Delay Causes and Emerging Digital Tools: A Novel Model of Delay Analysis, Including Integrated Project Delivery and PMBOK

Proposal for the Integration of the Assessment and Management of Electrical Risk from Overhead Power Lines in BIM for Road Projects

Electrical risk has a particular impact within the construction sector. This leads to the development of regulations to mitigate it, but correct application of regulations is impossible with a traditional 2D analysis. The construction sector is using technologies from the industrial sector (Construction 4.0), with BIM as the main enabling technology. Thus, the objective of this article is the evaluation of the risk produced by Overhead Power Lines (OPL) through BIM integration. The OPL, its risk zones, the affected road, and the envelope resulting from the geometry of the necessary machines to build it were digitized, converging in a single model to perform a 4D risk analysis. The risks of the execution of the embankment and road surface of a road section passing through an OPL were analyzed by means of the collision of the envelope with its risk zones, resulting in an integration of their evaluation, to which was added the introduction of preventive measures and their re-evaluation. The parametric 3D modelling allowed a better definition of the risk zones and the BIM management minimized errors, providing traceability of decisions from the design phase, complying with health and safety regulations and applying the principle of Prevention through Design (PtD).

Exploring the Application of BIM Technology in the Whole Process of Construction Cost Management with Computational Intelligence

The construction industry is a labor-intensive industry in China. In recent years, as people's living standards have risen, so have their requirements for the functionality, appearance, and comfort of buildings. The amount of information attached to construction projects is also increasing, especially for some modern large-scale construction projects. Due to the long construction period and a large amount of information in the construction process, the difficulty of project engineering management has also increased significantly. At the same time, with the gradual development and improvement of China's market economy system, the prices of labor and materials in the construction industry continue to rise, and the huge consumption of energy and resources no longer meets the strategic requirements of sustainable development. Therefore, construction project cost management is facing a huge impact, the actual effect of construction project cost management work is not good, and the phenomenon of uncontrolled construction project investment commonly occurs. The use of BIM software to build a building information model can integrate information from all stages of the project and facilitate the participation and cooperation of all project entities. Cost management is carried out before the actual construction of the project, thus realizing the whole process of cost management and effectively avoiding the occurrence of actual costs exceeding the budget after the project is completed. The study analyses the suitability of applying BIM technology to the whole process of construction project cost management with computational intelligence and explains the value and advantages of BIM in the whole process of construction project cost management. On this basis, this research provides a specific analysis of the application of BIM technology in the process of cost management at various stages of the whole process of construction projects and thus puts forward suggestions that can solve the obstacles that may be encountered in the whole process of cost management of construction projects in China.

BIM and Digital Tools for State-of-the-Art Construction Cost Management

Cost overrun has remained a key risk of construction projects that can be prevented by utilizing new technologies. This paper aims to identify the gap in the literature, which can potentially be addressed by using digital tools and technologies, by reviewing the current and state of the art practices. The paper presents the results of a systematic and critical content reviews on cost overruns, to address the question of what factors are affecting the cost overrun. This paper also reviews how building information modeling (BIM) in conjunction with other tools, such as the common tools in the Asia and Asia Pacific regions, are used for cost estimation and monitoring. The paper presents the results of the content review, including their contributions and limitations, which are also used to set key directions for future investigations. A total of 176 papers was identified to develop the construction cost management (CCM) dataset. The method was a mix of systematic reviews, including co-authorship network analyses, co-occurrence analytical map development covering 5671 keywords, and content analysis including theme identification and a critical review of selected papers. The paper critically reviewed 63 selected papers from CCM, which are divided into four clusters based on their scopes: BIM adoption for cost estimation and quantity surveying; BIM implementation for a bill of quantity, risk paths, and cost overruns; cost control and management; and, finally, BIM, virtual design, and value management. A trend analysis using a set of 16 themes (e.g., 3D model, BIM, Decision, Energy, and Life Cycle) for all the papers over the past ten years was developed. The content of each cluster of papers was reviewed based on the frequency of the selected themes in each cluster. The content of each cluster of papers was also reviewed critically and gaps were identified, so a set of directions for future investigations are presented.

