Special Issue "Fuzzy Hybrid Systems for Construction Engineering and Management"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 October 2020).

Special Issue Editors

Prof. Dr. Aminah Robinson Fayek
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: fuzzy logic; fuzzy hybrid systems; machine learning; decision support systems; simulation; optimization; system dynamics; agent-based modeling; subjective knowledge; construction
Special Issues and Collections in MDPI journals
Dr. Mohammad Raoufi
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: construction engineering and management; civil engineering
Dr. Sumati Vuppuluri
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: function approximation; fuzzy neural nets; fuzzy reasoning; fuzzy set theory; fuzzy systems; inference mechanisms

Special Issue Information

Dear Colleagues,

The construction industry is a vital part of many national economies. Construction industry performance is largely dependent on the effective planning, execution, and control of construction projects, which involve both complexity and uncertainty. Fuzzy logic methodologies are able to model subjective information, handle uncertainty and complexity, and address the lack of comprehensive datasets available for modeling in construction engineering and management. In recent years, researchers have combined fuzzy logic with other soft computing and simulation techniques to create advanced fuzzy hybrid systems that are well-suited to construction modeling. This Special Issue focuses on recent advances and applications of fuzzy hybrid computing techniques in the construction domain. The Special Issue also focuses on the practical application of these techniques to solve real-world problems across a wide range of construction engineering and management issues.

Papers are invited that cover, but are not limited to, the following topics:

  • Fuzzy hybrid techniques in construction
  • Fuzzy arithmetic applications in construction
  • Fuzzy simulation techniques in construction
  • Fuzzy machine learning and optimization techniques in construction
  • Fuzzy multi-criteria decision-making applications in construction
  • Neuro-fuzzy systems in construction
  • Construction applications of fuzzy hybrid techniques, including risk analysis, project performance, productivity, procurement, contracting strategies, construction methods, competency assessment, quality management, safety management, and project planning and control.

Prof. Aminah Robinson Fayek
Dr. Mohammad Raoufi
Dr. Sumati Vuppuluri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • soft computing
  • fuzzy logic
  • neuro-fuzzy systems
  • fuzzy hybrid techniques
  • artificial intelligence
  • machine learning
  • optimization
  • simulation
  • construction modeling
  • decision-making
  • uncertainty modeling
  • construction engineering and management

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
Algorithms 2020, 13(12), 325; https://doi.org/10.3390/a13120325 - 04 Dec 2020
Cited by 1 | Viewed by 1354
Abstract
Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical [...] Read more.
Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical data—large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor’s impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS). Full article
(This article belongs to the Special Issue Fuzzy Hybrid Systems for Construction Engineering and Management)
Show Figures

Figure 1

Article
Fuzzy Preference Programming Framework for Functional assessment of Subway Networks
Algorithms 2020, 13(9), 220; https://doi.org/10.3390/a13090220 - 03 Sep 2020
Viewed by 1124
Abstract
The 2019 Canadian Infrastructure report card identified 60% of the subway system to be in a very poor to a poor condition. With multiple assets competing for the limited fund, new methodologies are required to prioritize assets for rehabilitation. The report suggested that [...] Read more.
The 2019 Canadian Infrastructure report card identified 60% of the subway system to be in a very poor to a poor condition. With multiple assets competing for the limited fund, new methodologies are required to prioritize assets for rehabilitation. The report suggested that adopting an Asset Management Plan would assist municipalities in maintaining and operating infrastructure effectively. ISO 55000 emphasized the importance of risk assessment in assessing the value of an organization’s assets. Subway risk assessment models mainly focus on structural failures with minimum focus on functional failure impacts and network criticality attributes. This research presents two modules to measure the functional failure impacts of a subway network, given financial, social, and operational perspectives, in addition to the station criticality. The model uses the Fuzzy Analytical Network Process with application to Fuzzy Preference Programming to calculate the weights for seven failure impact attributers and seven criticality attributes. Data are collected using questionnaires and unstructured/structured interviews with municipality personnel. The analysis identified social impacts to have the highest score of 38%, followed by operational and financial impacts at 34% and 27.65%, respectively. The subway station criticality revealed station location to have the highest impact at 35%, followed by station nature of use and station characteristics at 30.5% and 31.82%, respectively. When integrated with probability of failure, this model provides a comprehensive risk index to optimize stations for rehabilitation. Full article
(This article belongs to the Special Issue Fuzzy Hybrid Systems for Construction Engineering and Management)
Show Figures

