Risk Management In Construction Projects Research Articles

Boosting large‐scale construction project risk management: Application of the impact of building information modeling, knowledge management, and sustainable practices for optimal productivity

AbstractThis study investigates the relationship between Building Information Modeling (BIM) and knowledge management in large‐scale construction projects, emphasizing risk management, and increasing productivity. This study aims to identify the most important and impactful aspects of modern management techniques in the large‐scale construction sector to improve construction quality; focusing on cost reduction, energy conservation, and pollution reduction during construction. Employing a validated questionnaire from experts and the Analytical Hierarchy Process (AHP), the research prioritizes key factors, elucidating the pivotal role of BIM in understanding project geometry, efficient documentation, design flexibility, and reduced project delays. Ascertained through data analysis employing the pairwise comparisons method of the AHP analysis method, the compatibility of all comparisons and matrices arising from the decision‐making process of experts was below 0.1. The results establish BIM's technical capabilities, economic considerations, and pollution reduction as paramount, contributing significantly to improve large‐scale construction project risk management. The prioritization of the main criteria reveals their significance in the following order: BIM technical capabilities (0.281), BIM economic capabilities (0.231), pollution reduction (0.228), knowledge management capabilities (0.096), BIM time capabilities (0.090), and human resources management capabilities (0.074). BIM can improve risk management for large‐scale construction projects by: using the BIM model to understand the geometry and features of large‐scale construction projects; digitizing and organizing contract documents, project data, plans, work instructions, roles and responsibilities, cost, time, and energy analysis, and other project‐related documents for efficient project management; decreased duplication and project delays; reducing the need for on‐site personnel during implementation and CO2 emissions reduction. According to the results of the BIM model utilization to better understand the geometry and characteristics of large‐scale construction projects, the item was identified as the most significant and influential among the main components.

An Artificial Neural Network Approach for Construction Project Risk Management

An Artificial Neural Network Approach for Construction Project Risk Management

Developing a multidimensional risk assessment model for sustainable construction projects

PurposeSustainable construction re-engineers the conventional project lifecycle to integrate sustainability solutions. The additional sustainability requirements introduce new layers of complexity, challenges and risks that if unaddressed, can derail the gains in sustainable construction projects. This study developed a multidimensional risk assessment model for sustainable construction projects in the United Arab Emirates (UAE).Design/methodology/approachThe research activities a comprised comprehensive literature review to shortlist relevant risks, an analysis of the probability – impact rating of the shortlisted risks – and the development of a risk assessment model for SC projects in the UAE. The model is developed based on the multicriteria framework and mathematical formulation of the fuzzy synthetic evaluation approach.FindingsThe developed model quantified the overall risk level in sustainable construction projects to be 3.71 on a 5-point Likert scale, indicating that investment in SC projects in the UAE is risky and should be carefully managed. The developed model further revealed that each of the risk groups, comprising management (3.82), technical (3.78), stakeholder (3.68), regulatory (3.66), material (3.53) and economic risks (3.502), presents a significant threat to realizing outcomes typical of SC projects.Originality/valueThis study developed a multidimensional risk assessment model capable of objectively quantifying the overall risk level and provides decision support to project teams to improve risk management in sustainable construction projects.

Can ChatGPT exceed humans in construction project risk management?

PurposeThe objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.Design/methodology/approachEmploying a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.FindingsChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.Originality/valueThis study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

A simulation-based risk interaction network model for risk management in international construction projects

The inherent complexity in International Construction Projects (ICPs) brings forth the intricate interactions between risks, invalidating the risk analysis under the conventional perception of independent risks. The diverse phases of ICPs feature the temporal dynamics of risks and interactions, exacerbating the challenge of pinpointing critical risks. This study aims to model how the risks are interconnected and amplified by proposing an ICP risk management framework, in which the project phase-based Risk Interaction Network (RIN) is constructed. An integrated index is designed to evaluate each risk’s criticality and discern key risks within a two-part procedure, where the simulation-based model estimates risk sensitivity to predecessor risks and captures the stochasticity of risk occurring, and network analysis gauges risk spread power on the entire network. An example of an application is provided to illustrate the utility of the proposed framework.

