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Developing a logistic regression model to measure project complexity

ARCHITECTURAL ENGINEERING AND DESIGN MANAGEMENT 2022年第3期18卷 226-240页

作者： Dao, Bac Kermanshachi, Sharareh Shane, Jennifer Anderson, Stuart Damnjanovic, Ivan FPT Univ FPT Sch Business & Technol Hanoi 10000 Vietnam Univ Texas Arlington Dept Civil Engn Arlington TX 76019 USA Iowa State Univ Dept Civil Construct & Environm Engn Ames IA USA Texas A&M Univ Dept Civil Engn College Stn TX 77843 USA

The study develops a binary logistic regression model to assess and measure complexity levels of a project. The complexity measures were statistically verified to create a basis for the model. The variable reduction process called Principle Component Analysis was used to combine the significant complexity indicators into component variables. The study enriches the complexity theoretical basis in the field of project management by providing an innovative approach that aids scholars and practitioners in assessing complexity levels based on the applicability of identified complexity measures. The research results also help facilitate the management process and formulate an appropriate complexity management plan.

关键词： Project complexity complexity attribute complexity indicator logistic regression model

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Identifying and measuring project complexity

Identifying and measuring project complexity

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International Conference on Sustainable Design, Engineering and Construction (ICSDEC)

作者： Dao, Bac Kermanshachi, Sharareh Shane, Jennifer Anderson, Stuart Hare, Eric Texas A&M Univ 3136 TAMU College Stn TX 77843 USA Iowa State Univ 498 Town Engn Ames IA 50011 USA

This study provides a constructive approach to identify and assess project complexity as a separate factor influencing projects. Project complexity was described in terms of managing projects rather than project physical features to ensure the research results can be generalized across different industries. The complexity attributes and indicators deemed to measure those associated attributes were developed. The data collected through a survey was analyzed using statistical methods to test the significance of complexity indicators in differentiating low complexity projects from high complexity projects. The data analysis resulted in 37 complexity indicators associated with 23 attributes statistically significant to project complexity. The research findings help scholars and practitioners in the project management field in developing an appropriate strategy to manage project complexity effectively. (C) 2015 The Authors. Published by Elsevier Ltd.

关键词： Project complexity complexity attribute complexity Indicator Project Management

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Project complexity assessment and management tool

Project complexity assessment and management tool

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International Conference on Sustainable Design, Engineering and Construction (ICSDEC)

作者： Dao, Bac Kermanshachi, Sharareh Shane, Jennifer Anderson, Stuart Texas A&M Univ 3136 TAMU College Stn TX 77843 USA Iowa State Univ 498 Town Engn Ames IA 50011 USA

Project complexity Assessment and Management (PCAM) Tool was developed to help project teams identify, assess, and manage project complexity. This Excel-based tool was designed with a "complexity Measurement Matrix" comprising of the indicators that are statistically significant to project complexity. The weight factors of these complexity indicators were developed based on the expert ranking result from a subject matter workshop. These factors function as the multipliers to place greater emphasis on the stronger complexity indicators. The comprehensive reports from the tool present the overall project complexity level, a series of radar diagrams describing the most important indicators, and associated management strategies. The outputs help project teams formulate a management plan for the most important contributors with sufficient flexibility to be deployed at multiple project stages and for different project sizes. (C) 2015 The Authors. Published by Elsevier Ltd.

关键词： Project complexity complexity attribute complexity Indicator Project Management

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Vigilance Differentiation from EEG complexity attributes 22nd

Vigilance Differentiation from EEG Complexity Attributes

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22nd International Conference on Neural Information Processing (ICONIP)

作者： Li, Junhua Prasad, Indu Dauwels, Justin Thakor, Nitish V. AI-Nashash, Hasan Natl Univ Singapore Ctr Life Sci Singapore Inst Neurotechnol SINAPSE Singapore 117548 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Johns Hopkins Univ Dept Biomed Engn Baltimore MD USA Amer Univ Sharjah Coll Engn Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9783319265612;9783319265605