Figure 1

Article
A Hybrid Genetic Algorithm-Based Fuzzy Markovian Model for the Deterioration Modeling of Healthcare Facilities
Algorithms 2020, 13(9), 210; https://doi.org/10.3390/a13090210 - 29 Aug 2020
Cited by 3 | Viewed by 1222
Abstract
Healthcare facilities are constantly deteriorating due to tight budgets allocated to the upkeep of building assets. This entails the need for improved deterioration modeling of such buildings in order to enforce a predictive maintenance approach that decreases the unexpected occurrence of failures and [...] Read more.
Healthcare facilities are constantly deteriorating due to tight budgets allocated to the upkeep of building assets. This entails the need for improved deterioration modeling of such buildings in order to enforce a predictive maintenance approach that decreases the unexpected occurrence of failures and the corresponding downtime elapsed to repair or replace the faulty asset components. Currently, hospitals utilize subjective deterioration prediction methodologies that mostly rely on age as the sole indicator of degradation to forecast the useful lives of the building components. Thus, this paper aims at formulating a more efficient stochastic deterioration prediction model that integrates the latest observed condition into the forecasting procedure to overcome the subjectivity and uncertainties associated with the currently employed methods. This is achieved by means of developing a hybrid genetic algorithm-based fuzzy Markovian model that simulates the deterioration process given the scarcity of available data demonstrating the condition assessment and evaluation for such critical facilities. A nonhomogeneous transition probability matrix (TPM) based on fuzzy membership functions representing the condition, age and relative deterioration rate of the hospital systems is utilized to address the inherited uncertainties. The TPM is further calibrated by means of a genetic algorithm to circumvent the drawbacks of the expert-based models. A sensitivity analysis was carried out to analyze the possible changes in the output resulting from predefined modifications to the input parameters in order to ensure the robustness of the model. The performance of the deterioration prediction model developed is then validated through a comparison with a state-of-art stochastic model in contrast to real hospital datasets, and the results obtained from the developed model significantly outperformed the long-established Weibull distribution-based deterioration prediction methodology with mean absolute errors of 1.405 and 9.852, respectively. Therefore, the developed model is expected to assist decision-makers in creating more efficient maintenance programs as well as more data-driven capital renewal plans. Full article
(This article belongs to the Special Issue Fuzzy Hybrid Systems for Construction Engineering and Management)
Show Figures

Figure 1

Article
Hierarchical Fuzzy Expert System for Organizational Performance Assessment in the Construction Industry
Algorithms 2020, 13(9), 205; https://doi.org/10.3390/a13090205 - 21 Aug 2020
Cited by 2 | Viewed by 1089
Abstract
Organizations have been trying to increase their efficiency and improve their performance in order to achieve their goals. Various factors determine organizational success. The construction industry is a project-based industry which is exceptionally dynamic. The need to identify the weak points and search [...] Read more.
Organizations have been trying to increase their efficiency and improve their performance in order to achieve their goals. Various factors determine organizational success. The construction industry is a project-based industry which is exceptionally dynamic. The need to identify the weak points and search for solutions to improve the performance of the construction organization is extremely crucial. The industry has always focused on the measure of project success. Previous research works have primarily focused on the measurement of financial or tangible assets. However, there is a lack of understanding of qualitative factors and their combined effect on organizational performance. Therefore, the objectives of this paper are to identify and study the success factors—both financial and non-financial factors. The potential success factors are collected from the literature review and construction experts through a questionnaire to evaluate their effect on organizational performance. The collected data have been analyzed using the Analytic Hierarchy Process (AHP) to shortlist the critical success factors. Thereafter, the Hierarchical Fuzzy Expert System has been used to build a prediction model based on the selected factors. The developed research/model benefits both researchers and practitioners to predict accurate company performance. Full article
(This article belongs to the Special Issue Fuzzy Hybrid Systems for Construction Engineering and Management)
Show Figures

Figure 1

Article
An Interval Type-2 Fuzzy Risk Analysis Model (IT2FRAM) for Determining Construction Project Contingency Reserve
Algorithms 2020, 13(7), 163; https://doi.org/10.3390/a13070163 - 07 Jul 2020
Cited by 2 | Viewed by 1651
Abstract
Determining contingency reserve is critical to project risk management. Classic methods of determining contingency reserve significantly rely on historical data and fail to effectively incorporate certain types of uncertainties such as vagueness, ambiguity, and subjectivity. In this paper, an interval type-2 fuzzy risk [...] Read more.
Determining contingency reserve is critical to project risk management. Classic methods of determining contingency reserve significantly rely on historical data and fail to effectively incorporate certain types of uncertainties such as vagueness, ambiguity, and subjectivity. In this paper, an interval type-2 fuzzy risk analysis model (IT2FRAM) is introduced in order to determine the contingency reserve. In IT2FRAM, the membership functions for the linguistic terms used to describe the probability, impact of risk and the opportunity events are developed, optimized, and aggregated using interval type-2 fuzzy sets and the principle of justifiable granularity. IT2FRAM is an extension of a fuzzy arithmetic-based risk analysis method which considers such uncertainties and addresses the limitations of probabilistic and deterministic techniques of contingency determination methods. The contribution of IT2FRAM is that it considers the opinions of several subject matter experts to develop the membership functions of linguistic terms. Moreover, the effect of outlier opinions in developing the membership functions of linguistic terms are reduced. IT2FRAM also enables the aggregation of non-linear membership functions into trapezoidal membership functions. A hypothetical case study is presented in order to illustrate the application of IT2FRAM in Fuzzy Risk Analyzer© (FRA©), a risk analysis software. Full article
(This article belongs to the Special Issue Fuzzy Hybrid Systems for Construction Engineering and Management)
Show Figures

Figure 1

Article
A Fuzzy-Based Decision Support Model for Risk Maturity Evaluation of Construction Organizations
Algorithms 2020, 13(5), 115; https://doi.org/10.3390/a13050115 - 02 May 2020
Cited by 1 | Viewed by 1818
Abstract
Risk maturity evaluation is an efficient tool which can assist construction organizations in the identification of their strengths and weaknesses in risk management processes and in taking necessary actions for the improvement of these processes. The accuracy of its results relies heavily on [...] Read more.
Risk maturity evaluation is an efficient tool which can assist construction organizations in the identification of their strengths and weaknesses in risk management processes and in taking necessary actions for the improvement of these processes. The accuracy of its results relies heavily on the quality of responses provided by participants specialized in these processes across the organization. Risk maturity models reported in the literature gave equal importance to participants’ responses during the model development, neglecting their level of authority in the organization as well as their level of expertise in risk management processes. Unlike the existing models, this paper presents a new risk maturity model that considers the relative importance of the responses provided by the participants in the model development. It considered their authority in the organization and their level of involvement in the risk management processes for calculating the relative weights associated with the risk maturity attributes. It employed an analytic network process (ANP) to model the interdependencies among the risk maturity attributes and utilizes the fuzzy set theory to incorporate the uncertainty associated with the ambiguity of the responses used in the model development. The developed model allows the construction organizations to have a more accurate and realistic view of their current performance in risk management processes. The application of the developed model was investigated by measuring the risk maturity level of an industrial partner working on civil infrastructure projects in Canada. Full article
(This article belongs to the Special Issue Fuzzy Hybrid Systems for Construction Engineering and Management)
Show Figures

Figure 1

Back to TopTop