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Digital data acquisition is crucial for operations in the digital transformation era. Reality capture (RC) has made an immeasurable contribution to various fields, especially in the built environment. This paper aims to review RC applications, potentials, limitations and the extent to which RC can be adopted for cost monitoring of construction projects.

A mixed-method approach, using Bibliometric analysis and the PRISMA framework, was used to review and analyse 112 peer-reviewed journal articles from the Scopus and Web of Science databases.

The study reveals RC has been applied in various areas in the built environment, but health and safety, cost and labour productivity monitoring have received little or no attention. It is proposed that RC can significantly support cost monitoring owing to its ability to acquire accurate and quick digital as-built 3D point cloud data, which contains rich measurement points for the valuation of work done.

The study’s conclusions are based only on the Scopus and Web of Science data sets. Only English language documents were approved, whereas others may be in other languages. The research is a non-validation of findings using empirical data to confirm the data obtained from RC literature.

This paper highlights the importance of RC for cost monitoring in construction projects, filling knowledge gaps and enhancing project outcomes.

The implementation of RC in the era of the digital revolution has the potential to improve project delivery around the world today. Every project’s success is largely determined by the availability of precise and detailed digital data. RC applications have pushed for more sustainable design, construction and operations in the built environment.

The study has given research trends on the extent of RC applications, potentials, limitations and future directions.

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).

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.

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.

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.

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 purpose of this study is to investigate the current construction progress monitoring (CPM) process in relation to the contractual obligations, how project management teams carry out this activity in the field and why teams continue to adopt the current method. The study aims to provide a comprehensive understanding of the current monitoring process and its effectiveness, identify any shortcomings and propose recommendations for improvements that can lead to better project outcomes.

The study conducted semi-structured interviews with 28 construction management practitioners to explore their views on contractual requirements, traditional progress monitoring practices and advanced monitoring methods. Thematic analysis was used to identify existing processes, practices and incentives for advanced monitoring.

Standard construction contracts mandate current progress monitoring practices, which often rely on manual, document-centric and labor-intensive methods, leading to slow and erroneous progress reporting and project delays. Key barriers to adopting advanced tools include rigid contractual clauses, lack of incentives and the absence of reliable automated tools. A holistic automated approach that covers the entire CPM process, from planning to claim management, is needed as a viable alternative to traditional practices.

The study's findings can inform researchers, stakeholders and decision-makers about the existing monitoring practices and contribute to enhancing project management practices.

The study identified contractually mandated progress monitoring processes, traditional methods of collecting, transferring, analyzing and dispensing progress-related information and potential incentives and points of departure towards technologically advanced methods.

Project management generally covers many important areas such as cost, quality and time in different industrial settings, but it is deficient in relation to integration of health, safety and environmental risks. Poor knowledge of project managers about HSE management necessitates the studying on the mutual effects of HSE and project management. Hence, investigating the impact of project management on health monitoring programs, safety prevention monitoring, environmental monitoring plans and finally the effectiveness of professional health monitoring programs and determining their importance are main objectives of this research. The paper aims to discuss these issues.

A model based on structural equations was designed and developed. The constructs of this model are project management, health monitoring and safety prevention monitoring program. Based on the conceptual model, some questionnaires were prepared and distributed among the experts of strategic project management.

The results of applied structural modeling suggest that project management focuses on each aspect of HSE management, including health monitoring programs, safety prevention monitoring programs, environmental monitoring plans and effectiveness of professional health monitoring programs. HSE management can also be strengthened by empowering project management. Checking fire protection systems, using appropriate techniques to identify contamination and disposal of waste and incorporating techniques for brainstorming or other ideas creation in the group are the most important tasks in HSE-enabled project management frameworks.

Since there is still no strategic alignment model that includes components of project management and HSE management, a model for achieving this goal is vital. This paper elaborates this alignment based on literature and using a field study.

The activities using drum resources restrict the operation of multi-project systems. However, existing monitoring methods are not suitable for the characteristics of drum activities in the multi-project system. The authors therefore propose an adaptive capacity constraint buffer monitoring model based on the attributes of drum activities, aiming to build a high-efficiency progress control framework for multiple projects.

Considering the attributes and the interrelationship of drum activities, the monitoring reference points are determined on the basis of decentralized buffers. The authors next set action thresholds according to the relationship between the drum activities' interval margin and buffer consumption, and then the corresponding monitoring measures are taken.

The empirical results show that, compared to the classic methods, the proposed approach can effectively monitor the progress of the drum plan and realize the dual optimization of multi-project duration and cost.

The buffer consumption at the follow-up monitoring time point is neglected when determining the action thresholds. Prediction methods can be introduced to present more all-sided monitoring.

