From BIM to UAVs: A Systematic Review of Digital Solutions for Productivity Challenges in Construction

Abstract

The construction industry faces persistent productivity challenges despite the widespread adoption of advanced digital technologies. This systematic review examines how digital technologies contribute to improving on-site labor productivity within the Architecture, Engineering, Construction, and Operations (AECOs) sector. Following the PRISMA methodology, 431 records were initially identified, with 28 high-quality articles ultimately selected for analysis through rigorous screening and snowballing techniques. The reviewed technologies include Building Information Modeling (BIM), photogrammetry, LiDAR, augmented reality (AR), global navigation satellite systems (GNSSs), radio frequency identification (RFID), and unmanned aerial vehicles (UAVs), which were categorized into three key areas: factors affecting productivity, modeling and evaluation, and productivity improvement methods. Findings highlight that these technologies collectively enhance resource allocation, reduce labor costs, and improve project scheduling through better coordination. Whilst digital technologies demonstrate substantial impact on construction productivity, further research is needed to quantify long-term benefits and address scalability challenges across different project contexts and organizational structures. Ultimately, the review concludes that digital technologies play a crucial role in enhancing construction productivity, highlighting the need for further research to assess long-term advantages and scalability across diverse construction environments. These technological advancements are essential for modernizing the industry and supporting sustainable growth in the digital transition era.

1. Introduction

The construction industry (CI) is currently facing significant productivity challenges. Inefficient management of engineering resources can directly impact overall productivity. Issues such as schedule and cost overruns, rework, and the effective digital management of constructed sites are prevalent in the global construction sector [1]. In recent years, digitization has provided the Architecture, Engineering, Construction, and Operations (AECOs) industry with the opportunity to enhance performance and accuracy [2]. This shift has the potential to reduce costs, modernize the industry, and ultimately boost productivity. Due to that, this work systematically aims to review digital solutions for enhanced productivity in the CI, aiming to consolidate the knowledge.

Construction productivity has shown a gradual increase in recent years [3]. In contrast, the productivity of other sectors, such as services and manufacturing, has increased at a faster and more significant rate [3]. Productivity measurement should not be regarded as a one-time activity solely for reporting purposes. It is a continuous process essential for obtaining representative indicators that enable more accurate project monitoring [1,2]. On-site project monitoring typically involves gathering data at the project level through direct human observations, surveys, and interviews to evaluate progress at the execution stage [4]. Additionally, construction companies’ approaches to implementing these processes can vary significantly [4].

On-site task-related observations and methods based on surveys or interviews are employed to assess construction production at the functional level [5,6]. These approaches indicate how effectively workers, products, and machines are utilized. By analyzing the tasks performed by craft workers and linking them to productivity performance metrics, monitoring at the operational level identifies critical factors contributing to performance variations that arise during operations [1].

In recent years, automation and digitalization have provided this sector with tools to achieve greater efficiency and precision, lowering costs and advancing production processes [1,2]. Recently, A significant area of research in the CI has concentrated on developing sensing technologies for automated data collection on-site to evaluate productivity [1]. This research can be categorized into two main domains: (1) automated tracking of project progress to evaluate construction production [7,8] and (2) automated tracking of resource utilization to assess construction production [9,10].

Recognizing the increasing interest and investment in digital technologies for productivity assessment, this research provides a systematic review concerning the use and application of digital tools in the AECOs sector. It explores their connection to productivity, highlighting associated benefits, significance, and tangible outcomes. This review is structured to address eight key objectives. Specifically, it poses eight critical questions to identify available and commonly used tools, evaluate the methods employed, and examine their benefits and significance in improving productivity. The following questions will be addressed in four dimensions:

• Mapping the technologies in use:

  ○

  Q1. What are the main technologies used to measure/manage construction productivity on-site?

  ○

  Q2. How can these technologies increase the productivity of direct labor?

• Analysis of the impact of specific technologies:

  ○

  Q3. What is the relative importance of point cloud technologies, and how has this importance varied over time?

  ○

  Q5. What is the importance, and how do video and images enhance labor productivity?

  ○

  Q7. What is the importance, and how do sensors enhance labor productivity?

• Supporting and integrative technologies:

  ○

  Q4. What digital tools help in training for on-site activities?

  ○

  Q6. Is BIM necessary to support an automatic productivity control solution on-site? And what are the benefits?

• Empirical grounding:

  ○

  Q8. Do the articles present empirical results from real-world case studies?

2. Materials and Methods

2.1. Study Type and Justification

The research used systematic review methods to identify and assess digital construction technologies which enhance productivity worldwide. The systematic review method replaced traditional literature reviews because it provides a structured approach which ensures transparency and reproducibility. The established procedures of this method enable researchers to find and evaluate and combine the most important research results from studies that match the review topic [11]. The research problem requires this methodology because it allows researchers to find all digital technologies used in construction projects and evaluate their productivity effects using standardized criteria and identify effective implementation methods for different organizational and project environments.

The systematic review design fulfills research objectives through its structured method to study how digital technology implementation affects construction productivity results. The method provides complete evidence assessment through transparent procedures that support construction management evidence-based practice.

2.2. Methodological Framework

The study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement [12] guidelines, following established systematic review methodology [13]. The PRISMA-P process [14] was conducted and resulted in a final selection of 28 documents, as shown in Figure 1.

The research protocol was prospectively developed and registered on the Open Science Framework (OSF) to ensure methodological transparency and reduce reporting bias (https://doi.org/10.17605/OSF.IO/6FGH3, accessed on 30 April 2025).

2.3. Main Process Stages

The systematic review process comprised five sequential stages: (1) protocol development and registration, (2) comprehensive database searching and study identification, (3) systematic screening and selection based on predetermined criteria, (4) data extraction and quality assessment, and (5) evidence synthesis and analysis. Each stage incorporated specific quality control measures to ensure methodological rigor and minimize potential biases.

2.4. Search Strategy and Information Sources

Comprehensive searches were conducted in two major multidisciplinary scientific databases: Scopus and Web of Science (WOS) during the first week of October 2024. These databases were selected based on their extensive coverage of construction management, engineering, and technology literature, ensuring comprehensive identification of relevant studies across multiple disciplines and publication venues.