ANALISIS DAN MITIGASI RISIKO KESELAMATAN DAN KECELAKAAN KERJA PADA PROYEK PEMBANGUNAN BALI INTERCONTINENTAL GRAND BALLROOM

ANALISIS DAN MITIGASI RISIKO KESELAMATAN DAN KECELAKAAN KERJA PADA PROYEK PEMBANGUNAN BALI INTERCONTINENTAL GRAND BALLROOM

Evaluation of risk factors affecting the safety of coal mine construction projects using an integrated DEMATEL-ISM approach

Purpose The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.Design/methodology/approach The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.Findings The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.Research limitations/implications Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.Social implications The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.Originality/value Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

Analisis Manajemen Risiko Pada Proyek Konstruksi Jalan di Provinsi Jambi

In the implementation of road construction projects, there are often various obstacles that arise due to the risks that occur which result in not achieving the expected performance, quality and achievement of work results. The purpose of this study was to determine the risk factors that affect the implementation of work in the field and to determine the application of risk management in road construction projects. In this study, risk identification, risk analysis and risk response were carried out in the construction of road construction projects, especially road construction in Jambi Province. The research method used was by way of a survey distributing risk factor questionnaires and actions on risk factors to consultants. The assessment method uses a risk matrix assessment sourced from AS/NZS 4360: 2004. From the results of this study, it is found that the risk level for road construction work in Jambi Province is very high risk (very high) of 8%, high risk (High) of 7%, and a moderate risk of (Medium) of 16%.

Barriers to risk management in small construction projects in Sri Lanka

PurposeThis study explores the current status of risk management (RM) in Sri Lanka and the obstacles in implementing RM in Sri Lankan small construction projects.Design/methodology/approachThe current research study adopted a quantitative research method. Using a purposeful sampling strategy, 100 construction companies engaged in small construction projects in Sri Lanka were selected and a questionnaire was distributed among top managerial employees of these companies. Employees belonging to 47 companies responded to the survey which provided information relating to 812 construction projects. The collected data were analyzed using the RM implementation index (RMII) and other quantitative measures such as mean, median and percentages.FindingsThe research findings indicated that RM incorporation was at a lower degree in small construction projects in Sri Lanka. The findings further indicated that RM incorporation was relatively high in the public construction projects compared to the private construction projects. The main obstacles identified through the survey for RM implementation were: “lack of funding”, “lack of time”, “low profit margin”, “not economical” and “lack of knowledge”.Research limitations/implicationsThe study was limited to exploring RM implementation in small construction projects in Sri Lanka. The study was limited to the building construction companies under the 150-million-rupee (approx. below 450,000 USD) financial limit registered with the construction industry development authority in Sri Lanka.Originality/valueRM in small construction projects in developing countries is a relatively less explored domain. Sri Lankan construction industry is another relatively less explored domain in terms of new management technique implementation such as RM and value management. The quantitative approach used for the study revealed that RM implementation is at a lower degree in the small construction projects. Moreover, several obstacles pertaining to RM implementation were recognized through this study. These findings will be useful for the construction stakeholders to overcome the recognized barriers and effectively use RM in their respective construction projects.

Risk Management Planning by Risk Register in Building Construction Project

This research addresses the common issue of vulnerabilities emerging during construction projects due to inadequate risk identification and analysis. It aims to mitigate this by creating a comprehensive risk register, documenting potential risk events, their causes, and characteristics. The main goal is to identify prominent risk factors in building construction projects and develop a risk management strategy using this register. The study used purposive sampling and considered 22 indicators, including human resources, cost, materials, equipment, and environmental conditions. Questionnaires were distributed to 50 contractors in the Banda Aceh region. Validity and reliability were confirmed through statistical analysis. The analysis showed that human resources were the most prevalent risk factor. Using a probability impact matrix, five indicators had medium-risk levels, while seventeen indicated low risk. Human resources, materials and equipment, and environmental conditions were identified as dominant risk factors, all at a medium-risk level. The study underscores the importance of addressing human resources risks through a structured risk register, which can significantly improve project management and execution. By systematically assessing and mitigating potential risks, project stakeholders can make informed decisions and enhance project outcomes in terms of timelines, costs, and quality. In conclusion, this research highlights the critical role of risk management in construction projects and emphasizes the value of a comprehensive risk register. Addressing dominant risks proactively can positively impact building construction projects.

