Pricing Construction Risk: Fuzzy Set Application

Effective risk management is essential to the success of real estate projects. However, in fact most of statistics emerging in the process of risk management are either ambiguous or unquantifiable, which makes it very hard to fathom. Fuzzy mathematics, to the contrary, is superior and feasible in the way to enable assessment by means of expressive variables. This article constructs a risk index system for real estate enterprise's construction project by means of Delphi method, and then quantifies and analyzes the risk factors by the method of entropy weight fuzzy synthetic evaluation. The case study demonstrates that the fuzzy synthetic evaluation model is practical for estate enterprise's agent construction project prior to the investment and during the contract negotiation.

In the past decade, emergencies have occurred in construction projects, directly influencing injuries and casualties due to the dynamic nature of construction. Currently, the practice of developing evacuation routes for most construction projects is presented in a static and non-interactive manner. This involves a two-dimensional evacuation plan that cannot provide a real-time evacuation route. Furthermore, some emergency signs are unclear and do not provide the correct evacuation routedue to construction activities not aligning with the construction progress. Therefore, conventional evacuation plans have limitations and require improvement. Building Information Modeling (BIM) and Augmented Reality (AR) can present the evacuation route position of exit in a three-dimensional and can update the construction work following the construction progress. The objective of this research is to develop a communication system to support the evacuation route in a dynamic nature to assist construction workers and staff in accessing evacuation information such as appropriate evacuation route exit voice and arrow direction distance from the current location to the exit and obstacle avoidance in the construction project. In addition, to validate the proposed system approach, the case study uses Building Information Modeling (BIM) and Augmented Reality (AR), and a real construction project is executed. Moreover, three engineers, four trainees, and five construction workers participated in evacuating the proposed system. The result indicated that the proposed system could provide the correct evacuation route following the dynamic nature of the construction project.

Association analysis of amino acids in molecular sequences can reveal crucial information and knowledge for understanding structure, function and interaction of proteins. The traditional methods of association rule mining like apriori, F-P Growth etc. fail to generate appropriate patterns due to inherent uncertainty present in data. The uncertainty in sequence data caused by variation in the length of sequences and lack of parameterization lead to under prediction and over prediction of the results. In this paper an attempt has been made to develop a soft set based approach for mining fuzzy association patterns in peptide sequences of dengue virus. The fuzzy set approach is employed to incorporate the degree of relationships among amino acids due to variation in length of the sequences. The soft set approach is employed to incorporate the relationship of parameters with amino acid association patterns. The 12,581 sequences of dengue virus are downloaded from NCBI and screened for redundancy to obtain non redundant 6995 sequences. The amino acid associations are explored and analyzed using soft fuzzy approach. Also the results obtained by soft fuzzy approach are compared with the results obtained individually by ordinary, fuzzy and soft set approaches. The soft fuzzy approach is able to overcome the issue of under prediction and over prediction of the results obtained by other approaches. Also the interesting association rules have been generated to predict the structure and physico chemical properties of the peptide sequences.

Weather has an important role in people's lives, such as agriculture, economics, socio-economic, disaster management, and finance. So, weather prediction is very important to be considered. In the prediction process we are often faced with the problem of data incompleteness. Therefore, it needs a proper classification algorithm that able to handle incomplete attribute values in the training data. In this paper, we use two approaches to handle incomplete data, namely are rough set and fuzzy rough set based algorithms. To test the performance of the two algorithms, we use meteorological data to classify rain or dry season. Conclusion of the study showed that the rough set approach is more efficient than the fuzzy rough sets approach. The advantage of fuzzy rough set approach can predict all the conditions that may occur, which can't be done by the rough set approach.

In construction projects, the relationship between project cost and duration, which is one of the main factors of the project, are considered the parameters decision-makers. When a construction schedule is planned by the planners, additional resources are allocated to shorten the project duration considering the completion time and cost of the activities so that the most suitable construction schedule is prepared by providing a trade-off between project duration and total cost. In the case of time-cost trade-off problems, the total cost of the project is determined by summing each activities’ cost by adding indirect costs. Moreover, total project duration is determined by obtaining total project duration after CPM (Critical Path Method) calculations. Obtaining optimum duration and costs is such a problematic issue with using conventional methods and common software packages. These techniques consider deterministic project values and include CPM calculations for fitness value calculation. Moreover, current studies defined alternatives as non-variable discrete values. However, construction projects are dynamic processes, and quantities are prone to be changed related to project size. In this study, time-cost trade-off optimization is performed by using harmony search method with introducing different implementation that can be more easily used for different projects. The model is implemented in a real construction project, and the conclusions are interpreted.