Vigilance is an ability to maintain concentrated attention on a particular event or target stimulus. Monitoring tasks require certainly high vigilance to properly detect rare occurrence or accurately respond to stimulation. Changes in vigilance can be reflected by EEG signal, so vigilance levels can be classified based on features extracted from EEG. Up to now, power spectral density was commonly employed as features to differentiate between vigilance levels in majority of previous studies. To the best of our knowledge, multifractal attributes for vigilance differentiation have not been exploited, and their feasibility still need to be investigated. In this study, we first extracted multifractal attributes based on wavelet leaders, and then selected statistically significant distinct attributes for the following classification (two vigilance levels). According to the results, classification accuracy was improved with increase of time window used for feature extraction. When time window was increased to 50 s, an averaged accuracy of 91.67% was achieved, and accuracies for all subjects were higher than 85 %. Our results suggest that multifractal attributes are promising for vigilance differentiation.

关键词： Vigilance classification Sustained attention complexity attribute Self-similarity Feature selection

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Exploring and measuring project complexity

Exploring and measuring project complexity

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作者： Dao, Bac Phuong Texas A&M University

学位级别：Ph.D.

Project complexity is a term that is not well understood in the construction industry in relation to academia and practice. Project complexity often refers to a measurement of the number of project elements and interactions between project elements or a relative comparison of difficulty to what was previously accomplished. Project complexity is a very critical factor that presents additional challenges to achieving project objectives. Impacts of project complexity can be negative if it is not assessed and managed appropriately. Developing a methodology to assess and measure project complexity can help project teams increase the likelihood of success and predictable project outcomes. This research proposed a constructive approach to assess and measure project complexity as a separate factor influencing projects including a model that helps identify the levels of project complexity. In this research, first, the concepts of complexity and its attributes were explored, and the literature relevant to defining and assessing project complexity was studied. A working definition of project complexity was then developed as a basis for describing project complexity. Project complexity was intentionally described in terms of managing projects rather than project physical features such as facility technology, types of materials, or project physical components to ensure that the research results could be generalized across a wide range of industry sectors. The possible project complexity attributes were then identified using complexity theory variables, the literature review results, and industry experience. The identified complexity attributes were used to develop the complexity indicators deemed to measure the associated attributes. The developed complexity indicators were then converted to survey questions for data collection purpose. The collected data was analyzed using statistical methods to test the significance of complexity indicators in differentiating low complexity pr

关键词： project complexity complexity attribute complexity indicator Thesis

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Project complexity Assessment and Management Tool

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Procedia Engineering 2016年 145卷 491-496页

作者： Bac Dao Sharareh Kermanshachi Jennifer Shane Stuart Anderson Texas A&M University 3136 TAMU College Station TX 77843-3136 USA Iowa State University 498 Town Engineering Ames IA 50011 USA

Project complexity Assessment and Management (PCAM) Tool was developed to help project teams identify, assess, and manage project complexity. This Excel-based tool was designed with a “complexity Measurement Matrix” comprising of the indicators that are statistically significant to project complexity. The weight factors of these complexity indicators were developed based on the expert ranking result from a subject matter workshop. These factors function as the multipliers to place greater emphasis on the stronger complexity indicators. The comprehensive reports from the tool present the overall project complexity level, a series of radar diagrams describing the most important indicators, and associated management strategies. The outputs help project teams formulate a management plan for the most important contributors with sufficient flexibility to be deployed at multiple project stages and for different project sizes.

关键词： Project complexity complexity attribute complexity Indicator Project Management

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Identifying and Measuring Project complexity

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Procedia Engineering 2016年 145卷 476-482页

作者： Bac Dao Sharareh Kermanshachi Jennifer Shane Stuart Anderson Eric Hare Texas A&M University 3136 TAMU College Station TX 77843-3136 USA Iowa State University 498 Town Engineering Ames IA 50011 USA

关键词： Project complexity complexity attribute complexity Indicator Project Management

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