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Purpose>The purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).Design/methodology/approach>IDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.Findings>The research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.Practical implications>DRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.Originality/value>The research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.

The importance of innovation has always been important for the economic growth and the quality of life of the humankind. But in the latest years the awareness of its importance is growing and has led to systematic study of the process and the characteristics of innovation. The paper is first giving an overview of the science of innovation, partly with the help of an innovation, the Artificial Intelligence. Then the paper focuses on differences in the level of innovation and tries to explain them by comparing innovative solutions to a well-known problem from the field of construction.

The progress monitoring (PM) of construction projects is an essential aspect of project control that enables the stakeholders to make timely decisions to ensure successful project delivery, but ongoing practices are largely manual and document-centric. However, the integration of technologically advanced tools into construction practices has shown the potential to automate construction PM (CPM) using real-time data collection, analysis, and visualization for effective and timely decision making. In this study, we assess the level of automation achieved through various methods that enable automated computer vision (CV)-based CPM. A detailed literature review is presented, discussing the complete process of CV-based CPM based on the research conducted between 2011 and 2021. The CV-based CPM process comprises four sub-processes: data acquisition, information retrieval, progress estimation, and output visualization. Most techniques encompassing these sub-processes require human intervention to perform the desired tasks, and the inter-connectivity among them is absent. We conclude that CV-based CPM research is centric on resolving technical feasibility studies using image-based processing of site data, which are still experimental and lack connectivity to its applications for construction management. This review highlighted the most efficient techniques involved in the CV-based CPM and accentuated the need for the inter-connectivity between sub-processes for an effective alternative to traditional practices.

Purpose The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps. Design/methodology/approach The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application. Findings The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks. Practical implications This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world. Originality/value To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

Despite the subjective and error-prone nature of manual visual inspection procedures, this type of inspection is still a common process in most construction projects. However, Automated Construction Inspection and Progress Monitoring (ACIPM) has the potential to improve inspection processes. The objective of this paper is to examine the applications, challenges, and future directions of ACIPM in a systematic review. It explores various application areas of ACIPM in two domains of (a) transportation construction inspection, and (b) building construction inspection. The review identifies key ACIPM tools and techniques including Laser Scanning (LS), Uncrewed Aerial Systems (UAS), Robots, Radio Frequency Identification (RFID), Augmented Reality (AR), Virtual Reality (VR), Computer Vision (CV), Deep Learning, and Building Information Modeling (BIM). It also explores the challenges in implementing ACIPM, including limited generalization, data quality and validity, data integration, and real-time considerations. Studying legal implications and ethical and social impacts are among the future directions in ACIPM that are pinpointed in this paper. As the main contribution, this paper provides a comprehensive understanding of ACIPM for academic researchers and industry professionals.

The necessity for automatic monitoring tools led to using 3D sensing technologies to collect accurate and precise data onsite to create an as-built model. This as-built model can be integrated with a BIM-based planned model to check the project’s status based on algorithms. This article investigates the construction progress monitoring (CPM) domain, including knowledge gaps and future research direction. Synthesis literature was conducted on 3D sensing technologies in CPM depending on crucial factors, including the scanning environment, assessment level, and object recognition indicators’ performance. The scanning environment is important to determine the volume of data acquired and the applications conducted in the environment. The level of assessment between as-planned and as-built models is another crucial factor that could precisely help define the knowledge gaps in this domain. The performance of object recognition indicators is an essential factor in determining the quality of studies. Qualitative and statistical analyses for the latest studies are then conducted. The qualitative analysis showed a shortage of articles performed on 5D assessment. Then, statistical analysis is conducted using a meta-analytic regression model to determine the development of the performance of object recognition indicators. The meta-analytic model presented a good sign that the performance of those indicators is effective where [p-value is = 0.0003 < 0.05]. The study is also envisaged to evaluate the collected studies in prioritizing future works from the limitations within these studies. Finally, this is the first study to address ranking studies of 3D sensing technologies in the CPM domain integrated with BIM.

Monitoring the progress on a construction site during the construction phase is crucial. An inadequate understanding of the project status can lead to mistakes and inappropriate actions, causing delays and increased costs. Monitoring and controlling projects via digital tools would reduce the risk of error and enable timely corrective actions. Although there is currently a wide range of technologies for these purposes, these technologies and interoperability between them are still limited. Because of this, it is important to know the possibilities of integration and interoperability regarding their implementation. This article presents a bibliographic synthesis and interpretation of 30 nonconventional digital tools for monitoring progress in terms of field data capture technologies (FDCT) and communication and collaborative technologies (CT) that are responsible for information processing and management. This research aims to perform an integration and interoperability analysis of technologies to demonstrate their potential for monitoring and controlling construction projects during the execution phase. A network analysis was conducted, and the results suggest that the triad formed by building information modeling (BIM), unmanned aerial vehicles (UAVs) and photogrammetry is an effective tool; the use of this set extends not only to monitoring and control, but also to all phases of a project.

This paper represents initial research with the aim to establishes a baseline for subsequent research into AI-based construction monitoring, building upon the NORMENG project in Croatia, which previously integrated photogrammetry, laser scanning, and BIM-based methods. The study tests general purpose AI’s ability to detect materials and estimate quantities, aiming to assess whether a broad, context-aware AI system can match the precision of specialized, domain-specific tools or even human work needed for productivity estimations. While the AI demonstrated potential for basic entity detection and preliminary quantity estimations, it showed significant limitations in delivering fine-grained, temporally accurate breakdowns without targeted adaptation. The findings underscore the need for domain-specific fine-tuning and human-in-the-loop validation to transform AI into a reliable tool for construction management. This initial contribution provides empirical insights and actionable recommendations for advancing automated progress monitoring in the construction sector.

Purpose This paper aims to propose an automatic imaging network design to improve the efficiency and accuracy of automated construction progress monitoring. The proposed method will address two shortcomings of the previous studies, including the large number of captured images required and the incompleteness and inaccuracy of generated as-built models. Design/methodology/approach Using the proposed method, the number of required images is minimized in two stages. In the first stage, the manual photogrammetric network design is used to decrease the number of camera stations considering proper constraints. Then the image acquisition is done and the captured images are used to generate 3D points cloud model. In the second stage, a new software for automatic imaging network design is developed and used to cluster and select the optimal images automatically, using the existing dense points cloud model generated before, and the final optimum camera stations are determined. Therefore, the automated progress monitoring can be done by imaging at the selected camera stations to produce periodic progress reports. Findings The achieved results show that using the proposed manual and automatic imaging network design methods, the number of required images is decreased by 65 and 75 per cent, respectively. Moreover, the accuracy and completeness of points cloud reconstruction is improved and the quantity of performed work is determined with the accuracy, which is close to 100 per cent. Practical implications It is believed that the proposed method may present a novel and robust tool for automated progress monitoring using unmanned aerial vehicles and based on photogrammetry and computer vision techniques. Using the proposed method, the number of required images is minimized, and the accuracy and completeness of points cloud reconstruction is improved. Originality/value To generate the points cloud reconstruction based on close-range photogrammetry principles, more than hundreds of images must be captured and processed, which is time-consuming and labor-intensive. There has been no previous study to reduce the large number of required captured images. Moreover, lack of images in some areas leads to an incomplete or inaccurate model. This research resolves the mentioned shortcomings.