This paper fulfils the dual optimization of multi-project duration and cost. It provides a reference guide for project managers.

A capacity constraint buffer monitoring method suitable for a multi-project environment is produced.

Proposes a reputation‐based model to examine the managerial investment and liquidation decisions regarding real estate projects in developing countries. Unlike investments in domestic projects, foreign investments are subject to noisy monitoring, resulting in a liquidation inefficiency where managers preserve their reputational capital by not liquidating projects likely to fail as long as negative signals are not revealed to the public. The manager’s decision to invest in foreign countries is influenced not only by the difference between foreign and domestic project returns, but also by the change in reputational capital. Shows that reputation‐based utility maximization can lead to an under‐investment in profitable foreign projects. Government support to invest in foreign countries can reduce the under‐investment problem, but it can also increase the liquidation inefficiency.

This paper responds to recent calls for deeper scrutiny of the institutional contexts of citizen science. In the last few years, at least two dozen civil society organizations in New York and Pennsylvania have begun monitoring the watershed impacts of unconventional natural gas drilling, also known as “fracking.” This study examines the institutional logics that inform these citizen monitoring efforts and probes how relationships with academic science and the regulatory state affect the practices of citizen scientists. We find that the diverse practices of the organizations in the participatory water monitoring field are guided by logics of consciousness-raising, environmental policing, and science. Organizations that initiate monitoring projects typically attempt to combine two or more of these logics as they develop new practices in response to macro-level social and environmental changes. The dominant logic of the field remains unsettled, and many groups appear uncertain about whether and how their practices might have an influence. We conclude that the impacts of macro-level changes, such as the scientization of politics, the rise of neoliberal policy ideas, or even large-scale industrial transformations, are likely to be experienced in field-specific ways.

This study aims to analyze the outcome features of effective monitoring and evaluation in construction projects delivery.

The study adopted a quantitative research approach. Questionnaire survey was administered to 230 participants who were drawn from metropolitan, municipal, district assemblies and regional coordinating councils in Ghana. Data collected were analyzed to determine the key and underlying monitoring and evaluation outcome features in project delivery. A Cronbach’s α value of 0.953 was achieved based on standardized items, while the Kaiser–Meyer–Olkin measure of sampling adequacy recorded was 0.876. The result of Bartlett’s test of sphericity also revealed a significance level of 0.000 (p < 0.05).

The study discovered that value for money, successful project closure, end-user satisfaction, timely completion of projects and fitness for purpose were the top five monitoring and evaluation outcome features. Similarly, three principal monitoring and evaluation outcome features were identified, namely, performance, satisfaction and value outcome.

The study, thus, seeks to guide project planning and implementation of effective construction project M&E.

The study contributes to the body of knowledge by establishing top and key success outcomes (KSO) in the implementation of monitoring and evaluation.

This paper aims to examine the role of project monitoring and evaluation (M&E) in international development (ID) project stakeholders' relationships. This study draws on agency theory to examine the specific role M&E plays in improving ID project impact.

Qualitative data comprising of in-depth interviews were collected from ID project stakeholders such as project donors, implementing partners and steering committee members.

Results of the study show that project M&E activities can serve multiple purposes including the collection of data for the assessment of inputs, outputs, outcomes and impact. This information is shared with stakeholders to assist in evidence-based decision-making to improve project impact on community. This study shows that M&E activities strengthen the relationship between stakeholders by involving multiple stakeholders at different stages of ID projects to identify community needs and to demonstrate the positive community impact. Agency issues such as goal incongruence, information asymmetry and risk-sharing affect the relationship between the stakeholders. Investing in different M&E activities can reduce these issues, ultimately leading to a positive impact at the community level.

There has been limited research that explores the principal-agent relationship between project stakeholders of ID projects through the lens of agency theory. The role of M&E to collect project data and address agency issues between project stakeholders to improve project impact is the novel contribution of this paper.

The unique nature of the construction sector makes it fall behind other sectors in terms of productivity. Monitoring construction productivity is crucial for the construction project's success. Current practices for construction productivity monitoring are time-consuming, manned and error prone. Although previous studies have been implemented toward reducing these limitations, a gap still exists in the automated monitoring of construction productivity.

This study aims to investigate and assess the different techniques used for monitoring productivity in building construction projects. Therefore, a mixed review methodology (bibliometric analysis and systematic review) was adopted. All the related publications were collected from different databases, which were further screened to get the most relevant based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria.

A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. Also, it was observed that current studies did not cover all the complex construction job sites and they were applied based on a small sample of construction workers and machines separately.

This review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques.