Construction Project Management Research Articles

Unethical behaviors of contractors in construction project management: Framework development and causal analysis

Unethical behaviors of contractors in construction project management: Framework development and causal analysis

Schedula Anima: Dynamic Visualization of Gantt Charts and Resource Usage Graphs in Project Scheduling

Scheduling is essential in managing projects. ‘Schedula Anima’ is a new software designed to provide a comprehensive view of schedules between early and late dates for construction project managers. Capturing the dynamic nature of projects, it offers improved visualization through an animation process that creates incremental frames of bar charts and the corresponding resource graphs. As activity delays are simulated, it is observed that delays earlier in the schedule have more significant effects on project completion. A new prioritization method is introduced to evaluate the ease of rescheduling activities. A metric for monitoring resource usage float is presented, and the search space for resource utilization is delineated. As resource smoothing is studied in the resource usage graph and the time domain, a correlation is discovered between resource smoothness and the float consumption rate. It is shown that the schedule and resource usage graph comprises five sub-areas representing different risk exposures. Animation also improves communication in project teams and is beneficial in education. Finally, it is discovered that the permutations of activities in the simulation form a group. Enhancing our perception of resource utilization and the management of delays, ‘Schedula Anima’ brings a renewed perspective to project scheduling.

Project Performance Analysis Based on Earned Value Method on Infrastructure & Infrastructure Improvement of Yohanis Kapiyau Timika Airstrip

This study analyzes the performance of construction projects using the Earned Value Method (EVM) on the Infrastructure and Facilities Improvement Project at Yohanis Kapiyau Airbase in Timika. Utilizing key performance indicators such as Cost Variance (CV), Cost Performance Index (CPI), Schedule Variance (SV), and Schedule Performance Index (SPI), the research evaluates cost and time deviations to provide a comprehensive understanding of project performance. EVM offers precise insights into the project's current status, including estimates for total costs and completion timelines, which are critical for effective decision-making and ensuring adherence to project plans. The findings demonstrate that EVM is a reliable tool for accurately monitoring and managing construction projects, enabling stakeholders to identify inefficiencies and implement corrective measures in a timely manner. By providing an in-depth evaluation of cost efficiency and schedule performance, the study contributes to the broader application of EVM in enhancing construction project management. This research is expected to benefit academics by advancing theoretical knowledge, assist contractors in optimizing project execution, and inform public stakeholders on best practices for infrastructure development, ultimately contributing to more effective and efficient management of construction projects in Indonesia.

Developing the Learning Curve Model to Enhance Construction Project Scheduling and Cost Estimating

Aim The aim of the study is to develop a scheduling and cost estimation model for repetitive construction units by applying the learning curve theory and to contribute to advancements in construction project management practices, promoting efficiency and competitiveness within the industry. Background Construction projects, particularly those with repetitive units like housing developments, face ongoing challenges in accurate scheduling and cost estimation. Traditional estimation methods often overlook the impact of learning effects, which can improve productivity and reduce costs as crews gain experience. Learning curve theory, widely applied in manufacturing, offers a framework to model these gains in construction settings. Integrating learning curves into project planning has the potential to enhance accuracy in forecasting timelines and budgets, ultimately improving project efficiency and resource management. Objective The objective of this study is to develop and apply a learning curve model to enhance scheduling and cost estimation in repetitive construction projects, particularly in a multi-unit housing project. Methods By incorporating historical data and analyzing critical factors that impact project duration and cost, a more reliable forecasting model is developed. The learning curves are created using a three-point approach, supported by artificial neural networks (ANN) and the relative importance index (RII), to systematically assess cost divisions and influential project factors. Results The results indicate that the learning curve model can achieve time savings of 27% and labor cost savings of 36% compared to traditional estimation methods that do not consider the effect of the learning curve in construction projects. Conclusion This research demonstrates that learning curve models, combined with advanced data analysis techniques, provide a robust framework for optimizing project schedules and budgets, ultimately leading to more efficient resource utilization and cost-effective project outcomes. In other words, the study presented in this paper is significant as it can lead to improved project outcomes, cost savings, better resource management, and overall advancement in the construction industry's practices and competitiveness. This approach allows for accurate scheduling and cost forecasting based on data-driven insights.