Analysis of Changes In Risk Management Implementation At PT. ABC Based On ISO 31000:2018

The implementation of a company's risk management is important because it can provide input for management to act preventively to prevent risk events that are often detrimental (downside risk). PT ABC as a subsidiary of the nickel mining company as Parent Company has transformed its risk management from partial to integrated based on ISO 31000: 2018. This change must be well prepared to avoid delays in the integration process because PT ABC has a project to develop the Industrial Estate and the RKEF (Rotary Kiln Electric Furnace) Smelter Development with a significant value so that preventing losses by carrying out risk mitigation efforts is important to implement. The purpose of this study is to comprehensively analyze changes in the application of risk management at PT ABC, evaluate the application of risk management adopted by the company, identify challenges faced by the company, and analyze the application of risk management to current business activities. The research method uses primary data through direct observation of existing change preparation practices and uses secondary data from related company data. The basis of the risk method used is the ISO 31000: 2018 guidelines. The results showed a significant impact in the integration of risk management adopted to be more comprehensive than the previous implementation. The risk management ntegration factor of the Parent Company and its subsidiaries has consequences for the sustainability of the program and budgeting in the RKAP, especially for improving the quality of risk management analysis through risk awareness programs. The implementation of PT ABC risk management in the RKEF smelter construction project identified 8 aspects of risk which include investment taxonomy (5 aspects), regulatory changes (1 aspect), and environment (1 aspect). Risk mitigation efforts have been carried out in the risk map from 1 aspect of high risk; 5 aspects of medium risk and to reduce the impact and probability that exists, namely: 3 risk aspects (from medium to high) and 5 risk aspects (from low to medium). This shows that there is a fairly good readiness to anticipate risks that have a negative impacts.

Data-Driven Approach to Risk Identification for Major Transportation Projects: A Common Risk Breakdown Structure

Identifying and evaluating risks is one of the most essential steps in risk management in construction projects. When technical and managerial complexity increases in major transportation projects, this becomes even more important. Currently, project teams are assumed to identify risks mostly based on their experience and expertise. It is a major issue that some state departments of transportation (DOT) project teams lack the risk management experience. This study proposes using a data-driven approach to unify and summarize existing risk documents to create a comprehensive risk breakdown structure (RBS). As a preliminary risk identification framework, a consolidated RBS were developed, using content analysis of public risk reports by various DOTs. Then, comparison was made between the developed RBS with 70 US transportation projects' risk registers. Natural language processing techniques, bidirectional encoder representations from transformers, were employed to calculate semantic text similarity to determine what percentage of risks are covered by generic RBS. The results showed that 70 generic risk templates cover almost 81% of the identified risks in the database of 70 major projects which is about 6000 individual risks. Project parties can use these results to discuss and identify context-specific risks as a starting point. The study also determined the interactions between risk items based on their co-occurrence using historical data. Research findings revealed the importance of considering interdependencies between risks in future studies.

Factor's determining successful risk management in construction projects

Factor's determining successful risk management in construction projects

Risk Management for Housing and Construction Projects

Risk Management for Housing and Construction Projects

Risk Management and Decision Analysis for Construction Projects

As construction projects continue to grow in size and complexity, risk management and decision analysis are increasingly becoming critical key factors in ensuring the successful completion of projects. The special nature of construction projects requires project teams to be able to comprehensively and systematically assess and deal with potential risks to ensure that the project can be completed with high quality within the scheduled time and budget. Based on analyzing the importance of risk management in construction projects, this paper discusses in depth the current situation and problems of risk management in construction projects, and puts forward countermeasures to strengthen the risk management of construction projects, so as to improve the feasibility and success of the project.

Return on Investment of Implementing Subsurface Utility Engineering on Transportation Projects: A Review and Analysis of Previous Cost–Benefit Studies

Since the late 1990s, studies conducted on the implementation of the subsurface utility engineering (SUE) concept in the design and planning phase of highway infrastructure projects have repeatedly shown a significant return on investment (ROI). ROI values varied across all studies, influenced by project characteristics, such as the number of projects studied and quantity and quality of project information. This study examines previous ROI studies on the implementation of SUE and compares the methodologies and cost-saving models used to account for the benefits of performing SUE on diverse construction projects. The study further investigates the results of the various cost-saving items and benefit factors to identify the most significant and least cost-saving items. The reduction in utility relocations during construction was identified as the significant cost saving in using SUE, while the direct cost to the public was identified as the least cost saving and not adequately estimated by most studies. The study supports why the adoption of SUE is encouraged for projects involving infrastructure construction and should be considered an integral part of engineering risk management for construction projects.

