Project Life Cycle Research Articles

The impact of BIM on project time and cost: insights from case studies

Building Information Modelling (BIM) has emerged as a transformative tool in the Architecture, Engineering, and Construction (AEC) industries, offering significant potential to improve project efficiency and outcomes. This study explores the impact of BIM implementation on project time and cost by analyzing critical factors such as design errors, unbudgeted changes, Requests for Information (RFIs), labour dynamics, and scheduling across multiple case studies. Quantitative results reveal that BIM adoption reduces project timelines by an average of 20% and costs by 15%, while also decreasing design errors by 30% and RFIs by 25%. Furthermore, BIM enhances collaboration among stakeholders, improves design visualization, and facilitates better risk assessment, leading to more informed decision-making throughout the project lifecycle. These findings are synthesized into a decision-making framework that estimates the probability of errors, evaluates their potential time and cost implications, and ensures alignment with project budgets. The framework serves as a strategic guide for project teams to assess the suitability of BIM for specific projects, thereby optimizing decision-making processes and improving overall project performance. Additionally, the study examines the role of BIM in sustainability by reducing material waste and improving resource allocation. This study addresses a critical gap in the field by systematically evaluating the interrelationships among BIM's impacts on key project parameters, which have often been treated in isolation in prior research. The importance of this work lies in its provision of a structured methodology to harness BIM's capabilities, demonstrating its value in delivering significant time and cost efficiencies while enhancing project quality. By integrating empirical analysis with practical applications, this research contributes to the growing body of knowledge on BIM adoption and provides actionable insights for AEC professionals seeking to optimize project outcomes.

Assessing BIM adoption in the Canadian construction industry: a fuzzy Bayesian network approach

Building information modeling (BIM) adoption has been recognized as a promising development within the construction industry. While BIM offers significant advantages for managing the entire lifecycle of construction projects, its uptake in the Canadian construction sector remains limited. This study employs Bayesian belief networks (BBN) to evaluate the factors influencing the successful implementation of BIM in Canada. Key drivers were identified through a comprehensive literature review, while occurrence probability data were gathered from Canadian construction professionals and analyzed using BBN. The results of this cross-sectional analysis indicate that under current conditions, the probability of successful BIM implementation is relatively low. To improve the likelihood of successful adoption, addressing the shortage of critical BIM resources is essential. The proposed BBN framework serves as a prototype for assessing BIM implementation success across various regions, providing valuable insights for industry stakeholders

Volume, Risk, Complexity: What Makes Development Finance Projects Succeed or Fail?

Abstract This study presents a quantitative analysis of the determinants of success—and failure—of bilateral development finance projects, based on a unique database covering 5,608 project evaluation results. Linking project success ratings to a comprehensive and partly novel set of explanatory factors along the project life cycle, four research hypotheses are investigated, finding that (I) development finance projects with comprehensive financing are (slightly) more successful, in particular budget funds show a significant positive correlation; (II) the project structure does not significantly influence project success; (III) more-complex development finance projects are less successful, in particular in terms of technical complexity and longer implementation duration; (IV) riskier projects are significantly less successful.

Seeking a Definition of Digital Twins for Construction and Infrastructure Management

The integration of digital twins (DTs) in construction is still in its infancy compared to other sectors. However, the potential for optimising project lifecycle management is significant, promising transformative impacts on safety and operational performance. In this study, the evolution of technologies preceding DTs is explored. A detailed description of the various platforms where DTs can be implemented is discussed and parallels are established with other sectors, such as manufacturing and healthcare, highlighting the successful application of DTs in these fields. The key benefits of integrating DTs in the construction industry and complex infrastructure management are assessed, emphasising that the accuracy of asset representation is crucial for their effective utilisation. Moreover, the challenges associated with recording, storing, and accessing both static and dynamic data are discussed, providing insights into the pros and cons of managing data through back-end versus front-end processes. Case studies of a transport railway station and an educational centre illustrate the practical applications and advantages of DTs, such as enhanced visual representation, improved understanding of construction and management dynamics, real-time information integration, and collaborative management processes. This paper advocates for the first steps toward establishing a European definition of DTs and standardising the relevant processes.