Prioritization of Preventive Measures: A Multi-Criteria Approach to Risk Mitigation in Road Infrastructure Projects

Risk management in construction projects is a critical process aimed at identifying, evaluating, and mitigating potential risks that could impact project performance. Preventive measures play a central role in this process, serving as proactive strategies to minimize the likelihood and impact of risks on project outcomes. This study involved 37 experts from multidisciplinary fields related to road infrastructure, ensuring a diverse and comprehensive perspective on risk evaluation and prevention. The DELPHI method was employed to systematically define key risks and their corresponding preventive measures, providing a structured foundation for further analysis. The experts evaluated 302 preventive measures across 56 risks using 4 predefined criteria: implementation costs, time required for implementation, implementation complexity, and probability of success. A multi-criteria decision making (MCDM) approach was then applied to analyze these evaluations, enabling the prioritization of preventive measures and the allocation of resources toward the most effective strategies. Additionally, fuzzy logic was employed to analyze and validate the results, providing a complementary approach to the MCDM methodology. The results of this research provide a robust framework for risk management, offering practical guidance for decision makers in the construction industry. By integrating expert judgment, systematic evaluation, and advanced analytical methods, this study delivers actionable insights and establishes a reliable methodology for enhancing the effectiveness of risk mitigation in road infrastructure projects.

Optimizing claim management process groups to enhance construction project success

In the complex construction projects, claims arising from unforeseen circumstances, contractual disputes, or project changes can significantly impact project outcomes, making effective claim management a critical aspect of overall project success. Ineffective claim management can lead to project delays, cost overruns, and in severe cases, project termination. This study examines critical factors influencing claim management in construction projects, analyzing stakeholder perceptions through a questionnaire distributed to 104 industry professionals. Using convenience sampling, the study utilizes a Relative Importance Index (RII), Frequency Index (FI), and Frequency Adjusted Importance Index (FAII) to rank the significance of 108 identified claim management factors across eight process groups. The findings emphasize the criticality of clear communication and robust contract management in mitigating claims. Poor negotiation skills, adversarial relationships, and inadequate documentation are identified as major obstacles to effective claim resolution. Pearson correlation analysis reveals strong correlations between key factors and their respective process groups, reinforcing the reliability. The research highlights the ‘claims resolution’ phase as particularly crucial, with factors like the use of balanced contracts (e.g. FIDIC/ICE) and comprehensive claim documentation emerging as highly significant. The study provides valuable insights for industry practitioners to enhance claim management practices and ultimately, improve project success rates.

Effect of Cost of Construction of Medium-Level Building Projects on Project Productivity in Kirinyaga County, Kenya

The construction industry is important in society, the economy and the environment. Despite its importance, it is faced with the challenge of the increasing world's urban population at a rate of 200,000 people per day who require affordable housing, social transportation and utility infrastructure. To counter this challenge, the industry will be under a moral obligation to transform by way of reducing construction costs, improving the use of scarce materials and making more eco-efficient over time. Therefore, this study's objective was to evaluate the effect of the cost of construction of medium-level building projects on project productivity in Kirinyaga County, Kenya. The total costs of construction (cost stack) are split into hard costs, soft costs and unforeseen costs. The methodology adopted for data collection was through the administration of structured questionnaires to select seven categories considered to be the main key stakeholders in the construction of building projects. These comprised landlords/homeowners, architects, structural engineers, quantity surveyors, building contractors, construction project managers, and Ministry of Housing and Infrastructure officials. To analyze the results, descriptive statistics and correlational analysis were used. In the analysis, emphasis was placed on the evaluation of the effect of the cost of construction on project completion time, quality and costs. Results from the study show that key factors affecting project productivity through increased costs are delay in payment to contractors and suppliers, high cost of construction materials and choice of construction materials. Other factors are the level of project complexity, site topography, architectural design errors and changes as well as structural design errors and changes.