The search strategy employed a systematic combination of key terms developed through preliminary scoping and expert consultation. The final search query incorporated Boolean operators and controlled vocabulary: ((Surveying OR “Point Cloud” OR “Computer vision” OR “Laser Scan*” OR photogrammetry OR liDAR OR UAV) AND (“Labor productivity” OR “Progress Monitoring” OR quantity OR “Construction automation” OR “Productivity Monitoring”) AND (construction OR building) AND (BIM OR “Building Information Model*”)). This comprehensive search strategy was designed to capture studies examining digital technologies’ impact on construction productivity while maintaining specificity to avoid irrelevant results.

2.5. Selection Criteria and Screening Process

Inclusion Criteria:

• Peer-reviewed articles examining digital technologies in construction contexts;
• Studies reporting productivity outcomes, progress monitoring, or construction automation applications;
• Research involving Building Information Modeling (BIM) integration with digital technologies;
• Publications in the English language;
• No chronological restrictions to capture both foundational and contemporary research.

Exclusion Criteria:

• Non-peer-reviewed publications (conference abstracts, editorials, opinion pieces);
• Studies not specifically addressing construction productivity or progress monitoring;
• Research focusing solely on design applications without construction implementation;
• Publications in languages other than English due to resource constraints
• Duplicate publications across databases.

2.6. Quality Assurance and Validation Strategies

To enhance methodological rigor and ensure reliability, several quality assurance measures were implemented:

• Snowballing Technique: Reference lists of initially retrieved papers were systematically reviewed to identify additional relevant studies not captured in the primary database searches, continuing until no new relevant materials were identified;
• PRISMA Compliance: The complete review followed PRISMA-P guidelines [14], with systematic documentation of all screening decisions and exclusions to ensure transparency and reproducibility.

2.7. Identified Potential Biases

• Publication Bias: The review may be subject to publication bias, as studies reporting positive outcomes of digital technology implementation are more likely to be published than those reporting negative or null results. This bias could lead to overestimating technology effectiveness and underrepresenting implementation challenges.
• Language Bias: The restriction to English-language publications may have excluded relevant research published in other languages, particularly studies from non-English speaking countries with significant construction technology development, potentially limiting the global representativeness of findings.
• Database Coverage Bias: While Scopus and Web of Science provide extensive coverage, the exclusive reliance on these databases may have missed relevant studies indexed in specialized construction or regional databases, potentially affecting the comprehensiveness of the evidence base.

3. Results

3.1. Selected Articles

The study selection process is illustrated in Figure 1’s PRISMA flow diagram, where the initial search phase, 431 records were identified across multiple databases. After removing 146 duplicates, 285 articles remained for screening. Applying predefined exclusion criteria, 247 articles were excluded, 202 due to document type (e.g., book chapters, editorials, conference proceedings, and surveys), five due to language (only English-language publications were considered), and 40 for lack of relevance to the research topic.

This screening resulted in 38 full-text articles assessed for eligibility. Of these, 33 were excluded for not meeting the core inclusion criteria, particularly regarding direct focus on on-site productivity and the application of digital technologies. Only 5 articles were retained at this stage. Although this number may seem limited, it reflects the strict methodological rigor adopted in this review to ensure relevance and quality.

A snowballing strategy was employed to strengthen the dataset, reviewing the reference lists of the initially included studies. This process led to identifying 23 relevant articles and expanded the final sample to 28 high-quality studies. The integration of snowballing is a recognized and effective method in systematic reviews, especially when addressing emerging or multidisciplinary topics with sparse direct literature.

3.2. Characteristics of the Included Studies

This section summarizes the 28 articles, aiming to identify and highlight the most frequently featured journals. Additionally, the articles are categorized by their geographical distribution and publication timeline.

The publication period of the articles is identified between 1986 and 2024, highlighting the years 2015 and 2020, with higher publications. It can be observed that since 1986, the subject in question has been addressed, as shown in Figure 2.

Table 1. Publication distribution related to the source of publishing.

Source (Name of Peer-Reviewed Journal)	References	Quantity (n)	Percentage (%)
Automation in Construction	[15,16,17,18,19,20,21,22]	8	29
Journal of Construction Engineering and Management	[23,24,25,26]	4	14
International Journal of Construction Management	[27,28]	2	7
MDPI—Buildings	[29,30]	2	7
Other Journals	[31,32,33,34,35,36,37,38,39,40,41,42]	12	43
Total		28	100

summarizes the selected papers, with the majority published in Automation in Construction (8 out of 28, 29%) and the Journal of Construction Engineering and Management (4 out of 28, 14%). The International Journal of Construction Management and MDPI—Buildings each accounted for two articles (7% each), while the remaining 12 articles (43%) were distributed across 12 different journals, grouped under “Other journals”.

Figure 3 illustrates the distribution of publications across countries between 1986 and 2024, highlighting global interest in digital technologies for enhancing construction productivity. The United States leads this field with five articles (18% of all publications), and South Korea leads with four articles (14% of all publications). Canada and China share third place, each contributing three articles (11% of all publications).

3.3. Technologies Used to Measure the Construction Productivity: On-Site and Field Analysis

One of the goals of the article analysis is to identify the technologies utilized and their applications in assisting owners, facility managers, and engineers. These technologies are used to evaluate and plan the productivity of workers and equipment on-site, enabling productivity improvements across various work fronts. This identification of the technologies used, and fields is summarized in

Table 2. Study Categories and Applied Technologies.

		References
Field	Factors Affecting Productivity	[23,24,27,28,40,41]
	Method and technology for productivity improvement	[18,21,29,30,32,34,37,38,42]
	Productivity modeling and evaluation	[15,16,17,19,20,22,25,26,31,33,35,36,39]
Digital Technologies	BIM	[15,18,21,22,29,30,31,33]
	LiDAR/ Total Station	[15,31,32,33]
	Video and Image	[16,17,20,31,34,36]
	RFID	[34]
	GNSS	[31,32]
	UAV	[32]
	AR	[30,32]

.

Since 2010, the evolutionary shift has been called Industry 4.0, a term commonly used to signify the fourth industrial revolution [43]. Industry 4.0 is characterized, among other aspects, by digitalization, which involves leveraging digital tools to transform business models and create new opportunities for value generation and revenue growth [44]. Brandon and Lu [45] indicated that the global CI is transitioning towards a machine-driven sector. Also, Froese [46] outlined a comprehensive range of digital innovations that could enhance the operational capabilities of construction companies and positively influence productivity. These innovations encompass the use of digital technologies in construction and design to enhance visualization [47], access to information both on-site and off-site [48], and progress tracking [49].

