Exploring the potential of digital twin technology to improve factors affecting construction productivity during the construction phase

Abstract

In the construction industry, productivity has been a challenging and significant problem. Despite its significant economic importance, the construction sector often underperforms due to inefficiencies observed across various project stages. Low productivity is caused by a variety of problems, including insufficient communication, labor shortages, inadequate planning and a limited integration of digital technologies. These serious problems eventually decrease the general efficiency and sustainability of construction projects by causing delays, cost overruns, risk factors and resource waste, particularly during the building stage. As a result of these problems the construction sector is becoming more interested in advanced technologies in order to experience a digital transformation. Thus, the industry may achieve better site control, more effective decision making, and improved collaboration. Digital twin (DT) technology is one of these innovations that is currently gaining attention as an exciting concept that could help with major inefficiencies during the building phase. DT makes it possible for virtual and physical environments to synchronize in current time while offering insights based on data for performance enhancement. When integrated with Construction 4.0 technologies such as internet of things (IoT), artificial intelligence (AI), machine learning (ML) and others, DT systems can enable predictive maintenance, better visualization, dynamic planning and modelling construction processes. According to current literature, the DT concept may be particularly beneficial during the building stage since the construction phase is characterized by complicated resource allocation, changing scheduling and critical cost, time and safety performance goals. DT implementation during this stage might facilitate real time site management, decrease rework, enhance safety conditions and allow for a more prepared decision making process. Despite these encouraging advantages, the implementation of DT in the construction stage remains limited, largely due to high costs, technical barriers and lack of awareness. This research investigates the potential of DT technology to improve productivity in the construction phase by examining how its capabilities align with the factors negatively affecting project performance. To achieve this goal, the objectives of the study are (1) to explore the role of DT throughout the building life cycle in the construction sector (2) to identify its benefits, challenges, and key application areas in the construction stage (3) to determine the factors affecting productivity during the construction phase (4) to compare these factors with the DT system's identified capabilities to assess its potential in overcoming productivity challenges. To accomplish these goals, the research adopts a two step method. First, a comprehensive literature review was conducted to identify the most critical productivity related factors and discover the present knowledge of DT system's role in construction such as its opportunities, obstacles and major application areas. As a second step, the questionnaire was designed based on the literature findings to understand the perception of sector experts and distributed to professionals in the construction industry. This resulted in 76 valid responses. The data were analyzed using SPSS v.29. The collected data were analyzed using several statistical methods including descriptive statistics, Cronbach's alpha reliability analysis, normality tests, correlation analysis, independent samples t-tests and one-way ANOVA. These methods enabled a detailed evaluation of relationships between variables and the identification of notable variations in perceptions across participant groups. Findings from the survey suggest that industry professionals recognize the high potential of DT technology to address key productivity challenges in the construction phase. According to the survey results, professionals identified labor, management systems and design related issues as the most influential and frequently occurring productivity factors. However, when assessing DT system's potential impact, participants believed the greatest improvements would occur in design related issues and management systems. These areas where DT capabilities such as integration with emerging technologies and time, cost optimizations are particularly effective. Despite labor being the most important productivity factor, the associated DT application area workforce monitoring was ranked lowest. This may indicate that DT was not believed to have much of an impact on labor concerns. In the statistical analyses, strong correlations for the benefits of DT concept were found between risk management, real time digital representation and resource management, as well as between time and cost management and resource management. In the challenges of the DT category, the highest correlations appeared among data integration, data management and high-fidelity modeling. For key application areas of DT, strong links were observed between material and equipment management, site monitoring and time and cost optimization. In addition to descriptive and correlation analyses, the study conducted independent samples t-tests and one-way ANOVA to explore differences in perceptions among participant groups. According to t-tests and ANOVA, there were minor variations between the participant groups. For the t-test, participants were divided into two groups based on their prior knowledge of DT technology (DT-informed and uninformed). Although no significant differences were observed in perceptions of respondents on the productivity factors and the opportunities of the DT system, significant differences emerged in specific areas. Regarding the potential impact of DT on productivity related factors, a statistically significant difference was identified for the communication factor, suggesting that DT-informed participants believed DT implementation could more strongly improve communication during the construction phase. In the challenges, DT-informed participants rated obstacles such as data integration, high-fidelity modeling and the need for skill and training more critically than non-informed participants. Similarly in the key application areas of DT in the construction phase, a significant difference was observed for the enhanced decision making processes variable, where DT-informed participants rated this application area more highly than non-informed participants. For the one-way ANOVA, participants were categorized according to their professional experience: less than 2 years, 2–5 years and more than 5 years. Even though opinions were generally the same among the groups, two notable distinctions were found. First, participants with more than 5 years of experience reported a higher mean score for DT's adaptability performance compared to those with 2–5 years of experience. Second, in the integration with the emerging technologies application area, those with more than 5 years of experience again rated the application area significantly higher compared to participants with 2–5 years of experience. These findings may imply that more experienced professionals tend to perceive a more positive view of DT as more adaptable and better suited for integration with advanced technologies in construction processes. According to the results obtained from the study, industry experts are optimistic about the DT technology and believe that it can significantly influence the factors determining efficiency and positively affect the productivity in the construction phase.