Implementation of Building Information Modeling (BIM) Utilizing Tekla Structures for Work Volume Calculation (Institute of Technology Keling Kumang Project in Sekadau District)

In the construction industry, planners use contract drawings and the Work Plan and Conditions (RKS) to determine the work to complete. The conventional approach in calculating the volume and quantity of work usually relies on Microsoft Excel as a calculation tool based on AutoCAD drawings. However, this method is considered inefficient and time-consuming, which can affect the quality of the resulting infrastructure. Building Information Modeling (BIM) is a solution to improve efficiency in planning and managing construction projects. In this research, Tekla Structures was chosen for its ability to generate project drawings, manage and monitor projects, amplify details with high accuracy, and automatically calculate the volume of work after modelling. This research aims to encourage wider application of digital technology in the construction industry in Indonesia. The results showed that comparing the total volume of concrete work between the calculation using Tekla Structures and secondary data had an accuracy rate of 87.61%, with a deviation of 12.39%. Meanwhile, the total volume of reinforcement works showed an accuracy of 95.66% with a deviation of 4.34%. This insignificant volume difference indicates that Tekla Structures can be relied upon to calculate the volume of work, especially in detailing and reinforcement. Although the accuracy of volume calculation is already high, improvement of the BIM model is still needed to make it closer to the actual conditions in the field. One of the efforts that can be made is to integrate real-time data from the field into the BIM model so that the accuracy of calculations increases and can support planning and project management more effectively.

A Combined Modeling Approach for Delay Management in Construction Projects Based on a Two-objective Mathematical Model and System Dynamics

A Combined Modeling Approach for Delay Management in Construction Projects Based on a Two-objective Mathematical Model and System Dynamics

USING THE DELPHI TECHNIQUE AND EFQM MODEL APPROACH TO BUILD A CONCEPTUAL MODEL FOR CONSTRUCTION PROJECT PERFORMANCE

This study presents a new conceptual framework for evaluating the performance of construction projects, based on an adaptation of the European Foundation for Quality Management (EFQM) model. Although the EFQM model is widely used for quality management and performance evaluation, there have been few studies to date that have used it to measure the performance of projects, which is a very important issue in the construction industry. The EFQM model is designed for organisations, so if it is to be used for construction projects, appropriate modifications are needed. That is, the criteria and attributes of the original EFQM model need to be modified. Therefore, the aim of the study is to develop a new set of criteria and their attributes. The attributes are drawn from previous studies and adapted to construction projects. The set of criteria and their attributes form a conceptual model for measuring construction project performance. The model was validated through semi-structured interviews with 20 experts. The results of the study showed that the conceptual model had five enabler criteria, two result criteria, and 50 criteria attributes. The qualitative and quantitative analysis results of the Delphi technique confirmed the importance of these criteria and their reliability. The results of the study will contribute to promoting the application of the EFQM model in the field of construction project management. In addition, stakeholders can use the model as a tool to evaluate the performance of construction projects.

AI-driven risk mitigation: Transforming project management in construction and infrastructure development

This study explores the transformative role of artificial intelligence (AI) in risk mitigation within construction and infrastructure development, addressing the growing complexities of modern project management. The purpose of the study was to evaluate how AI technologies, frameworks, and strategies can mitigate environmental, economic, and social risks while fostering sustainability and operational efficiency. Utilizing a multidisciplinary approach, the research integrates theoretical and practical perspectives to examine AI’s applications, ethical considerations, and future trends in project management. The study identifies predictive analytics, Internet of Things (IoT)-based monitoring, and blockchain technology as pivotal tools for enhancing decision-making, resource optimization, and transparency in project workflows. It underscores AI’s significant contributions to sustainability through climate-resilient design, energy-efficient systems, and environmentally compliant practices. Ethical and legal frameworks emerged as essential to ensuring the equitable and responsible deployment of AI, focusing on data privacy, accountability, and algorithmic fairness. Key findings demonstrate that AI-driven risk mitigation enhances project outcomes by reducing delays, minimizing costs, and fostering inclusivity. Applications in public health campaigns and social housing highlight AI’s capacity to address societal challenges while advancing sustainability goals. However, the study also identifies critical challenges, including organizational resistance, data quality limitations, and regulatory gaps, which hinder the full integration of AI technologies. The study concludes that AI is a transformative force in risk mitigation, offering innovative solutions to the multifaceted challenges of modern project management. To fully leverage its potential, the research recommends fostering interdisciplinary collaboration, developing adaptive ethical and legal frameworks, and advancing AI integration with emerging technologies like IoT and blockchain. Investments in education and training are also emphasized to bridge technical knowledge gaps and empower stakeholders.