This dissertation investigated how personality traits and team criteria influence team performance in real construction projects. Though personality influence on team performance has been significantly investigated in other business sectors, the literature revealed that the construction industry lacks investigations regarding personality influencing team performance. The existing literature revealed that the Big Five Factors (BFF) was the most popular assessment tool for personality. Thus, this dissertation adopted the 50-Item Personality questionnaire developed by Goldberg, which consited of extraversion, aggreeableness, conscientiousness, neuroticism, and openness to experience. The literature also revealed several common team criteria to evaluate team performance: team member satisfaction, shared values/goals/culture, commitment/responsibility, communication/information sharing, and trust/respect. This investigation aimed to find what personality traits influence team performance in construction projects and determine the accuracy of the personality traits and team criteria conditions. The qualitative comparative analysis (QCA) revealed that personality traits influence team performance only in combination with team criteria. It also revealed that agreeableness, conscientiousness, and openness to experiance are the most influential traits in the sampled construction projects. QCA also revealed team criteria conditions were the most influential and that trust and respect must be present in construction teams to achieve high team performance. This dissertation provided valuable findings to the construction industry by demonstrating that personality traits and team criteria are influence team performance. The practical implication of the results is that construction stakeholders must value trust/respect and use personality, especially agreeableness, to access and maintain trust/respect levels during the project's construction phase.

This paper presents a methodology to study the effect of different resolution strategies on the value of the investment in a project-specific dispute resolution ladder (DRL) using option/real option theories from financial engineering, process centric modeling, and system dynamics methodology. Of particular interest in this paper is the integration of these research methodologies into a computer model to support the evaluation of the DRL investment in a particular construction project by taking into account the characteristics of: (1) the project, and (2) the different alternative dispute resolution (ADR) techniques chosen for the DRL implementation. Finally, an example is presented to illustrate the application of the computer model in a real construction project.

Emerging trends in soft set theory and related topics.

Granular structure plays a very important role in the model construction, theoretical analysis and algorithm design of a granular computing method. The granular structures of classical rough sets and fuzzy rough sets have been proven to be clear. In classical rough set theory, equivalence classes are basic granules, and the lower and upper approximations of a set can be computed by those basic granules. In the theory of fuzzy rough set, granular fuzzy sets can be used to describe the lower and upper approximations of a fuzzy set. This paper discusses the granular structure of type-2 fuzzy rough sets over two universes. Definitions of type-2 fuzzy rough sets over two universes are given based on a wavy-slice representation of type-2 fuzzy sets. Two granular type-2 fuzzy sets are deduced and then proven to be basic granules of type-2 fuzzy rough sets over two universes. Then, the properties of lower and upper approximation operators and these two granular type-2 fuzzy sets are investigated. At last, several examples are given to show the applications of type-2 fuzzy rough sets over two universes.

Corrigendum.

The text explains that the fuzzy approaches have the objective to bring the decision process in planning closer to the decision maker, by allowing him to understand better the diversity of aspects that must be considered in planning decisions and helping the decision process while keeping as much information as possible, represented in the definition of fuzzy sets. The paper shows that the qualitative aspects of uncertainty, risk and decision making may be adequately modeled with a fuzzy set approach. It could help the decision maker guiding him towards a decision that takes in account uncertainty in the future, the multiple criteria evaluation of plans, as well as hedging policies.

Introduction. The construction industry has a major role to play in the global economy due to its multiplier effect, high volatility and complexity of production relations, however, existing risk management techniques, particularly those at the stage of investment justification, are still insufficiently developed. The aim of the study is to systematize modern ap proaches to risk-based lifecycle management of construction facilities, identifying gaps and promising areas of develop ment, including the use of digital technologies. The main tasks include an analysis of the regulatory framework, a review of risk assessment methods and development of strategies for minimizing them for different types of real estate.Materials and Methods. A comprehensive bibliometric study of modern approaches to construction risk management using VOSviewer software has been performed. The most authoritative publications from the international Scopus and Web of Science databases, as well as prominent Russian scientific papers, have been selected to be analyzed. The study covered peer-reviewed articles, monographs, and dissertations from the last decade for a representative sample. Special attention is paid to a critical analysis of methodological approaches to risk assessment at the pre-project stage, where traditional methods show the greatest limitations. The study makes use of a systematic approach combining a quantitative analysis of publication activity with a qualitative assessment of the research content. On top of that, successful cases of the introduction of modern risk-oriented practices in real construction projects are explored.Research Results. The analysis has enabled the key trends in the field of construction risk management over the past decade to be identified. The results of the analysis are indicative of a stable relationship between the quality of the risk management system and success of construction projects, which comes to the fore while investigating modern digital technologies, including artificial intelligence and machine learning methods that are increasingly used for processing large amounts of data in the construction industry. The greatest methodological difficulties are faced with at the stage of investment justification, where traditional expert approaches require mandatory addition of digital analysis tools as con firmed by the practical cases of implementation of risk-oriented approaches explored in the study. The development of adaptive techniques is particularly relevant that takes into account both traditional types of construction risks and new challenges associated with the digital transformation of the industry and taking into account ESG factors, while bridging the existing gap between scientific developments and their practical implementation is becoming a major condition for successful modernization of risk management, which calls for taking coordinated actions by all involved in the construc tion industry and improving the regulatory framework in compliance with modern technological capacities.Discussion and Conclusion. The bibliometric analysis has displayed the transition from traditional risk management methods to digital solutions, while indicating the continuing gap between theory and practice, particularly at the stage of investment justification. Modern approaches are actively integrating BIM, decision support systems and AI, but there is a problem of the lack of qualified employees. Hybrid methods combining expert assessments with machine learning and considering new risk factors such as sanctions and environmental requirements are becoming particularly relevant, which calls for modernization of the regulatory framework and professional standards.

Purpose. To comprehensively investigate the impact of modern information technologies on the design, management and implementation of construction projects, to identify key aspects and barriers to the introduction of digital tools in the construction industry, to substantiate the economic and organizational advantages of using innovative solutions such as building information modeling (BIM), project management systems (PMS) and artificial intelligence, as well as to develop recommendations for improving digitalization processes organization of construction in order to increase productivity, reduce costs and ensure the sustainable development of the industry. Methodology. The study used several methods, including the analysis of scientific publications, a comparative method for evaluating the effectiveness of various digital technologies used in construction, as well as an empirical approach based on data from real construction projects in which information technology was used. The study includes an analysis of specific examples of the use of BIM, PMS and AI in the design, construction and operation phases of facilities. Interviews with specialists and surveys of construction companies on practical experience in the implementation of digital technologies in the construction process are used. Findings. It was found that the application of building information modeling (BIM), automated project management systems (PMS) and artificial intelligence technologies can reduce construction organization costs by up to 20 % and increase the productivity of construction processes by 30 %. Key factors for the implementation of these technologies are identified, including the need for staff training, adaptation of existing processes to new tools, as well as overcoming cultural and organizational Barriers. In addition, it has been established that the main barriers to the adoption of digital technologies are high initial investment and insufficient level of digital training in the industry. Originality. For the first time, this study summarizes approaches to the integration of modern information technologies into various stages of the construction cycle, including design, construction, organization, resource management and operation. Practical value. The practical significance of this study is that its results can be used to develop strategies for the digitalization of construction companies. In particular, based on the data obtained, training programs for the training of construction professionals can be improved, contributing to their ability to work effectively with digital tools. The results can also be useful for optimizing management decisions at all stages of a construction project. This will reduce costs, increase resource efficiency and ensure a high level of construction quality. In the long run, this will lead to the sustainable development of the construction industry in the context of digital transformation.

The current literature discusses the methods to estimate the costs and cost contingency. The literature also distinguishes “known unknowns” and “unknown unknowns” contingencies. Little is written, however, about the evolvement of total project cost estimates during the preconstruction phase of construction projects. Moreover, not many studies are investigating the “known unknowns” and “unknown unknowns” contingencies in real construction projects. Practice expressed the need for getting more insight into the development of the estimated costs of the projects in the preconstruction phase. This paper, therefore, discusses the estimate of the total project costs (and cost contingency) in the preconstruction phases of 29 Dutch flood defense projects using a case study approach. Altogether, the projects have experienced an 11.51% increase in the estimated costs compared to the initial estimates, which is low compared to previous studies. This increase in the cost estimates of the flood defense projects can be explained by “technical” reasons. The investigation of “known unknowns” and “unknown unknowns” contingencies shows that the percentage of the “unknown unknowns” contingency has increased in the preconstruction phase while a reduction was expected. This increase suggests that the projects were not confident about their estimates and the increase can be explained by a lack of experience, organizations’ culture, or the phenomenon of “pessimistic bias.” Practitioners can avoid “pessimistic bias” behavior by asking for opinions about their estimates and using historical project data. Further research is suggested into realized cost contingency after project execution.

Multipurpose linear programming optimization model for repetitive activities scheduling in construction projects