Risk analysis in underground tunnel construction with tunnel boring machines using the Best-Worst method and data envelopment analysis

Background

The use of tunnel boring machines is continuously increasing; however, this activity faces many risks, resulting in negative events.

Methods

A novel approach using the best-worst method and a data envelopment analysis model was proposed to analyze the risks of underground tunnel construction with tunnel boring machines. The proposed approach was validated using a realistic case study of metro construction in Thailand.

Results

The proposed approach efficiently analyzed the risks and produced more solid findings. The most critical and least affected risks can be identified according to risk scores in descending order.

Implication

This study contributes a new method of risk analysis that benefits project managers and stakeholders who design risk management plans to reduce the occurrence and mitigate the severity of metro works.

Originality

The new risk analysis can obtain the best compromise ranking of risks based on decision-makers' preferences, probabilities, and various consequences under different circumstances.

Performance Evaluation of Construction Sub-contractors using Ordinal Priority Approach

The construction industry has been recognized as one of the crucial industries of a country. In large-scale public projects, it is frequently observed that the client, who serves the public, seeks the services of several sub-contractors (project outsourcing firms) to execute projects. It also seeks the consulting firms' services to monitor the progress and performance of these sub-contractors. However, performance evaluation of a large number of sub-contractors is not only a challenging phase but also a source of conflict and mistrust between the evaluators and evaluatees because of the subjectivity in the evaluation process. The current study classifies perceived organizational performance into two streams, arguing that the one involving the opinions from the independent evaluators is more objective than the self-evaluation one. The study also makes a pioneering attempt in post-qualification performance evaluation of sub-contractors through the Ordinal Priority Approach (OPA), a promising multi-attribute decision-making methodology. The deployment of the OPA allows the decision-makers to estimate the weights of the evaluation criteria, the sub-contractors to be evaluated, and the experts who evaluated them simultaneously. Thus, the methodology can minimize the causes of mistrust by uncovering unreliable experts and inappropriate criteria. Also, a novel Relative Performance Index (RPI) has been proposed to standardize the performance evaluation system. The results show that evaluation of the firms does not end at their evaluation as without evaluating the evaluators, such an evaluation is only partially effective.

A novel project portfolio selection framework towards organizational resilience: Robust Ordinal Priority Approach

The COVID-19 pandemic has affected the world's economic condition significantly, and construction projects have faced many challenges and disruptions as well. This should be an alarm bell for project-oriented organizations to be prepared for such events and take necessary actions at the earliest time. In this regard, project-oriented organizations should establish their business based on the resilience concept, making them flexible in dealing with risks and decreasing the recovery time after disruptions. The current study proposes a practical conceptual framework for project-oriented organizations to select the most appropriate portfolio based on organizational resilience strategy. First, portfolios are identified, and the projects are clustered based on organizational resilience strategy using the Elbow and Fuzzy C-Means methods. The projects' scores are then determined employing the stakeholders' opinions and Robust Ordinal Priority Approach (OPA-R), which can handle the uncertainty of the input data. After that, each portfolio's score is determined using the obtained scores of the projects, and the best portfolio linked to the organizational resilience strategy is selected. The application of the proposed method to a project-oriented organization is examined, and its usage for the managers of project-oriented organizations is discussed in detail.

An appraisal on enablers for enhancement of waste cooking oil-based biodiesel production facilities using the interpretative structural modeling approach

With the continuous depletion of energy sources globally and serious concern regarding environmental degradation by the use of fossil fuel, biodiesel may play a key transponder. Biodiesel blended with diesel fuel achieves a decreased environmental footprint without losing the reliability of output and consumption. Biodiesel is produced from a variety of sources. Biodiesel generation from waste cooking oil (WCO) is effective for both the atmosphere and human health. Many research studies reported WCO biodiesel as a potential alternative fuel for internal combustion engine. The present study aims to provide key promoting and implementing agents for WCO utilization and WCO-based biodiesel production. A systematic literature review has been performed to identify enablers and the contextual relationship between various enablers was developed using interpretative structural modeling (ISM) and expert views. Using the method of ISM and cross-impact matrix multiplication applied to classification (MICMAC) methodology, the impact of enablers is studied. The findings revealed that all established enablers play an important role and are equally important promoters for the development of biodiesel based on WCO. The findings further suggest that human health issues, biodiesel processing plants, biodiesel support vehicles, and biodiesel production technology play a key role in the manufacture of biodiesel dependent on WCO. The most important leaders in the development of WCO biodiesel are government policy and funding, confidence in environmental issues, and financial assistance to biodiesel manufacturers.

A multi-stage fuzzy decision-making framework to evaluate the appropriate wastewater treatment system: a case study

Selection of appropriate treatment processes for wastewater treatment (WWT) plants at the design stage involves a careful examination of different economic, environmental, and social parameters. Designers and decision-makers seek a compromise among such conflicting elements, which can be facilitated by decision support tools that are adapted for the ambiguity of individual opinions and decision parameters. This study aims to improve the qualification and efficiency of decision-making in WWT processes. A multi-stage framework is proposed to help select investments, technology, appropriate technology-specific system, and companies that apply such systems. The framework combines the Analytic Hierarchy Process (AHP), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), cash flow analysis, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within fuzzy logic. The main contribution is the description and formation of an integrated framework to guide businesses and researchers for the evaluation of several WWT decision processes. To the best of the authors' knowledge, no study in the literature fuses multiple stages of this WWT process with the proposed approaches.

Evaluation of an automatic dry eye test using MCDM methods and rank correlation

Dry eye is an increasingly common disease in modern society which affects a wide range of population and has a negative impact on their daily activities, such as working with computers or driving. It can be diagnosed through an automatic clinical test for tear film lipid layer classification based on color and texture analysis. Up to now, researchers have mainly focused on the improvement of the image analysis step. However, there is still large room for improvement on the machine learning side. This paper presents a methodology to optimize this problem by means of class binarization, feature selection, and classification. The methodology can be used as a baseline in other classification problems to provide several solutions and evaluate their performance using a set of representative metrics and decision-making methods. When several decision-making methods are used, they may offer disagreeing rankings that will be solved by conflict handling in which rankings are merged into a single one. The experimental results prove the effectiveness of the proposed methodology in this domain. Also, its general purpose allows to adapt it to other classification problems in different fields such as medicine and biology.