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An indicator to quantify the complexity of signals and surfaces based on scaling behaviors transcending fractal

CHAOS SOLITONS & FRACTALS 2022年 163卷

作者： Li, Zhiwei Wang, Jianjian Yuan, Meng Wang, Zhongyu Feng, Pingfa Feng, Feng Tsinghua Univ Shenzhen Int Grad Sch Div Adv Mfg Shenzhen 518055 Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China

In the well-established fractal scheme, the fractal dimension (D) is a central indicator of the complexity of fractal features. The D values of non-fractal signals and surfaces are 1 and 2, respectively, while there can be varieties in their complexities. In this study, the scaling characteristics of root-mean-squared roughness could exhibit a continuous variation transcending the boundary between fractal and non-fractal by using the roughness scaling extraction (RSE) method proposed in previous study, and an universal indicator (H-RSE, Hurst exponent calculated by RSE method) to quantify the complexity of both fractal and non-fractal features is demonstrated. The actual signals (milling vibration) and surfaces (silver thin films) together with the artificial ones generated through Weierstrass-Mandelbrot (W-M) function were analyzed. Within the fractal scope, the calculated results with RSE method could be close to the ideal D values of W-M function with an accuracy higher than those of the traditional fractal methods (including Box-Counting, Higuchi, Katz, power spectral density, structure function, and autocorrelation function methods). For the non-fractal features, the complexity could also be quantified effectively by H-RSE. Chatter could be recognize by H-RSE of milling vibration signals, because it was larger than 1, from 0.5 to 1, and less than 0.5 in idling, stable milling and chatter milling states, respectively;For thin film surfaces, H-RSE increased monotonically from 0.79 to 1.32 along with Sq increasing, indicating a strong positive correlation. The findings indicated that the scaling analysis could be utilized for both fractal and non-fractal features, which would be beneficial for various engineering applications.

关键词： Roughness scaling characteristics Fractal dimension complexity indicator Hurst exponent Weierstrass-Mandelbrot function Milling vibration signals Thin film surfaces

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Development of complexity management strategies for construction projects

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2023年第6期21卷 1633-1657页

作者： Kermanshachi, Sharareh Nipa, Thahomina Jahan Bac Dao Univ Texas Arlington Dept Civil Engn Arlington TX 76019 USA FPT Univ FPT Sch Business & Technol Hanoi Vietnam

Purpose The purpose of this study is to ascertain and list the most effective management strategies in efficiently handling the project complexities to enhance the performance of the project. Design/methodology/approach To fulfill the aim of this study, a comprehensive literature review was conducted, and the qualitative Delphi technique in two rounds was applied. Participants of the Delphi technique consisted of 12 subject matter experts (SMEs) with cumulative experience of 250 years in working in construction projects. In the first round of the Delphi technique, SMEs were asked to provide complexity management strategies to address the complexities due to 37 complexity indicators (CIs) under 11 complexity categories. In the second round of the Delphi technique, SMEs identified the top three management strategies for each of the 37 CIs. Findings This study collected the outcome of the two-round Delphi technique and based on the output developed the list of strategies to manage complexities related to each indicator. For example, establishing a well-informed governance team, assigning a Project Manager (PM) when the number of projects is more than one in an organization, and assigning a PM efficient enough to communicate with higher authority effectively will help in managing complexity that arises due to faulty assessment of the influence of a project on the organization's overall success. Originality/value This study will help practitioners in effectively managing the project complexities, and thus will reduce the monetary loss associated with project complexities.

关键词： Delphi technique Project complexity complexity indicator complexity management strategies

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Developing a logistic regression model to measure project complexity

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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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Design evaluation of automated manufacturing processes based on complexity of control logic

Design evaluation of automated manufacturing processes based...

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26th CIRP Design Conference

作者： Alkan, Bugra Vera, Daniel Ahmad, Mussawar Ahmad, Bilal Harrison, Robert Univ Warwick WMG Automat Syst Grp Coventry CV4 7AL W Midlands England

complexity continues to be a challenge in manufacturing systems, resulting in ever-inflating costs, operational issues and increased lead times to product realisation. Assessing complexity realizes the reduction and management of complexity sources which contributes to lowering associated engineering costs and time, improves productivity and increases profitability. This paper proposes an approach for evaluating the design of automated manufacturing processes based on the structural complexity of the control logic. Six complexity indices are introduced and formulated: Coupling, Restrictiveness, Diameter, Branching, Centralization, and Uncertainty. An overall Logical complexity Index (C-L) which combines all of these indices is developed and demonstrated using a simple pick and place automation process. The results indicate that the proposed approach can help design automation logics with the least complexity and compare alternatives that meet the requirements during initial design stages. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Process modelling design efficiency complexity indicator complexity management

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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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Design Evaluation of Automated Manufacturing Processes Based on complexity of Control Logic

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Procedia CIRP 2016年 50卷 141-146页

作者： Bugra Alkan Daniel Vera Mussawar Ahmad Bilal Ahmad Robert Harrison Automation Systems Group WMG University of Warwick CV4 7AL Coventry West Midlands UK

complexity continues to be a challenge in manufacturing systems, resulting in ever-inflating costs, operational issues and increased lead times to product realisation. Assessing complexity realizes the reduction and management of complexity sources which contributes to lowering associated engineering costs and time, improves productivity and increases profitability. This paper proposes an approach for evaluating the design of automated manufacturing processes based on the structural complexity of the control logic. Six complexity indices are introduced and formulated: Coupling, Restrictiveness, Diameter, Branching, Centralization, and Uncertainty. An overall Logical complexity Index (C L ) which combines all of these indices is developed and demonstrated using a simple pick and place automation process. The results indicate that the proposed approach can help design automation logics with the least complexity and compare alternatives that meet the requirements during initial design stages.

关键词： Process modelling design efficiency complexity indicator complexity management.

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