System-Level Critical Success Factors for BIM Implementation in Construction Management: An AHP Approach

Digital tools are transforming the construction industry, reshaping how projects are designed, managed, and delivered. Building Information Modeling (BIM), a cornerstone of this transformation, requires a systemic approach because its implementation spans several organization functions, involves multiple stakeholders, and encompasses all phases of the project life cycle. While extensive literature examines BIM adoption, there is no consensus on its key enablers and barriers nor a ranking of their impact on implementation success. This study investigates the system-level critical success factors (CSFs) for BIM adoption in construction management. First, it reviews earlier literature, identifying 18 CSFs across six dimensions: change management, process efficiency, regulatory compliance, strategic alignment, technology integration, and user training and support. Next, it utilizes the AHP method to rank the CSFs based on the data collected from 31 construction professionals. Results highlight the importance of aligning BIM initiatives with organizational strategies, streamlining workflows, fostering collaboration, and ensuring compliance with evolving regulations. The research concludes that effective BIM implementation requires holistic strategies that emphasize leadership, scalable technology integration, comprehensive training, and adaptability. By addressing these system-level CSFs, organizations can enhance efficiency, drive innovation, and strengthen resilience in an evolving construction landscape.

Reforming legal and regulatory structure of public–private partnerships in Sri Lanka

PurposeThis paper aims to investigate the current practices and deficiencies in the legal and regulatory structure of public–private partnerships (PPPs) in Sri Lanka and explore recommended practices by international model laws and guidelines addressing such deficiencies.Design/methodology/approachFirstly, a desk study review was conducted to explore the current practices of PPP in Sri Lanka. Secondly, nine expert interviews were conducted to comprehend further current PPP practices and identify the deficiencies of legal and regulatory structure of PPP in Sri Lanka, and content analysis was used to analyse the interviews findings. Finally, international model laws and guidelines were reviewed to explore recommended best practices.FindingsThe research explored non-existence of a national PPP policy, lack of clarity of executing a project’s preliminary affordability, and lack of guidance on risk assessment and feasibility studies as current major deficiencies of PPP legal and regulatory structure in Sri Lanka. Further, findings showed that setting-up two project governance structures, a steering committee and a project team, and develop clear guidelines and standard forms can be adapted to strengthen the PPP legal and regulatory structure.Research limitations/implicationsThe findings of this study may be useful to policy makers in tracing essential reforms to PPP legal and regulatory structure. This study is limited to legal and regulatory structure of first three phases of the PPP project life cycle: project identification, project preparation and project procurement.Originality/valueThis paper establishes a novel framework to visualise the current practices and deficiencies in the PPP legal and regulatory framework in Sri Lanka, and the way of incorporating recommended practices by international model laws and guidelines for its future reformations.

Sustainability and Stakeholder Engagement in Building Information Modelling-Enabled Construction: A Review of Critical Success Factors in Design and Planning Phases

This scoping literature review examines critical success factors (CSFs) in the design and planning phases of BIM-enabled construction projects, focusing on integrating sustainability practices across the quadruple bottom line: profit, people, planet, and process. By introducing the novel ‘Process’ pillar, this study aims to bridge critical gaps in sustainability research, emphasising technology-driven practices and mapping 62 CSFs from 31 studies to specific stakeholder roles, and focuses on early project phases in which decisions have the most significant impact on long-term outcomes. The findings highlight how early-phase decisions, guided by the QBL framework, can optimise project outcomes and drive long-term sustainability through effective stakeholder engagement. Despite advancements, the design and planning phases of BIM-enabled construction projects consistently exhibit an underrepresentation of end users and environmental considerations. These omissions highlight inadequacies in stakeholder engagement, which are critical for achieving comprehensive sustainability and aligning project outcomes with user needs and environmental goals. This research maps CSFs to corresponding stakeholders, revealing a complex network with the BIM coordinator/manager playing a pivotal role. This mapping underscores the importance of integrating stakeholder inputs, particularly from end users, early in the project lifecycle to enhance functionality and ensure the long-term viability of construction projects. However, current practices often overlook this, leading to a potential misalignment between project deliverables and user expectations. The construction industry can move towards more sustainable, efficient, and user-focused outcomes by addressing these gaps. This study calls for a paradigm shift in BIM methodologies to adopt a construction environment that is not only efficient but also adaptable to the needs of its users and the environmental imperatives of today's world.

Measurement of Project Cost and Time Efficiency with Earned Value Approach In The Implementation of Baggage Handling System at Sultan Hasanuddin International Airport Makassar

The installation of the Baggage Handling System (BHS) at Sultan Hasanuddin International Airport, Makassar, faces challenges related to cost and time inefficiencies, particularly delays in project completion. This study aims to evaluate these inefficiencies by applying the Earned Value Method (EVM) to assess project performance and identify areas for improvement. Utilizing EVM metrics, the research identifies that the Schedule Performance Index (SPI) is below 1, indicating a delay in the project timeline, with the actual duration extending to 556 calendar days compared to the planned 546 days. This delay underscores the need for a more robust project management approach. The study employs the Critical Path Method (CPM) in conjunction with Microsoft Project to improve scheduling accuracy, alongside recommendations for project acceleration using the Crashing Program method. The findings highlight that early implementation of EVM can help mitigate discrepancies in cost and time, enabling better control and monitoring throughout the project lifecycle. The study concludes that integrating advanced scheduling techniques and real-time performance metrics is essential to achieving efficiency and minimizing delays in similar infrastructure projects.

Digital Twins and AI Decision Models: Advancing Cost Modelling in Off-Site Construction

The rising demand for housing continues to outpace traditional construction processes, highlighting the need for innovative, efficient, and sustainable delivery models. Off-site construction (OSC) has emerged as a promising alternative, offering faster project timelines and enhanced cost management. However, current research on cost models for OSC, particularly in automating material take-offs and optimising cost performance, remains limited. This study addresses this gap by proposing a new cost model integrating Digital Twin (DT) technology and AI-driven decision models for modular housing in the UK. The research explores the role of DTs in enhancing cost estimation and decision-making processes. By leveraging DTs and AI, the proposed model evaluates the impact of emergent technologies on cost performance, material efficiency, and sustainability across social, environmental, and economic dimensions. As proposed, this integrated approach enables a cost model tailored for OSC systems, providing a data-driven foundation for cost optimisation and material take-offs. The study’s findings highlight the potential of combining DTs and AI decision models to enhance cost modelling in modular construction, offering new capabilities to support sustainable and performance-driven housing delivery. The paper introduces a dynamic, data-driven cost model integrating real-time data acquisition through DTs and AI-powered predictive analytics. This dynamic approach enhances cost accuracy, reduces lifecycle cost variability, and supports adaptive decision-making throughout the OSC project lifecycle.

Organising digital twin in the built environment: a systematic review and research directions on the missing links of use and user perspectives of digital twin in Architecture, Engineering and Construction (AEC) sector

There has been growing interest in the adoption of advanced digital technologies in the Architecture, Engineering and Construction (AEC) sector, as shown by the increasing number of review studies on the adoption of Digital Twin (DT) in the sector. While previous reviews on DT in the AEC sector have emphasised the technical opportunities and challenges, reviews that take a specific organisational and management focus on the use of DT are lacking. In this systematic review, we close this gap by analysing 102 papers from three main scientific databases (Google Scholar, Scopus and Web of Science), which emphasise organisational and management aspects of using DT in the AEC sector across the project and asset lifecycle phases. The findings indicate that, whereas there is still a stronger focus on finding optimisations for various performance concerns (e.g. project efficiency, health and safety and maintenance performance), there is increasing awareness and interest in using DT to encourage collaboration and (end-)user participation. Despite this recognition, our review also identified a number of critical knowledge gaps on the use and user perspectives of DT, which includes how powerful computational tools can be developed into user-friendly interfaces to reduce complexity and increase accessibility to all stakeholders in the decision-making process, and how participatory design frameworks can be combined with structured feedback systems and standardised data access protocols in order to stimulate communication, continuous improvement, and data-driven decision making.

The Role of Artificial Intelligence in Managing Scientific Research Projects Funded by KEGA and VEGA Grant Schemes

The article examines the application of Artificial Intelligence (AI) in managing scientific and educational projects funded by the KEGA and VEGA grant schemes in Slovakia. The objective is to analyse how AI contributes to more efficient management of project processes, including the initiation, planning, implementation, monitoring, and closure of projects. Research identifies key areas where AI provides significant benefits, such as automation of administrative tasks, optimisation of resource allocation, and decision-making support through predictive analysis. The article also highlights the utilisation of AI tools, such as chatbots and advanced machine learning algorithms, to improve communication and monitoring project activities. The findings indicate that AI not only facilitates the complex grant management but also improves the quality of achieved outcomes. Despite these positive contributions, the research has revealed several challenges, including insufficient integration of AI in certain phases of the project lifecycle and concerns about the ethical use of these technologies. Recommendations include the need to further education of project managers in AI-related areas and the development of strategies for their effective implementation. The article offers a fresh perspective on the use of AI in project management and introduces valuable tools and methodologies that can help project teams in achieving superior results.

Avoiding harm or creating benefit? How a risk focus sidelines social considerations in early decisions for Australian infrastructure projects

ABSTRACT There is a growing need to maximise the social benefits achieved from public investment in infrastructure, particularly with the transition to net zero economies. Infrastructure projects around the world contractually define roles and responsibilities for project partners via Project Delivery Models (PDMs). PDMs can underpin social benefit realisation, but the processes used to select them are largely opaque. This paper explores how social considerations inform PDM selection and how this is facilitated by policy. We interviewed senior procurement professionals about how social benefits and risks are considered, and analysed auditing guidance documents to examine how the regulatory environment supports social considerations. We found that decisions are based on risk minimisation rather than benefit creation, but social risks are inadequately considered. This situation is linked to an entrenched gap in social expertise and inadequate information handover between different teams over project lifecycles, often resulting in an operational disconnect between project phases. This ‘compartmentalisation’ presents challenges for inclusion and transparency in decision-making. Current auditing processes provide little incentive for social benefit consideration and reinforce a risk focus. We offer important insights into this early project stage and distil five recommendations for improving social benefit creation from infrastructure investments, particularly in developed economies.

A BIM-Enabled Workflow for the Rehabilitation of Heritage Steel Bridges

The rehabilitation of heritage steel bridges raises specific challenges due to their historical significance and structural complexity. These structures, often subjected to ageing, corrosion, and insufficient documentation, require specialized approaches that balance preservation with modern infrastructure demands. In this context, digitalization offers a transformative pathway, enabling more precise maintenance planning, enhanced decision-making, and better alignment with sustainability goals, with Building Information Modelling (BIM) fostering seamless information transfer and integration across project lifecycle. This paper highlights the potential of BIM in predictive maintenance, resource optimization, and strategic rehabilitation planning. It proposes a structured approach for defining and implementing information requirements (IRs), ensuring alignment between organizational objectives, asset-level needs, and project-specific deliverables. A significant contribution of this research is the development of a template for information delivery specification (IDS), providing a robust framework for data exchange and quality control throughout project phases. The IDS supports the integration of technical and historical data into a centralized digital repository, addressing both operational and heritage preservation needs. Finally, the article discusses structural assessment and strengthening strategies within the BIM workflow, emphasizing their role in achieving efficient and sustainable bridge rehabilitation.

Risk Assessment in Mass Housing Projects Using the Integrated Method of Fuzzy Shannon Entropy and Fuzzy EDAS

Mass building projects play a key role in the economic prosperity of any country. Furthermore, these projects are among the main drivers of environmental and social problems. In recent years, with the spread of the concept of sustainable development in the life cycle of construction projects and the dynamic and eventful nature of these projects, the issue of risk management in the sustainable construction industry has received more and more attention among researchers. The construction industry, like other industries, faces various risks. Therefore, it is crucial to identify and evaluate risks in mass construction projects due to the high volume of work. In this study, an integrated model based on fuzzy Shannon entropy and fuzzy EDAS is proposed for risk assessment in large-scale building projects. Initially, by reviewing related articles, 66 effective sub-indicators are identified and classified into 18 risk categories, including 6 external risks and 12 internal risks. Subsequently, a questionnaire is designed to assess the three factors of detection, probability of occurrence, and severity risks for each risk index. This questionnaire distributes to 15 mass production companies in the construction field in Isfahan. The fuzzy Shannon entropy method is then applied to determine the weight of risk factors. The weights of each factor, detection, probability of occurrence, and severity, are calculated as 0.386, 0.342, and 0.273, respectively. These weights are used in the fuzzy EDAS method to prioritize the identified risks in mass-building projects. The results of the fuzzy EDAS method determined the three most critical risks: “inflation rate volatility”, “import/export restrictions”, and “unforeseen climatic conditions”. Additionally, three low-risk sub-indicators are obtained: “limitation on working hours”, “collapse of the structure”, and “unpredictable fire”.

Boosting circular economy solutions in the construction sector using a life cycle assessment

AbstractConstruction and demolition waste (CDW) management activities have had wide‐ranging adverse impacts on the Earth's climate. Addressing the problems of construction and demolition waste production requires a comprehensive and multifaceted approach and specific attention to the development of sustainable circular on‐site practices. This study compares recycled products against conventional products already available on the market. This study applied a life cycle assessment (LCA) to evaluate the benefits of secondary raw material (SRM)‐based products compared to conventional virgin raw materials. Focusing on a Spanish case study that had foreseen the revitalization of a degraded area, the construction of a small utility building, and road construction, this analysis also assessed the overall environmental footprint of the overall construction work. The results showed that, despite the impact savings achieved by opting for SRM‐based products rather than conventional solutions, the production stages still have the greatest impact on the scale of construction, with the sole exception of the construction of small facilities where the construction itself was the largest contributor. Climate change; water scarcity; freshwater eutrophication; resource use, energy carriers; and resource use, minerals and metals were the most significant impact categories both in the analysis of the construction products and the overall construction work. An LCA is vital in the construction and demolition sector because it provides a holistic understanding of the environmental impacts associated with the different stages of a project's life cycle fostering the implementation of circular on‐site practices.

A dynamic risk interdependency network-based model for project risk assessment and treatment throughout a project life cycle

A dynamic risk interdependency network-based model for project risk assessment and treatment throughout a project life cycle

Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects

Project Risk Management contains processes ranging from planning to control. It is applied to identify risks, analyze them, and design responses to change the occurrence rate and/or the effect of project risks. It is important for project managers to analyze the effects of the risks in projects and also consider project risks in their decisions. If project risks are not addressed during the risk management process, issues such as schedule delays, cost overruns, and even project failure may occur. This paper aims to introduce a Markov method to integrate project risk analysis and risk response planning. This method is applied to forecast the following status of the project when limited information about the project is available. Moreover, earned value management (EVM) methods were used to include various types of project risks through the project lifecycle. The model also offers the capability to choose the most effective risk response for managing project risks through the application of the Markov decision process (MDP). Eventually, we introduce a case study to demonstrate functionality and effectiveness of the presented approach. Solving the model allows for identifying the best set of risk response strategies tailored to each specific project state. The computational results illustrate that the current state of the project has a significant impact in the process of risk response planning. Since uncertainty is the inherent characteristic of projects, the use of the project’s current state is more reliable than the previous status of projects, and the Markov method is applied in this research because it uses the current state for its modelling. Using this method, managers can predict the future state of projects and find the best response in each status of projects.

BEMTrace: Visualization-driven approach for deriving Building Energy Models from BIM.

Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a basis for thermal simulations to assess the building's energy performance. This paper explores the intersection of BIM and BEM, focusing on the challenges and methodologies in converting BIM data into BEM representations for energy performance analysis. BEMTrace integrates 3D data wrangling techniques with visualization methodologies to enhance the accuracy and traceability of the BIM-to-BEM conversion process. Through parsing, error detection, and algorithmic correction of BIM data, our methods generate valid BEM models suitable for energy simulation. Visualization techniques provide transparent insights into the conversion process, aiding error identifcation, validation, and user comprehension. We introduce context-adaptive selections to facilitate user interaction and to show that the BEMTrace workfow helps users understand complex 3D data wrangling processes.

A Theoretical Proposed Frameworks to Satisfied Sustainability in Iraq's Building Industry

The current research introduces a theoretical framework developed to integrate sustainability ideas into the construction sector. The suggested framework, established on the sustainable triple-bottom paragraph concept, encompasses resource preservation, expensive reduction, and planning that emphasizes the well-being. After conducting a comprehensive literature assessment, each concept related to strategies and methodologies applicable throughout the life cycle of construction projects is elucidated, accompanied by many case examples to illustrate the approaches. The framework will enable designers to achieve a suitable equilibrium among economic, cultural, and environmental considerations, transforming the mindset of construction practitioners regarding the information utilized in evaluating building projects, thus promoting the sustainability of the Iraqi construction industry.

A Review of SDLCs for Big Data Analytics Systems in the Context of Very Small Entities Using the ISO/IEC 29110 Standard-Basic Profile

Context: A Systems Development Life Cycle (SDLC) is a model of phases-activities, roles, and products systematically used to develop software with functional expected quality. Although SDLC is widely applied to various software types, it remains unusual in Big Data Analytics Systems (BDAS). Objective: To address this issue, several SDLCs for BDAS have been proposed, along with comparative studies, to guide interested organizations in adapting them. This research seeks a lightweight, balanced, and feasible for small development teams or organizations, taking advantage of favorable characteristics of the international ISO standard. Method and Materials: This study describes the knowledge gap by reporting a comparative analysis of four relevant SDLCs. A selective research method was applied (CRISP-DM, TDSP, BDPL, and DDSL), focusing on alignment with the recent ISO/IEC 29110-basic profilestandard. The goal was to identify which SDLC contributes and fits better from a lightweight approach. Results: From the rigorous approach Cross Industry Standard Process for Data Mining (CRISP-DM) showed the highest alignment with the standard, for the agile approach it was Domino Data Science Lifecycle (DDSL) being the closest of the four. Team Data Science Process (TDSP) stood out as the most agile of those analyzed but fell short of the required results. BDPL, which manages another standard, was too rigorous and more distant. Conclusions: Research on new SDLC for Big Data Project Lifecycle (BDPL) has been practically nonexistent in software engineering from 2000 to 2023. Only BDPL was found in the academic literature, while the other three came from gray literature. Despite the relevance of this topic for BDAS organizations, no adequate SDLC was identified