Assessing the Accuracy of ChatGPT Use for Risk Management in Construction Projects

Artificial Intelligence (AI) is considered promising digital technology that has important opportunities for enhancing project oversight and delivering improved decision-making in the risk management domain. However, there is a limited amount of research that has evaluated AI tools’ performance in risk management. Therefore, with the intention of sustaining more accurate risk-based decision-making process in the construction industry, this paper investigates the accuracy of ChatGPT in risk management for different project types. In this context, Key Performance Indicators (KPIs) related to each risk management sub-process were determined, and then a questionnaire that consisted of prompt templates was prepared for collecting data from ChatGPT. Afterwards, ChatGPT’s responses were evaluated by experts with focus group sessions. The findings indicate that ChatGPT has a moderate level of performance in managing risks. It provides more accurate knowledge in risk response and risk monitoring rather than risk identification and risk analysis sub-processes. This research paves the way for future studies by demonstrating an implication of ChatGPT use for risk-based decision making. In addition, gaining insight into the precision of ChatGPT in the risk-based decision-making process will empower decision-makers to establish resilience in business operations through technology-driven risk management.

Multi-criteria decision-making model for risk management in modular construction projects

The modular sector needs a precise guide to determine the most efficient risk management approaches. The main research objective of this study is to develop a multi-criteria decision-making model to find the most efficient risk management approach according to the relevant risk criteria. The risk criteria and risk management approaches for modular construction projects were also identified and classified within this scope. A systematic literature review, semi-structured interviews, and open-ended questionnaires were performed for identification and classification purposes. For ranking and quantifying the identified risks and risk approaches, as well as developing the decision-making model, the Delphi method and the Analytical Hierarchy Process (AHP) were conducted. A two-round Delphi method, with eleven experts, was conducted to achieve efficient performance scores of the identified risk management approaches. The percentage standard deviation decreased, Relative Importance Index (RII), Cronbach’s alpha, and Kendall’s coefficient of concordance (Kendall’s W) were calculated to ensure the outputs’ reliability, validity, and agreement level. The AHP method opted to quantify the Delphi method outputs, solve the multi-criteria decision-making process, and develop the multi-criteria decision-making model for risk management of modular construction projects. Triangulation results show that the critical risk categories are supply chain, health and safety, stakeholders, and governmental support. Lean principles such as the Last Planner System, Value Stream Mapping, Just in Time, and Kaizen are top-rated risk management approaches. This research’s novelty is identifying and analyzing crucial risk categories, providing the relevant risk management approaches ranked according to efficiency performance, and presenting a decision-making model as a guideline for risk management of modular construction projects.

Uncertainty network modeling method for construction risk management

In recent decades, uncertainty management has increasingly elicited attention in construction management research due to increasing project complexity. However, existing management methods have not been able to solve the issues around risk and uncertainty, and regardless of the proposed network-based risk modeling approaches, there are insufficiencies in contemporary methods, such as their practical applicability. This study examined the current state and issues of uncertainty and risk management and proposed a novel uncertainty network model (UNM) as a solution. The uncertainty network model was designed and validated using design science methodology (DSM), drawing on literature and empirical data from interviews, questionnaires, case observations, and case testing. The UNM visually presents project risks, uncertainties, and their interconnections and criticality transforming project stakeholders’ tacit knowledge into an explicit, systematic representation of a project’s uncertainty and risk architecture. Applied to a real-world construction project, the model received positive feedback, demonstrating its effectiveness in enhancing practitioners’ understanding of networked risks and the potential to guide cost-effective risk-control activities by applying a systemic lens to project management. This practical validation showcases the model’s potential in addressing the shortcomings of existing methods and improving construction project risk management.

Risk Analysis and Research for Construction Projects

As the population grows and the demand for public infrastructure increases, more and more construction projects are being carried out. In order to avoid the failure of construction projects due to their high level of complexity, risk anticipation and risk response can be made through the study of project risks, which can greatly improve the success of construction projects. The investment and time spent on a construction project are huge and if the project fails or is delayed, there will be serious consequences. This is why construction project risk management is so important for construction projects. Therefore, this paper analyses the risks of construction projects. It finds that construction project risks can be classified as internal and external risks and suggests improvements to the existing three-step project risk management process Identify-Assess-Respond, adding a project risk review. This paper enriches the study of risk classification and management processes for construction projects and proposes risk management process measures that have implications for future construction projects to deal with risks more efficiently.