Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning

Construction projects are highly risk-prone due to both internal factors (e.g., organizational, contractual, project, etc.) and external factors (e.g., environmental, economic, political, etc.). Construction risks can thus have a direct or indirect impact on project objectives, such as cost, time, safety, and quality. Identification of these risks is crucial in order to fulfill project objectives. Many tools and techniques have been proposed for risk identification, including literature review, questionnaire surveys, and expert interviews. However, the majority of these approaches are highly reliant on expert knowledge or prior knowledge of the project. Therefore, the application of such tools and techniques in risk identification for renewable energy projects (e.g., wind farm and solar power plant projects) is challenging due to their novelty and the limited availability of historical data or literature. This paper addresses these challenges by introducing a new risk identification framework for renewable energy projects, which combines case-based reasoning (CBR) with fuzzy logic. CBR helps to solve problems related to novel projects (e.g., renewable energy projects) based on their similarities to existing, well-studied projects (e.g., conventional energy projects). CBR addresses the issue of data scarcity by comparing novel types of construction projects to other well-studied project types and using the similarities between these two sets of projects to solve the different problems associated with novel types of construction projects, such as risk identification of renewable energy projects. Moreover, the integration of fuzzy logic with CBR, to develop fuzzy case-based reasoning (FCBR), increases the applicability of CBR in construction by capturing the subjective uncertainty that exists in construction-related problems. The applicability of the proposed framework was tested on a case study of an onshore wind farm project. The objectives of this paper are to introduce a novel framework for risk identification of renewable energy projects and to identify the risks associated with the construction of onshore wind farm projects at the work package level. The results of this paper will help to improve the risk management of renewable energy projects during the construction phase

Determining Project Contingency Reserve Using a Fuzzy Arithmetic-Based Risk Analysis Method

Traditional techniques for estimating contingency reserve fail to capture subjective uncertainties and expert knowledge, and they rely on historical data. This paper proposes a fuzzy risk analysis model (FRAM) that uses fuzzy arithmetic to analyze risk and opportunity events and determine construction project contingency reserve. The FRAM allows experts to use natural language to assess the probability and impact of risk and opportunity events by employing linguistic scales represented by fuzzy numbers, thus addressing the data reliance problem of probabilistic methods. It enables experts to customize linguistic scales and fuzzy numbers for different project types and stages. The FRAM also deals with the challenges associated with deterministic approaches by addressing measurement imprecision and the subjective uncertainty of experts’ opinions. Moreover, the FRAM allows analysts to estimate contingency at different levels of confidence. This paper also illustrates Fuzzy Risk Analyzer© (FRA©), software that implements the fuzzy arithmetic procedure of the FRAM

Leveraging Innovation for Sustainable Construction: Proceedings of the CIB Task Group 58: Clients and Construction Innovation Workshop

This publication consists of a volume of papers presented at the workshop of the CIB Task Group 58:Clients and Construction Innovation, held on May 18-19, 2009 at the University of Alberta in Edmonton,Canada. The workshop theme, “Leveraging Innovation for Sustainable Construction”, reflects a growing concern among clients for perspectives, approaches,and tools that will secure the practice of construction economically, socially, and environmentally. This collection encompasses some of the most incisive assessments of the challenges facing the construction industry today from a range of researchers and industry practitioners who are leading the way for tomorrow’s innovations. It provides a useful documentation of the ongoing conversation regarding innovation and sustainability issues and a foundation of knowledge for future research and development.The papers contained in this volume explore the workshop’s overarching theme of how to leverage innovation to increase the sustainability of the construction process and product. Participants sought to generate discussion on the topics of innovation and sustainability within the construction field, to share international examples of innovation from the research community and from industry, and to establish a point of reference for ongoing enquiry. In particular, our contributors have noted the value of learning through practice in order to orient research based on real-world industry experience. Chapters two and three present complementary models of sustainable research programs through the three parts collaboration of government, industry, and academia. Chapters four and five explore new tools and forms of technological innovation as they are deployed to improve construction project management and set the direction for advances in research. Chapters six, seven, and eight closely study practical examples of innovation in large-scale construction projects, showing with concrete results the impact of applying creative methods and best practices to the field. Innovation and sustainability in construction are truly global efforts; these papers illustrate how we can draw on international examples and cooperative organizations to address these important issues for long-term benefit of the industry

A Framework for Total Productivity Measurement of Industrial Construction Projects

Productivity measurement is a concern for both construction practitioners and researchers. In construction, productivity can be measured at three levels: activity, project, and industry. At the project level, previous studies focused on measuring the productivity of specific activities. In addition, existing project-level productivity metrics do not consider the effect of all resources used in a project. In order to effectively assess overall project performance, the productivity of all project activities and resources used must be taken into account. This study presents a framework that takes into consideration all resources used in a project and proposes a metric for measuring the total productivity of construction projects. A focus group session with experts, followed by questionnaire surveys, were used to assess the applicability of the framework. This paper makes a contribution by providing researchers and practitioners with a framework and tools for data collection and analysis of total construction project productivity.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Identifying factors affecting motivation of construction crew workers

Motivation is a critical factor affecting construction crew performance. Motivation arises from various internal and external factors such as self-efficacy, assigned goals, and other sources. On construction and industrial projects, workers usually work in crews; thus, performance factors such as productivity are mostly measurable at the crew level. Crew motivation is one factor impacting crew performance. However, it is difficult to define and measure crew motivation in construction, due to the uniqueness and dynamism of the construction environment. Additionally, motivational factors may be described in the form of subjective or objective data. Therefore, a method of measurement is required to systematically and explicitly measure each factor affecting crew motivation and therefore performance. This paper reviews theories and models of motivation that have been developed in research domains other than construction. Next, it overviews motivation literature within the construction domain, and discusses shortcomings of these current approaches. From the literature review, the key factors affecting crew motivation are identified. Finally, the paper proposes a method of measuring crew motivation. The findings and methods presented in this paper will help to define andNon UBCUnreviewedFacultyOthe

Context Adaptation of Fuzzy Inference System-Based Construction Labor Productivity Models

Construction labor productivity (CLP) is one of the most studied areas in the construction research field, and several context-specific predictive models have been developed. However, CLP model development remains a challenge, as the complex impact of multiple subjective and objective influencing variables have to be examined in various project contexts while dealing with limited data availability. On the other hand, lack of a framework for adapting existing or original models from one context to other contexts limits the possibility of reusing existing models. Such challenges are addressed in this paper through the development of a context adaptation framework. The framework is used to transfer the knowledge represented in fuzzy inference (FIS) based CLP models from one context to another, by using linear and nonlinear evolutionary based transformation of the membership functions combined with sensitivity analysis of fuzzy operators and defuzzification methods. Using four context-specific CLP models developed for concreting activity under industrial, warehouse, high-rise, and institutional building project contexts, the framework was implemented, and the prediction capability of the adapted models was evaluated based on their prediction similarity with the original models. The results showed that linearly adapted CLP models for industrial and institutional contexts and nonlinearly adapted CLP models for warehouse and high-rise contexts provide a similar prediction capability with the original models. The proposed context adaptation framework and findings from this paper address the limitations in past context adaptation research by examining a practical context-sensitive application problem and further examining the role of fuzzy operators and defuzzification methods. The findings assist researchers and industry practitioners to take full advantage of existing FIS-based models in the study of new contexts, for which data availability might be limited

A Framework for Identifying and Measuring Competencies and Performance Indicators for Construction Projects

ABSTRACT In contemporary construction environments, employees and managers alike are faced with numerous pressures to carry out work to meet corporate expectations of performance. Continuous change and adaptation in organizational structures, practices, and technologies are conducive to successful management and execution of construction projects. Construction projects tend to measure how well they perform against a set of predefined performance indicators. These performance indicators are based on the ability of construction projects to attain necessary sets of "competencies" that enable successful execution of work. This paper identifies and classifies the different competencies and performance indicators that are used in construction projects and proposes a framework and methodology to identify and measure them. Appropriate measurement scales for the different competencies and performance indicators are developed, and a survey structure is proposed to collect data on the competencies and performance indicators from experts in the construction domain. A data aggregation method is introduced to combine experts' evaluation of construction projects' competencies and performance indicators. Lastly, the paper discusses future work pertaining to the development of a cascade fuzzy neural network that can predict different performance indicators for construction projects based on the identified competencies

Application of fuzzy logic integrated with system dynamics in construction modeling

Construction projects are complex systems and their behaviors are extremely dynamic throughout their life cycles. This complexity and dynamism makes them perfect candidates for system dynamics modeling for management purposes. However, ill-known variables, a lack of historical data, uncertainties, subjectivity, and the use of linguistic terms in defining construction variables all complicate the application of system dynamics in construction. Fuzzy logic is an artificial intelligence technique that has the ability to model vague, incomplete, linguistically-expressed, and subjective data in a precise way. Since the quality of system dynamics modeling relies significantly on the accuracy of the data, integrating system dynamics with fuzzy logic makes for a powerful construction project simulation tool. Integrated fuzzy system dynamics models can effectively capture the dynamic characteristics of construction projects and simulate them more precisely by using fuzzy logic to capture subjective and linguistically-expressed information. In this paper, we illustrate how fuzzy logic and system dynamics can be integrated for use in construction project simulation. Moreover, we present a review of potential applications of integrated fuzzy system dynamics models in construction. Finally, we compare the performance of system dynamics with integrated fuzzy system dynamics for a construction-related problem adopted from the literature, and discuss how integrating fuzzy logic can enhance system dynamics capabilities for construction modeling.Non UBCUnreviewedFacultyOthe

Competency and Performance Measures for Organizations in the Construction Industry

Organizations operating in the construction industry seek to understand how to successfully identify and manage competencies, given the significant influence they have on performance. Thus, organizations need to explore new approaches for assessing and enhancing their competencies to improve their performance. This paper presents a review of past studies and identifies the most common organizational competency and performance measures. A focus group was conducted to evaluate and verify the list of identified competency and performance measures. The contributions of this paper are threefold. First, this paper addresses the lack of studies on organizational-level competencies specifically for the construction domain. Second, this paper identifies, categorizes, and ranks organizational competency and performance measures. Third, the categorization of competency and performance measures, verified by the focus group, provides organizations with a systematic method to evaluate their competencies and improve their performance.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author