Regarding the technologies used, seven technologies were identified. These were:

• Building Information Modeling (BIM)—Facilitates the object-oriented physical representation of buildings, supports the visualization of real-world objects, improves coordination among project stakeholders, and aids in creating construction documents for project delivery. BIM-enabled collaboration reduces rework, costs, and errors throughout the building’s entire lifecycle, with significant benefits during the pre-planning phase. Implementation: High-performance workstations (16 GB RAM), cloud platforms, standardized software, and BIM Execution Plans [50].
  Costs: $50,000–150,000 first year; software $2825–4180/year; consultants $5000–50,000 (BIM.com.sg, 2024). ROI ranges −83.3% to 39,900% [51].
  Learning Curve: 6–12 months; 40–60 h basic training; 20–30% productivity decrease initially [52];
• Point Clouds Technologies—digital methods used to capture, represent, and process spatial data in the form of dense sets of 3D points. Implementation: Digital cameras (20 MP+), UAV platforms, LiDAR scanners, processing software (Pix4D, Agisoft).
  Costs: Photogrammetry $5000–15,000; LiDAR $80,000–200,000; Total stations $25,000–60,000 [53].
  Learning Curve: Photogrammetry 2–4 weeks; LiDAR 4–8 weeks; advanced processing 3–6 months [54];

  ○

  Photogrammetry (digital cameras)—Captures real-time images on-site to identify deviations in construction, monitor activities, and assess productivity and activity progress;

  ○

  LiDAR and Total Station—Enable data collection and monitor potential deviations from the constructed work while calculating real-time productivity and comparing it with the calibrated productivity benchmarks;

• Augmented Reality (AR)—The combination of real and virtual images provides a real-time experience for the user and assists in inspection and error identification, improving productivity and safety on-site. Implementation: Tablets/smartphones, head-mounted displays, Unity3D/Vuforia platforms, 3D model preparation.
  Costs: Tablets $500–1500; HoloLens $3500–5000; development $50,000–150,000 [55].
  Learning Curve: Basic operation 1–2 weeks; development 3–6 months; advanced implementation 6–12 months [56];
• Global Navigation Satellite System (GNSS)—Offers spatial and location-based data that can significantly reduce labor-intensive data collection and lower associated costs. Implementation: RTK base stations, rover units, machine control integration, communication networks [57]. Costs: RTK systems $25,000–50,000/machine; receivers $8000–15,000; base stations $15,000–30,000; Basic RTK setup from $300 [29].
  Learning Curve: Basic operation 1–2 weeks; RTK configuration 4–6 weeks; advanced applications 2–4 months [58].
• Radio Frequency Identification (RFID)—Improves the ability to obtain data, identify, and track equipment. Implementation: Tag selection (passive/active), reader infrastructure, and inventory management integration [59]. Costs: Passive tags $0.10–0.50; active tags $5–15; readers $500–3500; complete system $25,000–75,000 [60];
  Learning Curve: Basic operation 1week; configuration 2–3 weeks; advanced analytics 2–4 months [61];
• Unmanned Aerial Vehicle (UAV)—Increases efficiency and safety, reduces labor costs, and collects data and photographs. Implementation: Pilot certification, flight planning software, data processing capabilities, and regulatory compliance. Costs: Consumer drones $1000–5000; professional systems $10,000–50,000; DJI Matrice RTK $15,370; Certification $3000–8000 [62].
  Learning Curve: Basic operation 2–4 weeks; professional surveying 6–8 weeks; advanced applications 3–6 months [63];
• Machine Learning (ML)—enhances accuracy by processing large datasets, predicts delays through historical analysis, and automates monitoring tasks using tools like computer vision. It identifies key productivity drivers, provides real-time decision support, and optimizes resource allocation. Implementation: Data collection infrastructure, cloud computing, model development platforms, specialized personnel [64]. Costs: Infrastructure $50,000–200,000; cloud computing $500–5000/month; specialists $80,000–150,000/year [65].
  Learning Curve: Basic implementation 3–6 months; custom development 6–12 months; advanced applications 12–24 months [64,65];

The fields were classified into three categories:

• Factors affecting productivity;
• Productivity modeling and evaluation.
• Method and technology for productivity improvement

Understanding the factors affecting productivity in the CI can help designers create more efficient structural designs and enable builders to plan better, schedule, and manage activities. Several studies have identified and quantified the factors affecting productivity in various construction activities, such as masonry, pipe installation, formwork, steel reinforcement, and concrete pouring [66]. The impact of factors influencing construction productivity can vary depending on the specific activity. While some factors may exhibit similar characteristics across various activities, their effect on the productivity rate may differ in magnitude. A total of six occurrences are identified in articles focusing on “factors affecting productivity” [23,24,27,28,40,41].

Regarding productivity modeling and evaluation, productivity models can measure various factors’ influence on construction labor productivity. These models are crucial for estimating, scheduling, and planning decisions in construction projects. Several attempts were made to evaluate the effects of these factors on construction using various methodologies [67]. Multiple modeling techniques have been developed to study the factors affecting labor productivity, with the goal of aiding estimations. These methods include regression analysis, statistical models, expert systems, and artificial intelligence. From the perspective of construction companies, the most significant factors impacting labor productivity were identified [68]. A total of thirteen occurrences are identified in articles addressing “productivity modeling and evaluation” [15,16,17,19,20,22,25,26,31,33,35,36,39].

Methods and technology for productivity improvement appear last. Advancements in processes and technologies have significantly enhanced productivity in the construction industry. Techniques such as Building Information Modeling (BIM) streamline project design, coordination, and execution, reducing errors and rework while improving efficiency across project phases [69]. Additionally, automation technologies such as robotic bricklaying and 3D printing have shown promise in accelerating construction timelines and reducing labor costs [69] RFID and IoT-enabled sensors provide real-time tracking of materials and equipment, improving resource allocation and minimizing delays [70].

Furthermore, integrating UAVs (Unmanned Aerial Vehicles) for site monitoring enhances progress tracking and safety oversight, contributing to better project outcomes [71]. These innovations demonstrate that adopting modern methods and technologies is essential for addressing productivity challenges in the construction sector. A total of nine occurrences are identified in articles focusing on “method and technology for productivity” [18,21,29,30,32,34,37,38,42].

3.4. The Impact of BIM on Labor Productivity

While Building Information Modeling (BIM) is widely recognized as a digital representation of a facility’s physical and functional attributes that serves as a shared information framework [72,73], the empirical evidence supporting its productivity benefits reveals significant methodological inconsistencies that warrant critical examination. The current body of research demonstrates a troubling pattern of inadequately controlled studies, limited sample sizes, and inconsistent measurement approaches that undermine the reliability of reported productivity gains. According to Bryde et al. [74], despite analyzing 35 construction projects, suffers from fundamental methodological limitations that compromise its conclusions regarding BIM’s impact on costs, control, and time savings. The research lacks proper control groups, isolating BIM’s specific contribution from other variables that may influence project performance, making it impossible. Furthermore, the study fails to account for the learning curve associated with BIM implementation, potentially conflating initial adoption challenges with long-term productivity impacts. The reported benefits of “enhanced coordination and communication” remain largely anecdotal without standardized measurement protocols or statistical validation.

Khanzode et al. [75], which reports 5–25% productivity gains in MEP coordination at the Camino Medical Healthcare Center, exemplifies the methodological deficiencies in BIM productivity research. Critical analysis reveals several fundamental flaws that question the validity of these findings. First, the study employs a single-case design without control groups, making it impossible to establish causal relationships between BIM implementation and productivity improvements. The wide range of reported gains (5–25%) suggests significant measurement variability and a lack of standardized metrics, raising questions about the precision and reliability of the assessment methods. Moreover, Khanzode et al. [75] fail to provide adequate sample size justification or statistical power analysis, which are fundamental requirements for establishing the significance of observed productivity changes. The study’s reliance on subjective performance indicators, including “improved project planning” and “successful completion within deadlines,” lacks the quantitative rigor necessary for scientific validation. The absence of baseline productivity measurements prior to BIM implementation further compromises the study’s ability to demonstrate actual improvement rather than normal project variation. These methodological shortcomings are particularly concerning given the study’s widespread citation in subsequent research, potentially propagating unsubstantiated claims throughout the literature.

Sacks and Barak [76] examined the influence of BIM on productivity within structural engineering practices, reporting productivity improvements ranging from 15% to 41%. These gains were primarily attributed to reduced time spent on drawing tasks, showcasing BIM’s role in streamlining design workflows and optimizing resource use. Coats et al. [77] introduced the application of various Key Performance Indicators (KPIs) through action research conducted with a small UK-based construction company. Their study compared productivity by analyzing the man-hours spent on project tasks and evaluating the short duration of the project’s development and delivery phases. This approach highlighted how specific KPIs could effectively measure and track productivity levels, supporting better performance assessments and project outcomes.

A critical examination of the BIM productivity literature reveals systematic bias toward positive outcomes, with limited reporting of failed implementations or negative productivity impacts. This publication bias creates an artificially optimistic view of BIM’s effectiveness while obscuring the real challenges and limitations associated with its implementation. The absence of rigorous peer review standards for industry case studies further compounds this problem, allowing methodologically flawed research to influence academic discourse and industry practice. The persistent challenge of “quantifying the impact of BIM,” as acknowledged in the literature, reflects deeper issues with research design and measurement standardization rather than inherent difficulties in assessment. The field’s reliance on anecdotal evidence and poorly controlled studies undermines the development of evidence-based implementation strategies and realistic performance expectations.

Table 3. BIM-Related Research on Labor Productivity in Construction.

Reference	Title	Year	Country	Research
[15]	4D point cloud-based spatial-temporal semantic registration for monitoring mobile crane construction activities	2024	China	Determining the optimal placement of a BIM object and achieving high-resolution tracking of hoisting activities
[18]	Productivity improvement of precast shop drawings generation through BIM-based process re-engineering	2020	Singapore	BIM components which enable the automated shop drawings and reinforcement schedule generation
[21]	Measuring the impact of BIM on labor productivity in a small specialty contracting enterprise through action research	2015	Canada	BIM was used to visualize, coordinate, and negotiate work between trades, containing details like spool drawings for building the penthouse. It also benefited non-modeling trades, such as the electrical contractor
[22]	BIM-assisted labor productivity measurement method for structural formwork	2017	South Korea	It develops a method for acquiring field labor productivity data by integrating a 3D model with related information
[29]	Evaluating the Impact of Building Information Modeling on the Labor Productivity of Construction Projects in Malaysia	2020	Malaysia	This research evaluated the effect of BIM on labor productivity from the perspective of AEC professionals, instead of directly measuring labor productivity on-site
[30]	BIM-based and AR application combined with a location-based management system for the improvement of the construction performance	2019	Italy	The AR4C application is designed to improve project control by quickly identifying deviations from the project schedule and variations in performance and progress. This will be achieved by overlaying a 3D BIM model onto the real world using augmented reality (AR).
[31]	Automatic Estimation of Excavator’s Actual Productivity in Trenching and Grading Operations Using Building Information Modeling (BIM)	2023	Finland	The productivity is assessed by comparing the height between the desired mode and the actual map of nearby areas, which is updated every few seconds
[33]	A Real-Time Productivity Tracking Framework Using Survey-Cloud-BIM Integration	2020	Pakistan	The models were developed to utilize planned quantities for productivity tracking

presents studies on BIM-related research addressing labor productivity in construction, categorized by year and country, and including the research focus of each study.

3.5. Video and Image to Enhance Productivity

The widespread availability of point-and-shoot cameras, time-lapse devices, and smartphone cameras has greatly increased the number of photographs taken at construction sites. This trend has been further supported by the emergence of various photographic services in recent years, offering “visual” documentation of construction progress to project stakeholders [78]. New companies specializing in aerial robotics have entered the market, providing extensive collections of aerial imagery to enhance project monitoring and documentation.

The scientific community has utilized these images alongside multi-view geometry methods from computer vision to enable project-level monitoring. These collected images are instrumental in tracking construction progress and ensuring quality control. Time-lapse imagery, captured from fixed camera positions, documents work-in-progress (WIP) and allows for comparisons over time [79]; these images can be cross-referenced with a BIM model [3,8], represent the construction state to identify discrepancies and ensure alignment with project goals. Various visualization techniques employing color-coded construction elements can be used to assess discrepancies in construction progress [8]. One such method involves overlaying a 4D BIM model onto time-lapse photos to facilitate progress monitoring, enabling precise identification of deviations between the planned and actual state of the project. This integration of visual data enhances accuracy in tracking and managing construction timelines. The methods above are used to assess the on-site utilization and management of workers and equipment through progress monitoring. Analyzing the movements and tasks performed by workers and equipment and comparing them to productivity and performance metrics, either visually, through graphs, or via interviews, helps identify key causes of productivity deviations at the operational level. This approach allows for targeted improvements to enhance efficiency and address on-site challenges effectively.

Table 4. Video and Image-Based Research on Productivity Monitoring in Construction.

Reference	Title	Year	Country	Research
[16]	Multicamera vision-based productivity monitoring of earthmoving operations	2020	South Korea	Conducted experiments using video stream data recorded from an actual earthmoving site for highway construction, and analyzed jobsite videos collected from multiple non-overlapping cameras in the field of construction productivity monitoring
[17]	A vision-based approach for automatic progress tracking of floor paneling in off-site construction facilities	2021	Canada	Tracking the trajectory and movement of construction resources, as well as recognizing workers’ gestures
[20]	An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations	2011	USA	In comparison to the traditional manual construction video analysis method, the new method enables the interpretation of these videos into productivity information
[31]	Automatic Estimation of Excavator’s Actual Productivity in Trenching and Grading Operations Using Building Information Modeling (BIM)	2023	Finland	The productivity is calculated by comparing the height of the desired model to the actual map of surrounding areas, which is updated every few seconds
[34]	Improving tower crane productivity using wireless technology	2006	South Korea	Video devices integrated with RFID technology provided clear images of the site and materials, enabling workers to rely less on traditional methods of communication and achieve greater productivity
[36]	Construction performance monitoring via still images, time-lapse photos, and video streams: now, tomorrow, and the future	2015	China	Images, time-lapse photos, and video streams are used to document work in progress, detect, and track workers. From these, productivity information and progress deviations can be inferred based on a reduced video set

presents studies on video and image-based research addressing labor productivity monitoring in construction, categorized by year and country, and includes the research focus of each study.

4. Discussion

4.1. Summary of Evidence

The persistent demand for increased productivity in the construction industry highlights that this sector’s improvement efforts must meet expectations. Identifying current practices’ strengths and weaknesses and developing practical strategies aligned with technological advancements is crucial to implementing meaningful advancements. Adopting advanced practices is vital for project success, especially in today’s highly competitive business environment [80]. Aziz and Hafez [81] observed that, despite introducing numerous advanced methods that have contributed to modernizing construction processes over the past forty years, the desired levels of success and productivity still need to be adequately achieved.

From the perspective of construction life cycle tasks identified in the literature, this section highlights key features of digital technologies that, when integrated, enhance labor productivity in the CI. These measures collectively aim to promote the adoption of digital technologies within the sector. The findings reveal that most studies focus on the following areas: applications of BIM technologies implemented on-site [15,22,30,31,33], the use of BIM through company surveys [4], and BIM adoption during the design stage to enhance visualization [18,21]. Other areas include using video and images for monitoring progress and productivity [16,17,20,31,34,36] and employing LiDAR technology for data collection and real-time productivity calculation [15,31,32,33]. Based on the development of digital technologies for productivity control, the author identified three key areas for productivity analysis that align with technology functionalities aimed at improving productivity during the construction phase: Factors affecting productivity, methods and technologies for productivity improvement, and productivity modeling and evaluation.

A comprehensive assessment is essential to validate the importance of employing a system designed to enhance and manage labor productivity through digital tools. Most of the selected articles include a validation method, though diverse approaches have been utilized, such as questionnaires, interviews, and others, often resulting in comparative analyses of different solutions.

Among the 28 publications reviewed, case studies were featured in 11 articles [15,16,17,20,22,23,24,28,29,33,34]. Additionally, four studies utilized data collected through questionnaires [23,27,28,38], while complementary information gathered via interviews was noted in three other publications [26,40,41].

The 28 selected studies exhibit significant methodological heterogeneity that fundamentally impacts the comparability and generalizability of findings. Single case studies comprise the most significant proportion (14 articles, 50%), including technology-specific implementations. These studies provide detailed contextual insights but have fundamental limitations, including the absence of control groups, preventing causal inference, and limited external validity [15,16,17,18,20,21,22,29,30,31,32,33,34,36]. Comparative studies with historical or cross-sectional controls represent a smaller proportion (6 articles, 21%), which attempt to address causality concerns but introduce temporal or contextual confounding where productivity improvements may result from learning effects, market changes, or regional differences rather than technology adoption [23,25,27,28,39,40]. Survey and review-based studies (8 articles, 29%) provide broader industry perspectives but rely on subjective assessments and lack objective productivity measurements [19,24,25,26,35,37,38,42].

Sample size limitations are pervasive, with 71% of studies employing inadequate samples for statistical inference; single case studies inherently provide n = 1. In contrast, comparative studies typically include fewer than 10 projects per group, which is insufficient for detecting meaningful productivity differences. The absence of formal power analyses across all reviewed studies indicates fundamental gaps in research design rigor, suggesting many studies may be underpowered to detect actual technology effects. Furthermore, inadequate control for confounding variables, including project complexity, team experience, organizational culture, and concurrent process improvements, undermines internal validity, as exemplified by BIM implementation studies [18,22,29] that fail to isolate technology impacts from simultaneous organizational changes. This methodological heterogeneity prevents meaningful quantitative meta-analysis and suggests that reported productivity gains ranging from 10% to over 200% likely reflect measurement artifacts and study design limitations rather than actual technology effectiveness, requiring practitioners to interpret current evidence appropriately when making technology investment decisions.

In this review, the author aimed to address the eight research questions outlined earlier in Section 1. Following the PRISMA methodology, 28 articles were selected for analysis. The insights derived from these studies are summarized in

Table 5. Answers to article evaluation.

ID	Answers to the Research Questions
A1	Several advanced technologies are used to measure and manage the productivity of construction on site, each playing a crucial role in optimizing efficiency and resource allocation between these technologies. Among them, which stand out most by reference order are BIM, Cameras, LiDAR, GNSS, AR, UAV, and RFID.
A2	These technologies boost construction productivity by ensuring precision, seamless coordination, and operational efficiency. They enable improved collaboration, reduce errors, guarantee accurate positioning, facilitate real-time monitoring, provide aerial site inspections, generate precise 3D maps, and integrate digital models with the real world. Together, they streamline processes and deliver significant gains in labor productivity.
A3	Point cloud technologies have become essential in construction, significantly improving accuracy and efficiency in tasks like 3D scanning, mapping, and modeling. Initially, their use was limited due to high costs and technical complexity. However, as technology advanced, point cloud applications expanded from specialized surveying to key roles in BIM integration, as-built documentation, and facility management. By 2015, with improved hardware and more accessible software, point clouds became integral to precise measurements, reducing manual errors and enhancing project planning. Today, these technologies are critical in optimizing construction workflows and ensuring greater productivity and precision. Building Information Modeling (BIM), augmented and virtual reality (AR/VR).
A4	Several digital tools are essential for training workers on-site, providing hands-on learning, real-time feedback, and immersive experiences to prepare them for construction tasks. These tools enhance workers’ understanding of processes, safety, and operational procedures in a risk-free and efficient manner. AR and VR are among the most effective tools for on-site training, and BIM is another valuable digital training tool
A5	Video and image-based technologies have become essential tools for enhancing labor productivity in the construction industry. They offer visual insights, real-time monitoring, and actionable data that enable improved decision-making, process optimization, and resource management. Their significance lies in their ability to track progress, observe worker activities, identify inefficiencies, and ensure safety, directly leading to increased productivity
A6	BIM is not strictly necessary to support an on-site automatic productivity control solution, but it significantly enhances such systems’ effectiveness and integration. BIM provides a centralized and highly detailed digital representation of a construction project, which can be used to link real-time data from various automated productivity monitoring tools, such as sensors, cameras, and wearable devices. This integration helps to streamline workflows, ensure data consistency, and facilitate decision-making. While some productivity control systems can function without BIM, relying solely on point cloud data, images, or videos, these systems are often less coordinated and may lack the comprehensive scope that BIM enables. BIM’s ability to provide real-time updates, visualize construction progress, and link with other technologies makes it an asset for automatic productivity control
A7	Sensors are critical in enhancing labor productivity by providing real-time data, automating monitoring processes, and optimizing resource allocation. They improve efficiency by tracking equipment and material usage, monitoring environmental conditions, and providing insights into worker performance. By reducing manual data collection, sensors minimize errors and save time. Furthermore, they enable predictive maintenance, ensuring machinery operates smoothly without unexpected downtime. Sensors also enhance safety by detecting hazardous conditions and alerting workers, which reduces accidents and improves overall productivity on the construction site. Through these capabilities, sensors help streamline operations, improve decision-making, and optimize workflows, ultimately driving higher labor productivity
A8	The presented articles showcase results, with qualitative and quantitative data, which will be detailed below. These results provide a concrete and evidence-based analysis, offering a deeper understanding of the impact of technologies on construction productivity

as follows:

4.2. Productivity Improvement Measurement

Evaluating productivity improvement in construction is essential to assess the effectiveness of strategies, pinpoint opportunities for further optimization, and ensure efficient achievement of project goals. This process requires analyzing diverse metrics, leveraging advanced technologies, and integrating quantitative and qualitative methods. Accurate productivity evaluation is vital for identifying inefficiencies, optimizing resource allocation, and improving overall project performance. Despite advancements in techniques and technologies, the construction industry has faced challenges in reaching desired productivity levels, underscoring the need for a systematic approach to productivity enhancement [81].

Digital tools, such as BIM, time-tracking systems, and data collection technologies like sensors and video analysis, facilitate the evaluation of qualitative and quantitative productivity aspects. These tools provide valuable insights into labor efficiency and operational workflows, enabling more informed decision-making and tailored strategies for improvement. By offering a comprehensive understanding of productivity dynamics, these methods help the construction sector overcome longstanding challenges and drive performance gains effectively.

The combination of qualitative and quantitative analysis provides a holistic view of productivity in construction projects. Quantitative data, such as labor hours, material usage, and task completion rates, delivers measurable insights that can be used to benchmark performance. On the other hand, qualitative data obtained through interviews and observational studies sheds light on contextual factors like worker satisfaction, workflow challenges, and safety concerns. Studies emphasize the synergy between these two approaches, demonstrating how qualitative insights can validate and interpret quantitative findings [8]. Together, these methodologies enable construction managers to implement tailored strategies that address numerical targets and underlying human factors, ultimately driving productivity gains.

The potential productivity benefits associated with using digital tools were investigated in the articles analyzed, whose qualitative and quantitative results are summarized in

Table 6. Analysis of qualitative and quantitative results.

Reference	Results
	Quantitative Results	Qualitative Results
[15]	F1-score of 99.93–100% in BIM object detection	Safer operations reduce interruptions, sustaining productivity
[16]	The average error rate for the extraction of productivity information decreased from 23.8% to 8.8%	Improved accuracy in extracting productivity data. Detailed assessment of construction equipment operational efficiency
[17]	The presented approach can calculate the duration and the man-hours required per task with an accuracy above 92%	Enables rapid identification of bottlenecks or delays in production, improving decision-making, facilitates activity planning and prioritization, reducing idle time and increasing overall productivity and identifies errors or rework in early stages, preventing waste and improving overall performance
[18]	it was concluded that there would be a substantial time saving of 380 man hours	Reduced manual work through automation, streamlined process through BIM integration and parametric BIM components ensured consistency and reduced errors
[19]	Simulations showed a potential reduction of up to 10–15% time (hours/days) in the total control cycle time compared to traditional methods	Faster decision-making due to quicker feedback on project performance indicators (PPIs) and improved tracking of inventory using RFID and sensors
[20]	N/A	Interprets construction operation videos into productivity data (work cycles, delays, activity times). Reduces manual analysis effort and Integrates object recognition, tracking, and contextual reasoning (spatial, temporal, semantic)
[21]	For labor productivity with time as the input, there was an increase in productivity ranging from 75% to 240%. Percentage increase in output per labor unit in BIM-modeled/prefabricated areas compared to traditional methods	BIM and prefabrication significantly reduced on-site labor effort and improved task completion efficiency and improved accuracy of installation and fewer errors due to clash detection and coordinated planning
[22]	Maximum potential productivity (trendline projection): 61.25 m2/man-day	Integration of BIM spatial zoning + quantity take-off + actual labor records. Enables automatic productivity measurement and comparison zone-by-zone and establishes a best-fit trend to predict future productivity and compare planned vs. actual. Useful for control and forecasting
[23]	N/A	Productivity can be further enhanced by engaging skilled construction managers to deliver strong on-site leadership, enforce effective labor supervision standards, and collaborate with executive management to implement strategic initiatives
[24]	Average productivity loss of about 30% on days with change orders	The action-response model defines a new approach to evaluating the causes of productivity loss, divided into three categories: External conditions, Crew responses and Contractor actions
[25]	N/A	Is presented as each factor impacts productivity, conceptually, listing the factors such as: Improved Work Methods, Equipment Availability, Skilled Workforce, Material Planning and Logistics, and Supervision and Management
[26]	N/A	Demonstrated that the work sampling technique can effectively predict productivity per work unit in construction. This statistical method estimates the proportion of time workers devote to different categories of activities, such as direct work, supportive work, and delay
[27]	The RII for each factor investigated varies in value from 0 (not inclusive) to 1. The list of 5 areas: Errors and omissions in project drawings 0.922 Change orders during execution 0.896 Delay in responding to requests for information 0.878 Lack of labor supervision 0.867 Clarity of project specifications 0.862	The effects of the 33 factors surveyed on labor productivity in construction in Oman are determined. The data collected were analyzed using the relative importance index (RII) technique and listed as five main areas: Errors and omissions in the project drawings; Requests for change during execution; Delay in response to requests for information; Lack of labor supervision and clarity of project specifications
[28]	The RII for each factor investigated varies in value from 0 (not inclusive) to 1. The list of 5 areas: Planning 0.83 Worker–management relationship 0.83 Education and experience 0.81 Climate 0.81 Technology and equipment 0.80	The study highlights five equally important productivity factors forming a single cluster: planning, worker-management relationships, education and experience, technology and equipment. The data collected were analyzed using the relative importance index (RII) technique.
[29]	The five underlying factor categories explain about 19% of the variation in perceived labor productivity when BIM is adopted. Individual (Supervision): positive coefficient; highest perceived positive impact on productivity. Individual (Labor): negative coefficient; perceived as reducing labor productivity when adopting BIM	Individual supervision (site supervision, field coordination) has the greatest perceived positive impact when BIM is implemented. Individual (Labor) workers’ skills, experience, and BIM proficiency can negatively affect productivity if not adequately trained and education and training are crucial to mitigate negative impacts from low-skilled labor and to maximize productivity gains from BIM adoption
[30]	10–20% reduction in time (hours/days) required for task completion and up to 15% (days) reduction in delays for critical tasks	3D model overlay in AR improves workers’ understanding of tasks. Real-time progress visualization helps quickly identify delays
[31]	Average productivity in grading (0.03 m2/s) and Average productivity in trenching (0.01 m/s)	Assess productivity and track the progress of operations
[32]	Reduced the working time by about 13 h compared with the conventional method. Assuming 8 h of work per day, the number of workdays was reduced by 1.6 days	Productivity has increased
[33]	Compliance with planning >90% in test cases	Allows managers to instantly track actual vs. planned progress, enabling quick adjustments. Operators can correct errors and optimize tasks before small issues become delays
[34]	The case study showed that the work speed, meaning the total cycle time for each lifting activity, improved by 9.9–38.9%, with an average improvement of 26.5%	Overall productivity increases through error reduction and optimized lifting operations
[35]	N/A	Planning and coordination, team communication, task complexity, required skills and use of modern tools and equipment improving productivity
[36]	N/A	Visual monitoring of infrastructure or building elements, visual monitoring of equipment and construction workers and use of computer-vision–based techniques
[37]	N/A	Emphasizes the importance of fundamentals such as focus on project value, lifecycle concept, and workflow management to reinforce the implementation of advanced techniques that include BIM, automation, 3D printing, modular construction, artificial intelligence, and others for improving productivity
[38]	N/A	Emphasizes the importance of integrating advanced digital techniques with productivity fundamentals to enhance construction productivity
[39]	Productivity rates for erecting concrete reinforcing footings vary from 0.28 to 2.10 m3 per person-hour	Shows long-term reduction in efficiency; highlights the need for process improvement. Pandemic significantly reduced workforce availability and productivity
[40]	Productivity decreases by up to 25% in time (hours/days)	Difficulty recruiting supervisors and skilled workers, leading to errors and rework. Weak supervision and organization lead to inefficiency and waste
[41]	Productivity decreased by 12% to 18% in time (hours/days)	Poor planning, inadequate coordination, and unrealistic goals and deadlines have a significant impact on productivity
[42]	6% productivity increase with better workforce management and 50% of the average profit increase comes from a 6% productivity improvement	Identify the most critical aspects in terms of human, external and management issues that affect construction productivity

. Quantitative findings provide objective performance metrics, including percentage improvements, efficiency ratios, and statistical significance indicators that enable empirical technological impact assessment. While metric heterogeneity across studies limits direct comparative analysis, the aggregated data reveal consistent productivity enhancement patterns. Qualitative evidence contextualizes these metrics by elucidating implementation mechanisms, organizational factors, and operational constraints influencing technology adoption outcomes. The convergent analysis of quantitative performance data and qualitative implementation insights enables robust evaluation of digital technologies’ productivity impact and facilitates evidence-based recommendations for construction industry applications.

4.3. Limitations of the Systematic Review

This systematic review presents several methodological limitations that constrain the scope and generalizability of findings. The deliberate focus on digital tools for monitoring and analyzing labor productivity, while providing depth, significantly limited the number of relevant studies and excluded research examining broader productivity dimensions such as material efficiency, equipment utilization, and automated construction processes. Additionally, restricting English-language publications and reliance on Scopus and Web of Science databases may have introduced geographic bias, potentially excluding relevant research from non-English-speaking countries with significant construction technology development programs. The broad construction industry focus may have obscured sector-specific patterns and requirements, as different construction sectors face distinct productivity challenges and technology adoption constraints.

The included studies demonstrated substantial methodological heterogeneity, employing diverse research designs, outcome measures, and contexts, which prevented quantitative meta-analysis and required reliance on narrative synthesis approaches. The absence of standardized productivity measurement protocols across studies limited meaningful quantitative comparisons and may have obscured important patterns in technology effectiveness. Furthermore, studies spanning approximately two decades of rapid technological evolution encompassed multiple generations of digital technologies, from early BIM implementations to contemporary AI applications, potentially complicating the synthesis of findings across varying technological capabilities and maturity levels.

Significant literature search limitations emerged during the review process, where 9 out of 25 studies retrieved through backward snowballing contained keywords identical to the initial database query yet were not captured by the systematic search, suggesting substantial inconsistencies in keyword indexing practices and metadata representation. These limitations provide crucial context for interpreting the review findings and highlight priority areas for future research, including the development of standardized productivity measurement protocols, expanded multi-database and multi-language search strategies, sector-specific investigations, longitudinal impact assessments, and international collaborative initiatives to ensure more comprehensive global representation of construction technology research.

5. Conclusions

The study addressed research questions about assessing productivity in construction tasks using digital technologies, as outlined in Section 1. Adhering to the established research protocol and selection criteria, 28 articles were meticulously analyzed and chosen from an initial pool of 431. Eight key research questions were developed and addressed, examining various digital technologies and productivity management aspects. This review emphasizes productivity management and analysis, featuring a comprehensive exploration of digital tools, especially BIM, images, and videos, as referenced in Table 3 and Table 4.

The limitations of this study stem from the inclusion and exclusion criteria applied, which led to a reduced number of analyzed articles. A key constraint was the focus on using digital tools to monitor and analyze labor productivity. This narrowed scope significantly limited the relevant research studies in the Construction Industry.

This study comprehensively synthesizes how digital technologies transform construction productivity management. The review establishes a clear connection between innovative tools and their practical applications, such as progress tracking, real-time data collection, and predictive modeling, by categorizing technologies and methodologies. Identifying recurring themes, including the critical role of BIM in productivity enhancement and the increasing importance of image-based monitoring, enriches the understanding of digital innovation in construction. Furthermore, the study highlights the evolution of Industry 4.0 technologies and their impact on productivity in labor-intensive industries such as construction.

The findings have significant practical implications for construction professionals and industry stakeholders. Quantitative and qualitative evidence support adopting digital technologies to enhance construction productivity. Studies have shown that managers can implement BIM to streamline workflows, improve coordination, and reduce errors. Tools such as UAVs and RFID systems enable real-time monitoring of site activities, while photogrammetry and time-lapse photography support quality control and performance evaluation. Furthermore, computer vision and AR technologies have demonstrated the potential to transform on-site and off-site construction processes by automating routine monitoring tasks and delivering actionable insights. These technologies allow for more accurate resource allocation, reduced labor costs, and better scheduling, creating opportunities for more efficient project execution.

5.1. Technology Selection by Project Context

BIM shows exceptional promise for complex projects exceeding $50 million with multiple stakeholders and extended timelines. BIM’s coordination capabilities benefit healthcare facilities, educational campuses, and mixed-use developments. Projects with formal digital execution plans achieved 23% better coordination efficiency [82]. In construction contexts, information asymmetries manifest as design-construction disconnects, material specification uncertainties, and trade coordination failures. BIM addresses these asymmetries by providing unified data models accessible to all stakeholders, reducing rework rates by 15–25% through improved information transparency [75,76]. BIM implementation succeeds in organizations with established change management capabilities and projects exceeding 18-month durations.

UAV and photogrammetry excel in large-scale monitoring applications spanning extensive geographical areas or hazardous environments. Infrastructure projects such as highways, railways, and utility installations demonstrate optimal UAV deployment. The Crossrail project exemplifies this approach, applying laser scanning over 118 km with millimeter-level precision and 60% cost reduction [83]. UAV proves most cost-effective for sites exceeding 10 hectares requiring frequent progress monitoring. In construction, traditional progress monitoring occurs weekly or monthly, creating a substantial lag between productivity problems and corrective interventions. UAV-based monitoring enables daily or real-time progress assessment, reducing the time between problem identification and resolution from weeks to hours.

GNSS and RTK systems deliver superior performance in earthwork-intensive projects where precision positioning provides substantial productivity gains. The Panama Canal expansion demonstrates optimal deployment, achieving centimeter-level accuracy across 40 km and 20% productivity improvement [84]. In earthmoving operations, GNSS-guided equipment achieves centimeter-level accuracy consistently, eliminating the 5–15% material waste typical in manual grade control operations.

RFID and computer vision prove effective for complex material management and safety-critical operations. These technologies suit prefabricated construction, high-rise buildings, and industrial facilities. RFID and computer vision tool tracking reduces search time and tool loss [61], which is particularly valuable for projects with inventories exceeding 1000 items and durations longer than 12 months.

Machine learning algorithms applied to construction data enable predictive maintenance, optimal resource allocation, and proactive problem identification. Theoretical grounding derives from operations research and optimization theory, where improved information quality and predictive capabilities enable superior resource allocation decisions [85] Caterpillar’s Cat Product Link System integrates machine learning in construction machinery to provide predictive maintenance and operational efficiency. The system collects data from equipment and uses AI algorithms to predict maintenance needs, significantly reducing downtime and improving equipment longevity.

5.2. Implementation Strategy

Construction organizations should adopt structured digital transformation using the Integrated Project Delivery (IPD) model. BIM-enabled IPD projects achieved 25% faster delivery times and 30% cost reductions [73] Implementation should follow an incremental approach: foundational technologies (BIM, GNSS) first, followed by monitoring systems (UAV, RFID), then advanced analytics (computer vision, machine learning).

Organizations with annual revenues exceeding $100 million demonstrate higher success rates with comprehensive digital transformation, while smaller contractors achieve better outcomes by focusing on specific, high-impact technologies aligned with their project types and core competencies.

5.3. Future Directions

This review demonstrates digital technologies’ critical role in addressing construction productivity challenges. However, challenges remain in quantifying long-term impacts and ensuring widespread adoption. Future research should focus on developing integrated systems and evaluating scalability across different contexts. Strategic technology selection based on project characteristics, organizational capabilities, and specific productivity challenges represents the most promising approach for successful digital transformation in construction.