A Stitch in Time Saves Nine: Nexus between Critical Delay Factors, Leadership Self-Efficacy, and Transnational Mega Construction Project Success

Delay factors are frequent in the construction industry globally, resulting in significant overruns in project cost and time. In context, megaprojects can be more prone to critical delays, hence, demanding a high degree of self-confident leadership. Despite the continuous scholarly attempts to examine mega construction project success, the underlying role of critical delay factors and leadership self-efficacy has been largely overlooked. Hence, to address these rarely examined linkages, the present study empirically explored the effects of critical delay factors (CDFs) on transnational mega construction project (TMCP) success with the moderating influence of leadership self-efficacy (LSE). Based on a study sample (N = 211) extracted from the China–Pakistan Economic Corridor, the hypothesized relationships were tested through partial least squares–structural equation modeling. The study included nine critical delay factors and three subdimensions of TMCP success, derived from previous research. The findings revealed a negative relationship between CDFs and TMCP success, as a 1% increase in CDFs triggered a 28.8% negative change in TMCP success. A positive moderating effect of LSE on the relationship between CDFs and TMCP success was also empirically supported, as 1% increase in LSE resulted in 18.4% positive change in TMCP success. The present study bridges the fragmented literature on critical delay factors in the global construction industry, megaproject success, and project leadership, by providing the first empirical evidence linking these potential relationships. Moreover, the present study also provides an extension to existing studies to identify the role of CDFs and LSE in impacting multi-faceted success (i.e., management success, ownership success, and investment success) in mega construction projects.

Big Data in Construction: Current Applications and Future Opportunities

Big data have become an integral part of various research fields due to the rapid advancements in the digital technologies available for dealing with data. The construction industry is no exception and has seen a spike in the data being generated due to the introduction of various digital disruptive technologies. However, despite the availability of data and the introduction of such technologies, the construction industry is lagging in harnessing big data. This paper critically explores literature published since 2010 to identify the data trends and how the construction industry can benefit from big data. The presence of tools such as computer-aided drawing (CAD) and building information modelling (BIM) provide a great opportunity for researchers in the construction industry to further improve how infrastructure can be developed, monitored, or improved in the future. The gaps in the existing research data have been explored and a detailed analysis was carried out to identify the different ways in which big data analysis and storage work in relevance to the construction industry. Big data engineering (BDE) and statistics are among the most crucial steps for integrating big data technology in construction. The results of this study suggest that while the existing research studies have set the stage for improving big data research, the integration of the associated digital technologies into the construction industry is not very clear. Among the future opportunities, big data research into construction safety, site management, heritage conservation, and project waste minimization and quality improvements are key areas.

Project Managers’ Personality and Project Success: Moderating Role of External Environmental Factors

Successful project completion is a challenging phenomenon for project managers. Various factors play an indispensable role in the success of a project. The objective of this study is to examine the role of project managers’ personalities in project success with the moderating role of external environmental factors i.e., political, economic, social. The study includes 145 project managers from 36 large-scale construction projects, from both the public and private sectors. The big five personality model was used to evaluate the personality traits of project managers and triple constraint criteria (cost, time, and quality) was used to gauge project success. Data has been collected through a well-structured questionnaire. The analysis of data indicated that personality traits like extraversion and openness are positive predictors of project success, whereas conscientiousness, agreeableness, and neuroticism did not have any direct relationship with project success. Importantly, the findings of this study concluded that external environmental factors—like political, economic, and social—moderately influence the link of specific project managers’ personality traits to project success. The role of external environmental factors as moderators has been discussed. The findings indicate the essential personality traits, as well as the role of external factors for achieving project success. The research contributions have relevance to both theory and practice and provide a deeper insight that is useful for individuals, organizations, researchers, practitioners, and decision-makers.

Exploring the significant factors that influence delays in construction projects in Hargeisa

Over the past decades, construction delays have been perceived as a global challenge that hinders the time delivery, budget, and quality of construction projects. It is the most common, expensive, and risky problem associated with both private and public construction projects. Within this context, the aim of this study is to investigate the significant factors that influence delays in construction projects in Hargeisa, using the road and building projects as a baseline. Through a questionnaire survey with 51 critical delay factors that are categorized into seven major groups, data were collected from 51 construction stakeholders selected based on simple random sampling from the different construction companies. Feedback from the respondents was analyzed using Relative Importance Index (RII) for ranking purposes. Results showed that delay in honoring payment progressively, underestimation or overestimation of the project cost, and delay in the approval of major changes in the work scope were ranked as the three major causes of delays in construction projects in Hargeisa. The implications of these results are vital to future projects as they clearly demonstrate how less attention is given to the application of project management tools such as robust cost, scope and risk management in construction projects. As such, construction stakeholders are recommended to make efforts to use the appropriate project management practices needed to manage the 16 identified critical delay factors, when executing future construction projects. Although, the current study focused on Somaliland construction projects, it expands and improves the understanding of delay factors in the global context, and as such can be applied to other countries and future studies.

Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

An Overarching Review on Delay Analyses in Construction Projects

Numerous studies have been conducted by researchers on the delay analysis topic, which is one of the primary areas of scientific study due to the effects of delays on time and cost in construction projects. Following fruitful contributions made by the researchers, it is believed to be extremely important to summarize the existing studies in terms of being a road map for future studies and practitioners. In this context, not only does this study aim to detect the most significant authors, sources, organizations and countries contributing to the improvement of delay domain in the construction industry concerning delay analyses worldwide but also to provide the researchers with extensive insights concerning the prominent research themes, trends and gaps in the literature. Hence, 168 documents related to delay analyses from 1982 to 11 February 2021 were detected through the Scopus Database and the Web of Science Database, and scientometric analyses were conducted via VOSviewer software. By evaluating the related research, two main research areas were detected in this field, namely; improving the delay analysis methods and resolving the disputes before they occur. This study is believed to make theoretical and practical contributions in that it examines the delay analysis topic in all aspects such as prominent institutions, countries, authors and sources, synthesizes the data and highlights possible research domains, gaps and trends concerning the delay analysis topic in construction industry.

Critical Review of the Evolution of Project Delivery Methods in the Construction Industry

Selecting the appropriate project delivery method (PDM) is a very significant managerial decision that impacts the success of construction projects. This paper provides a critical review of related literature on the evolution of project delivery methods, selection methods and selection criteria over the years and their suitability in the construction industry of today’s world. The literature review analysis has concluded that project delivery methods evolve at a slower rate compared to the evolution of the construction industry. The paper also suggests features of an evolved project delivery method that is digitally integrated, people-centered, and sustainability-focused. Moreover, the paper highlights the latest selection criteria such as risk, health and wellbeing, sustainability goals and technological innovations. Furthermore, the paper concluded that advanced artificial intelligence techniques are yet to be exploited to develop a smart decision support model that will assist clients in selecting the most appropriate delivery method for successful project completion. Additionally, the paper presents a framework that illustrates the relationship between the different PDM variables needed to harmonize with the construction industry. Last, but not least, the paper fills a gap in the literature as it covers a different perspective in the field of project delivery methods. The paper also provides recommendations and future research ideas.

Lean Practices Using Building Information Modeling (BIM) and Digital Twinning for Sustainable Construction

There is a need to apply lean approaches in construction projects. Both BIM and IoT are increasingly being used in the construction industry. However, using BIM in conjunction with IoT for sustainability purposes has not received enough attention in construction. In particular, the capability created from the combination of both technologies has not been exploited. There is a growing consensus that the future of construction operation tends to be smart and intelligent, which would be possible by a combination of both information systems and sensors. This investigation aims to find out the recent efforts of utilizing BIM for lean purposes in the last decade by critically reviewing the published literature and identifying dominant clusters of research topics. More specifically, the investigation is further developed by identifying the gaps in the literature to utilize IoT in conjunction with BIM in construction projects to facilitate applying lean techniques in a more efficient way in construction projects. A systematic review method was designed to identify scholarly papers covering both concepts “lean” and “BIM” in construction and possibilities of using IoT. A total of 48 scholarly articles selected from 26 construction journals were carefully reviewed thorough perusal. The key findings were discussed with industry practitioners. The transcriptions were analyzed employing two coding and cluster analysis techniques. The results of the cluster analysis show two main directions, including the recent practice of lean and BIM interactions and issues of lean and BIM adoption. Findings revealed a large synergy between lean and BIM in control interactions and reduction in variations, and surprisingly there are many uncovered areas in this field. The results also show that the capability of IoT is also largely not considered in recent developments. The number of papers covering both lean and BIM is very limited, and there is a large clear gap in understanding synergetic interactions of lean concepts applying in BIM and IoT in specific fields of construction such as sustainable infrastructure projects.

