematic organization and application of data, both traditional and 'big'. The final chapters investigate legal issues, particularly related to those data that are publicly available, and conclude by speculating on the future of Big Data in real estate. |
| data analysis in construction: Structural Analysis and Design of Tall Buildings Bungale S. Taranath, 2016-04-19 As software skills rise to the forefront of design concerns, the art of structural conceptualization is often minimized. Structural engineering, however, requires the marriage of artistic and intuitive designs with mathematical accuracy and detail. Computer analysis works to solidify and extend the creative idea or concept that might have started o |
| data analysis in construction: Lean Construction Management Shang Gao, Sui Pheng Low, 2014-05-23 The book presents a mixed research method adopted to assess and present the Toyota Way practices within construction firms in general and for firms in China specifically. The results of an extensive structured questionnaire survey based on the Toyota Way-styled attributes identified were developed and data collected from building professionals working in construction firms is presented. The quantitative data presented in the book explains the status quo of the Toyota Way-styled practices implemented in the construction industry, as well as the extent to which these attributes were perceived for lean construction management. The book highlights all the actionable attributes derived from the Toyota Way model appreciated by the building professionals, but alerts the readers that some attributes felled short of implementation. Further findings from in-depth interviews and case studies are also presented in the book to provide to readers an understanding how these Toyota Way practices can be implemented in real-life projects. Collectively, all the empirical findings presented in this book can serve to enhance understanding of Toyota Way practices in the lean construction management context. The readers are then guided through to understand the gaps between actual practice and Toyota Way-styled practices, and the measures that they may undertake to circumvent the challenges for implementation. The book also presents to readers the SWOT analysis that addresses the strengths, weaknesses, opportunities and threats towards the implementation of the Toyota Way in the construction industry. The book prescribes the Toyota Way model for use in construction firms to strategically implement lean construction management. The checklist presented in the book enables readers to draw lessons that may be used additionally as a holistic assessment tool for measuring the maturity of firms with respect to their Toyota Way implementation. Consequent to this, management would then be in a better position to develop plans for Toyota Way implementation by focusing on weak areas, strengthening them, and thus increasing the likelihood of success in the implementation of the Toyota Way. In a nutshell, this book provides a comprehensive and valuable resource for firms not only in the construction industry but also businesses outside of the construction sector to better understand the Toyota Way and how this understanding can translate to implementation of lean construction/business management to enhance profitability and survivability in an increasingly competitive global market place. |
| data analysis in construction: Proceedings of the 18th International Conference on Computing in Civil and Building Engineering Eduardo Toledo Santos, Sergio Scheer, 2020-07-14 This book gathers the latest advances, innovations, and applications in the field of information technology in civil and building engineering, presented at the 18th International Conference on Computing in Civil and Building Engineering (ICCCBE), São Paulo, Brazil, August 18-20, 2020. It covers highly diverse topics such as BIM, construction information modeling, knowledge management, GIS, GPS, laser scanning, sensors, monitoring, VR/AR, computer-aided construction, product and process modeling, big data and IoT, cooperative design, mobile computing, simulation, structural health monitoring, computer-aided structural control and analysis, ICT in geotechnical engineering, computational mechanics, asset management, maintenance, urban planning, facility management, and smart cities. Written by leading researchers and engineers, and selected by means of a rigorous international peer-review process, the contributions highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaborations. |
| data analysis in construction: The Construction Contracts Book Carrie Okizaki, Carson Fisk, David A. Scotti, 2020 Annotated analysis and comparison of the AI, ConsensusDocs, and EJCDC contract forums-- |
| data analysis in construction: Complex Surveys Thomas Lumley, 2011-09-20 A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems. Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work. |
| data analysis in construction: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data analysis in construction: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| data analysis in construction: , |
| data analysis in construction: BIM in the Construction Industry Hee Sung Cha, Shaohua Jiang, 2021-01-15 This book contains 19 peer-reviewed papers on the subject of BIM in the construction industry. These articles cover recent advances in the development of BIM technologies and applications in the field of architecture, engineering, and construction (AEC) industry. |
| data analysis in construction: Data Analysis in Qualitative Research Stefan Timmermans, Iddo Tavory, 2022-06-06 From two experts in the field comes an accessible, how-to guide that will help researchers think more productively about the relation between theory and data at every stage of their work. In Data Analysis in Qualitative Research, Iddo Tavory and Stefan Timmermans provide a how-to guide filled with tricks of the trade for researchers who hope to take excellent qualitative data and transform it into powerful scholarship. In their previous book, Abductive Analysis: Theorizing Qualitative Research, Timmermans and Tavory offered a toolkit for innovative theorizing in the social sciences. In this companion, they go one step further to show how to uncover the surprising revelations that lie waiting in qualitative data—in sociology and beyond. In this book, they lay out a series of tools designed to help both novice and expert scholars see and understand their data in surprising ways. Timmermans and Tavory show researchers how to “stack the deck” of qualitative research in favor of locating surprising findings that may lead to theoretical breakthroughs, whether by engaging with theory, discussing research strategies, or walking the reader through the process of coding data. From beginning to end of a research project, Data Analysis in Qualitative Research helps social scientists pinpoint the most promising paths to take in their approach. |
| data analysis in construction: BIM and Big Data for Construction Cost Management Weisheng Lu, Chi Cheung Lai, Tung Tse, 2018-10-11 This book is designed to help practitioners and students in a wide range of construction project management professions to understand what building information modelling (BIM) and big data could mean for them and how they should prepare to work successfully on BIM-compliant projects and maintain their competencies in this essential and expanding area. In this book, the state-of-the-art information technologies that support high-profile BIM implementation are introduced, and case studies show how BIM has integrated core quantity surveying and cost management responsibilities and how big data can enable informed decision-making for cost control and cost planning. The authors' combined professional and academic experience demonstrates, with practical examples, the importance of using BIM and particularly the fusion of BIM and big data, to sharpen competitiveness in global and domestic markets. This book is a highly valuable guide for people in a wide range of construction project management and quantity surveying roles. In addition, implications for project management, facilities management, contract administration, and dispute resolution are also explored through the case studies, making this book essential reading for built environment and engineering professionals. |
| data analysis in construction: Class , 1928 |
| data analysis in construction: Statistical Data Analysis Glen Cowan, 1998 This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are takenfrom particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques,statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding). |
| data analysis in construction: Project Management for Construction Chris Hendrickson, Tung Au, 1989 |
| data analysis in construction: Computational Topology for Data Analysis Tamal Krishna Dey, Yusu Wang, 2022-03-10 Topological data analysis (TDA) has emerged recently as a viable tool for analyzing complex data, and the area has grown substantially both in its methodologies and applicability. Providing a computational and algorithmic foundation for techniques in TDA, this comprehensive, self-contained text introduces students and researchers in mathematics and computer science to the current state of the field. The book features a description of mathematical objects and constructs behind recent advances, the algorithms involved, computational considerations, as well as examples of topological structures or ideas that can be used in applications. It provides a thorough treatment of persistent homology together with various extensions – like zigzag persistence and multiparameter persistence – and their applications to different types of data, like point clouds, triangulations, or graph data. Other important topics covered include discrete Morse theory, the Mapper structure, optimal generating cycles, as well as recent advances in embedding TDA within machine learning frameworks. |
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