Critical factors for implementing sustainable construction practices in project delivery and management

Introduction/Background: The construction Industry is central to world economic growth and the ongoing process of urbanization. However, it is also an environmental problematic area as it causes pollution, resource depletion and emission of the greenhouse gases. Modern construction is experiencing significant concerns with sustainability hence the need to shift from the conventional construction practices and embrace sustainable construction. This paper looks at key issues based on construction project delivery and management for sustainability. It also focuses on how the industry can maintain the triple bottom line of sustainability in the different phases of project life cycle. Materials and Methods: This research uses literature review as the method for integrating and analyzing the existing knowledge in the construction sustainable practices. Both qualitative and quantitative data collection methods were used in the present study and the most relevant sources of information consisted of peer-reviewed journal articles, industry reports and literature as well as sustainability assessment tools were also used to analyse the findings and identify the major themes and effective measures in the field. The study focuses on three main areas: A)Functions of construction business in sustainable development, B)Measures towards improving sustainability in social, economical and environmental aspects in construction projects and C)Sustainable construction project management. Further, the research assesses sustainable assessment tools with more emphasis put on sustainable neighborhood development through LEED-ND. Results: The result finds several key factors for supporting a sustainable construction approach. They are; procurement and management of materials, use of green building practices, use of LCA in decision making and engagement of stakeholders. A viewpoint for the research is an integrated approach to the use of sustainable practices within the processes of implementing particular projects, taking into consideration the environmental, social and economic factors at the various stages of the project. Simultaneously, the LEED-ND has presented itself as an all-encompassing instrument for measuring and directing the sustainable development of neighbourhoods, focusing on both the level of buildings and of whole communities. Discussion: These are some of the key findings which indicate the interaction between different sustainability issues in construction projects. However, unlike other approaches that focus on the use of technology or even green building, the study brings out the issue of integrated design process, stakeholder engagement, and long term thinking as critical in sustainable development. They also confirm the effectiveness of targeted and case-based approaches because the issues of sustainability and their priorities differ from country to country and from one type of construction project to another. Conclusion: The need to understand critical implementation factors for sustainable construction is what has informed this research work and so, this study will add to the existing body of knowledge the world over. It stresses the imperative for constructing higher order change in the construction industry towards the type of sustainability solutions that are based on the idea of utilising a life cycle approach to problem solving. Therefore, understanding the nature and the degree of the elements impacts will help the practitioners, policymakers and the researchers to design measures towards making construction projects sustainable. Future studies should build on stronger linkages of impacts, longer time horizons of assessment, and the identification of new sources of financing sustainable practices

Enhancing sustainable performance with green construction based on local wisdom through implementation strategy as a mediating variable

+This research examines the integration of local wisdom-based green construction practices and their influence on sustainable performance through implementation strategies in the Indonesian construction industry. It focuses on ten construction projects in Bali. The main problem faced is the lack of understanding of the effectiveness of local wisdom-based green construction practices in improving sustainable performance in developing countries. Using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, data was collected from 200 engineers involved in green construction projects. Results show that local wisdom-based green construction practices significantly influence sustainable performance, both directly (path coefficient 0.290) and indirectly through the mediation of implementation strategies (indirect effect 0.575). The research model shows high explanatory power with R-squared values of 0.802 for Implementation Strategy and 0.831 for Sustainable Performance. These findings indicate that integrating local wisdom in green construction practices when mediated by an effective implementation strategy, can significantly improve the sustainable performance of construction projects. Sustainable waste management was identified as the dominant indicator (loading factor 0.936) of local wisdom-based green construction. In contrast, alignment with government policies and regulations (loading factor 0.925) became critical in the implementation strategy. The results of this study can be applied by project developers, construction managers, and policymakers in designing more effective implementation strategies for integrating local wisdom into green construction practices, considering customizations based on variations in cultural and geographical contexts