Additive Manufacturing Applications for Industry 4.0: A Systematic Critical Review

Additive manufacturing, including 3D printing (3DP), is one of the critical pillars of Industry 4.0 and the next construction revolution. Several countries, including China, have utilized 3DP on larger scales or real projects. However, reviews of the lessons learned from previous large-sized practices of 3DP utilization are scarce. This paper presents a few practical applications of implementing 3DP over the past decade and suggests a direction for future research work. Recent publications on 3DP practices are systematically reviewed using an interpretivist philosophical lens, and more specifically, the nozzle characteristics are focused upon. The Scopus and China National Knowledge Infrastructure (CNKI) journal databases are utilized, resulting in the examination of 54 English and 62 Chinese papers. The selected practices from Mainland China, Hong Kong, Taiwan and Macao are considered for this review. A content critical review approach is adopted, and the identified papers are critically reviewed. These papers reported key challenges and advantages from their reported practices, such as limitations in aggregate sizes, nozzle sizes, standards, post-occupancy satisfaction, final product quality, productivity challenges and other associated risks. The paper reports upon prominent limitations and signposts directions for future investigations.

A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home

This article reviewed the state-of-the-art applications of the Internet of things (IoT) technology applied in homes for making them smart, automated, and digitalized in many respects. The literature presented various applications, systems, or methods and reported the results of using IoT, artificial intelligence (AI), and geographic information system (GIS) at homes. Because the technology has been advancing and users are experiencing IoT boom for smart built environment applications, especially smart homes and smart energy systems, it is necessary to identify the gaps, relation between current methods, and provide a coherent instruction of the whole process of designing smart homes. This article reviewed relevant papers within databases, such as Scopus, including journal papers published in between 2010 and 2019. These papers were then analyzed in terms of bibliography and content to identify more related systems, practices, and contributors. A designed systematic review method was used to identify and select the relevant papers, which were then reviewed for their content by means of coding. The presented systematic critical review focuses on systems developed and technologies used for smart homes. The main question is ”What has been learned from a decade trailing smart system developments in different fields?”. We found that there is a considerable gap in the integration of AI and IoT and the use of geospatial data in smart home development. It was also found that there is a large gap in the literature in terms of limited integrated systems for energy efficiency and aged care system development. This article would enable researchers and professionals to fully understand those gaps in IoT-based environments and suggest ways to fill the gaps while designing smart homes where users have a higher level of thermal comfort while saving energy and greenhouse gas emissions. This article also raised new challenging questions on how IoT and existing developed systems could be improved and be further developed to address other issues of energy saving, which can steer the research direction to full smart systems. This would significantly help to design fully automated assistive systems to improve quality of life and decrease energy consumption.

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

Integration of Cost and Work Breakdown Structures in the Management of Construction Projects

Scope management allows project managers to react when a project underperforms regarding schedule, budget, and/or quality at the execution stage. Scope management can also minimize project changes and budget omissions, as well as improve the accuracy of project cost estimates and risk responses. For scope management to be effective, though, it needs to rely on a robust work breakdown structure (WBS). A robust WBS hierarchically and faithfully reflects all project tasks and work packages so that projects are easier to manage. If done properly, the WBS also allows meeting the project objectives while delivering the project on time, on budget, and with the required quality. This paper analyzes whether the integration of a cost breakdown structure (CBS) can lead to the generation of more robust WBSs in construction projects. Over the last years, some international organizations have standardized and harmonized different cost classification systems (e.g., ISO 12006-2, ISO 81346-12, OmniClass, CoClass, UniClass). These cost databases have also been introduced into building information modeling (BIM) frameworks. We hypothesize that in BIM environments, if these CBSs are used to generate the project WBS, several advantages are gained such as sharper project definition. This enhanced project definition reduces project contradictions at both planning and execution stages, anticipates potential schedule and budget deviations, improves resource allocation, and overall it allows a better response to potential project risks. The hypothesis that the use of CBSs can generate more robust WBSs is tested by the response analysis of a questionnaire survey distributed among construction practitioners and project managers. By means of structural equation modeling (SEM), the correlation (agreement) and perception differences between two 250-respondent subsamples (technical project staff vs. project management staff) are also discussed. Results of this research support the use of CBSs by construction professionals as a basis to generate WBSs for enhanced project management (PM).