Construction schedule delay risk assessment by using combined AHP-RII methodology for an international NPP project

Modelling the impacts of security on construction delays: A case of Afghanistan

Construction projects in conflict-affected regions face unique challenges, with security issues often serving as significant impediments to progress. This research investigates the multifaceted relationship between security factors and construction delays in Afghanistan, aiming to fill a critical gap in existing literature by offering a comprehensive analysis of the specific challenges encountered within this context. Employing a quantitative methodology and engaging stakeholders directly involved in construction projects, the study delves into the intricacies of security-related delay factors, identifying key contributors and assessing their impacts on project timelines. Through quantitative data analysis, the research reveals that security-related issues such as the presence of local armed individuals, interference from tribal leaders, and other forms of insecurity pose significant threats to construction projects in Afghanistan. These factors are found to have both high severity and frequency, underscoring their pervasive influence on project outcomes. Furthermore, the study highlights the interconnected nature of delay factors, emphasizing the need for a holistic approach to project management that integrates security considerations into broader risk management frameworks. Stakeholder perspectives play a central role in the research, with insights gathered from clients, consultants, contractors, and other key actors providing valuable insights into the diverse range of concerns within the construction industry. Variations in risk perceptions and priorities among different stakeholder groups underscore the importance of tailored interventions that account for the specific needs and challenges faced by each group. Building on these insights, the research offers recommendations for enhancing project resilience and mitigating the impacts of security-related delays in Afghanistan. Recommendations include the development and implementation of robust security protocols, proactive risk management strategies, and improved coordination with local authorities and security forces. Moreover, the study identifies avenues for further research, including comparative analyses across different regions and longitudinal studies tracking project outcomes over time. By deepening our understanding of the impacts of security on construction delays, this research contributes to the development of evidence-based policies and practices aimed at promoting sustainable development in conflict-affected regions and beyond.

Delay Causes and Types in Nigerian Power Construction Projects

Numerous events cause delays to entire power projects’ completion timelines, leading to loss. This paper focuses on revealing the critical delay causes in Nigerian power projects and identifying the delay types such as excusable delay, compensable delay, critical delay, and concurrent delay from the different perspectives of owner and contractor. Based on the survey of the 84 industrial experts in power distribution and transmission projects in Nigeria, this study provides the mean score and rank of 39 delay causes. Among the causes, 14 are identified as critical in the Nigerian power project. The top three critical delay causes are corruption and bureaucracy in government, inadequate fund/budget allocation, and price fluctuation/inflation. Owner and contractor do not show the difference in classifying the 14 delay causes into excusable and compensable delays. However, there was a difference between owner and contractor in categorizing whether a cause is critical or not and concurrent or not. A different viewpoint of types of delay cause can lead to a dispute which may lead to other delays and losses, and thus it is meaningful to understand the types of delay perceived from owner and contractor. Even though the delay cause and types are different depending on an individual project, the results of this study serve as a reference and would be beneficial for the practitioners in the construction industry to help identify the delay causes to manage them effectively.

A New Hybrid AHP and Dempster—Shafer Theory of Evidence Method for Project Risk Assessment Problem

In this paper, a new hybrid AHP and Dempster—Shafer theory of evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, a three-phase framework is proposed. In the first phase, quantitative research was conducted to identify the risk factors that can influence a project. Then, a hybrid PCA-agglomerative unsupervised machine learning algorithm is proposed to classify the projects in terms of Properties, Operational and Technological, Financial, and Strategic risk factors. In the third step, a hybrid AHP and Dempster—Shafer theory of evidence is presented to select the best alternative with the lowest level of overall risks. As a result, four groups of risk factors, including Properties, Operational and Technological, Financial, and Strategic risk factors, are considered. Afterward, using an L2^4 Taguchi method, several experiments with various dimensions have been designed which are then solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index, Reduced Risk Indicator, and Solving Time. The findings indicated that, compared to classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.

Scenario-based preference modeling to examine the robustness of airport mega projects initiatives

Airports are a high complex type of projects that are exposed to many disruptiveness. Proper management between airport expansion projects and airport operations is needed to ensure safety and efficiency of the project and the aviation. Scenario-based preference modeling is one of the robustness analysis techniques that describes the influence of disruptive scenarios on the project initiatives across different criteria. A scenario-based preference model was applied in this work to investigate the influence of different scenarios on Kuwait International Airport expansion. Scenario-based preference is a multi-criteria assessment, which allows the involvement of multiple stakeholders. The key target of the model was to illustrate the most and least robust initiatives, and the highest and least ranked initiatives over the scenarios. The analysis also ranked the scenarios based on their level of disruptiveness. The outcomes of this method can be used to mitigate the system and improve the project robustness by understanding each kind of risk impact. The results showed that the most and least disruptive scenarios were S₃, economic crisis, and S₅, compelling circumstance, and the most robust initiative was x1, completion of the main terminal building (T2). It is important to discuss the contribution and insights extracted from these analyses. For example, showing the most and least disruptive scenarios is not enough. It is important to mention what insights the readers can gain by knowing the importance of these scenarios. It is very important to highlight the significance of the results. The results showed that the most disruptive scenario was economic crisis. This indicates the fact that current Covid 19 pandemic had significantly affected the local economy, reduced country income that is based mainly on oil and consumed considerable budget on medical related activities. This had resulted in a lower expenditure on mega infrastructure projects as the airport, which caused considerable delay and interruption to major activities. Furthermore, the results showed that the most robust initiative was completion of the main terminal building (T2). This is mainly true due to the fact that the government is very keen to complete this major terminal in the airport as it will release the pressure on the old portion of the airport and increase the airport capacity. Based on these facts, it is clear that priorities in airport infrastructure activities and construction initiatives were affected considerably by the global and local circumstances and come first the global pandemic.

Using probability impact matrix (PIM) in analyzing risk factors affecting the success of oil and gas construction projects in Yemen

Purpose

This study aims to identify and assess the significant risks in Yemen oil and gas construction projects based on their risk rating (impact and probability) by using probability–impact matrix (PIM).

Design/methodology/approach

In total, 51 risk factors that might affect construction projects in the oil and gas sector are defined through a detailed literature review and expert judgment. The risk factors were tabulated in a questionnaire form, which was sent to a total of 400 participants asking their contribution in identifying the risk matrix for the risk factors in terms of impact and probability of occurrence during the project life cycle. Five zones were used in the matrix according to the degree of risk factor’s severity on the success of the project. These zones are light green, dark green, yellow, light red and dark red.

Findings

The PIM analysis for risk factors found that five factors are located in the dark red zone, as top risks factors have a very high impact and very high probability of occurring; 40 factors are located in the light red zone; six factors are located in the yellow zone; and no factors are located in the green zone (light and dark), which is considered an indication of the importance of risk factors under study and their impact on the success of construction projects in the oil and gas sector. Moreover, the factors under feasibility study and design and resources and material; are the most categories effect on project success.

Research limitations/implications

The research was limited to the oil and gas construction projects in Yemen.

Practical implications

Practically, this study highlights the top risk factors in oil and gas construction projects, which might cause an adverse effect on project success in Yemen. Classification and ranking of these factors by using the risk matrix provide the basis for risk response planning to enhance the chances of project success.

Originality/value

This paper identifies the matrix for risk factors affecting the success of construction projects in the oil and gas industry in Yemen. There is a significant contribution expected from this research, especially for companies operating in the oil and gas sector and other organizations that plan to invest in this field, in addition to expected benefits for the Yemeni Government and researchers because of lack of research in this area.

Risk Level Evaluation on Construction Project Lifecycle Using Fuzzy Comprehensive Evaluation and TOPSIS

A risk is a predominant problem in the developing countries construction projects. Although numerous studies have been concerned on risk, there is a limited study on a mechanism to identify the typical risks and effects level. This paper presents an approach for evaluating the risks in case of schedule delays at the various lifecycles of construction projects. The methodology applied is an integrated model of the technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy comprehensive evaluation (FCE). In this study, common criteria, sub-criteria, and attributes are constructed to make a decision concerning the influence level of risk of delay at the construction project lifecycle. The results showed that the construction stage (44%) is highly influenced, the second highly influenced stage is post construction (37%), and the least risked stage is pre-construction (35%). The construction projects in Ethiopia have faced an average delay risk of 38% at a high and very high-risk level. This work is expected to serve as a tool to assist managers in the management and control of schedule delays to mitigate their risks.