Quantity Surveying and its Association with Building Information Modeling (BIM) and Digital Twin (DT)

Quantity surveyors (QS) have many roles in the construction industry, and one of such roles is preparing bills of quantities that mainly entails taking measurements and pricing of construction work. They also develop schedule of project work, engage in construction project management, arbitration as well as dispute resolutions. Quantity take-off was their original role during 1820s when QS profession first came into existence. Critical observations brought criticisms that the profession would not survive if a tool could be developed that substituted QS roles in construction. Contingent to that criticism has seen the emergence of Building Information Modeling (BIM) that has specific attributes where the BIM technology can be used to quantify building works and produce schedule of works including cost loaded schedule in a 4D and 5D model interface respectively. Thus far, has the BIM technological innovation posed threats to the QS roles and/or to the profession? What of digital twin (DT)? The aim of this research was to investigate the QS and its association with BIM and DT innovation in the construction industry and to evaluate their future in the industry. Are they a bane or boon to the QS profession? Specific objectives were to determine the extent of BIM usage by QS, the level of the current usage and the future of QS considering BIM growth in the construction field. Strength, Weakness, Opportunity and Threat (SWOT) analysis was conducted about BIM usage. Survey questionnaire was administered to investigate the use of BIM and DT, current usage and future standing in construction. The results showed that BIM and DT were opportunities that added to the progress of QS. It was concluded that QS needed to embrace BIM technology more with integration of digital twins for full benefits in the construction industry.

The Role of Artificial Intelligence in Aviation Construction Projects in the United Arab Emirates: Insights from Construction Professionals

The applications of Artificial Intelligence (AI) in the airport industry are significantly transforming operational efficiency, safety, and passenger experiences. This study investigates the integration of AI within aviation construction projects, with a focus on the United Arab Emirates (UAE). While AI technologies such as facial recognition, IoT, and biometric systems have advanced airport security and operations, their use in construction project management remains limited. A survey was conducted among 101 engineering professionals and experts with experience or involvement in managing aviation-related construction projects. Participants, many of whom had familiarity with AI tools, provided insights into the applicability of AI in areas such as planning, scheduling, and safety monitoring. The majority agreed that AI has the potential to revolutionize project management processes, improving decision-making, and efficiency. AI tools can predict delays, optimize workflows, and enhance safety through real-time data analytics and machine learning algorithms, reducing risks and human error. Despite the UAE’s leadership in AI-driven security advancements, its use in aviation construction is still underdeveloped. This research highlights the potential for broader AI integration across the entire lifecycle of aviation projects. By adopting AI in these areas, UAE airports could set new benchmarks for cost effectiveness, sustainability, and project delivery, reinforcing the region’s status as a leader in technological innovation within the aviation industry.

A conceptual cost estimation model for building construction projects by hybrid Back-Propagation Neural Network and Dung Beetle Optimizer algorithm

ABSTRACT Accurate conceptual cost estimation is vital in construction project management for effective feasibility studies before project initiation. Relying on rough experiential estimates can lead to significant errors and constrain bid prices, risking financial losses. Machine learning (ML) offers a way to bypass expert input and manual quantity surveying, addressing the challenge of inadequate initial estimation data. However, a demand-oriented conceptual cost estimation model based on ML is lacking in the preliminary design phase to assess cost-influencing factors comprehensively. This research develops an optimal model by comparing the conceptual cost estimation performance of hybrid Dung Beetle Optimizer (DBO) + Back-Propagation Neural Network (BPNN), Genetic Algorithm (GA) + BPNN, Particle Swarm Optimization (PSO) + BPNN, and single BPNN. First, the 20 key input variables affecting conceptual cost estimation were determined by the SHAP and correlation matrix together; Second, after the simulation experiment by using a dataset of 117 general building projects in MATLAB, the DBO+BPNN model with 12 hidden layer neurons achieves the best performance in cost estimation by the performance comparison based on score maker methods with 2 correlation metrics and 5 accuracy metrics. Importantly, implementing this model can provide decision-makers with reliable conceptual cost information, enhancing project success likelihood.

Improving Improving Efficiency and Accuracy of Quantity Take-Off Through BIM Integration: A Case Study of the D Lecture Hall Building at Malikussaleh University

Abstract. Quantity Take-Off (QTO) calculation is a crucial step in construction planning to determine material needs with high accuracy. However, traditional methods such as manual calculations often fail to meet accuracy demands, especially in projects with complex designs. This study compares manual methods, Revit, and Clash Detection in generating QTO for the D Lecture Hall building project at Malikussaleh University. The aim of this research is to analyze the quantitative differences between methods and assess which method is the most effective. The research method involves calculating the QTO for structural and architectural elements using the three methods, followed by an analysis of the average differences in results. The findings show that the manual method has an average difference of 35.76% for structural elements and 43.14% for architectural elements when compared to Revit. The difference between manual and Clash Detection is slightly smaller, with 33.09% for structural elements and 42.41% for architectural elements. Conversely, the difference between Revit and Clash Detection is very small, only 1.51% for structural elements and 3.98% for architectural elements. While the QTO results between Revit and Clash Detection are almost identical, Clash Detection offers the additional advantage of validation, ensuring that the design is free from clashes between elements. This study concludes that Clash Detection is the most effective method in generating QTO because it not only provides accurate results but also ensures that the design is ready for implementation without technical errors. By integrating BIM technology, this research offers a more efficient and accurate approach to QTO calculation, serving as a guideline for construction project managers in handling complex designs more effectively. Keywords: Quantity Take-Off (QTO), Clash Detection, Building Information Modeling (BIM).

A cost estimation recommendation system for improved contingency management in construction projects

A cost estimation recommendation system for improved contingency management in construction projects

A Digital Simulation Game for Resource Management in Construction Projects

The construction industry is on the verge of a digital transformation. Consequently, the gap widens between industry demands and fresh graduate capabilities. Therefore, there is a pressing need for a paradigm shift in the teaching approach for construction management education. To meet both industry demands and student expectations, Game-Based Learning (GBL) can substantially enhance both technical and transferable skills for digital-native students who struggle with traditional teaching methods. Although the body of knowledge recognizes GBL in general, their utilization remains limited in construction management. Moreover, there is little discussion on the design process of educational games. To address these gaps, this study adopted a unique approach to educational game design by developing a time-management strategy game about resource management in construction projects with unpredictable events. This paper presents the game design process and evaluation results of a single-player digital game called ‘Always Under Stress’ developed using the Godot game engine. The goal of the game is to introduce resource management in construction projects and its associated challenges. The design and development steps, game engine, and workflow are discussed in-depth in relation to the learning objectives. In addition, this study explains how elements of randomness in educational game design can be used to challenge students’ planning and adaptability skills. Various stakeholders (n = 31), comprising of students, professors, and industry professionals, were invited to evaluate the game across six dimensions of educational game design. Results revealed positive reception towards the developed prototype regarding educational value and critical comments regarding user experience and onboarding for beginners. The findings of this study provide invaluable guidelines, considerations and lessons learned for prospective educational game designers and researchers.

Project Management Using Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT)

Project delivery within the established time line and project cost is one of the persistent issues in current project management practices. This work therefore aimed at assessing the appropriateness of Project Evaluation Review Technique and Critical Path Method in project management with a case study of a UNN lecture hall construction. Consequently, the research establishes the impact of these quantitative operations research tools on the efficient project management. Both of these project management techniques were explained and used in relation to the data that was obtained from the lecture hall construction project manager, concerning the project activities and the time taken for each of these activities. As indicated by the findings of this study, both the methods were posited to achieve success in project management where by relationship and connectivity of the activities that define a project life cycle persist as the key issues. As such, it was suggested that in view of the project critical activities determined by the CPM and/or PERT analyses, more resources and attention should be directed towards effective management of such activities to avoid the occasion of project delay as well as ensure the successful completion of projects.