Construction Labor Productivity Monitoring: Comparison
Please note this is a comparison between Version 2 by Sirius Huang and Version 1 by Lee Tsu Yian.

Construction labor productivity (CLP) is a critical measure of efficiency in the construction industry. CLP monitoring entails tracking, measuring, and evaluating labor productivity to identify areas for improvement and ensure that project objectives are accomplished. 

  • construction projects
  • labor productivity
  • workforce

1. Introduction

Construction labor productivity (CLP) monitoring is a major area of concern in the construction industry. The construction industry has high labor needs, and labor costs and labor resources frequently make up a significant portion of project costs. Labor costs typically constitute approximately 40–60% of total construction costs [1,2][1][2]. A previous study estimated that clients would save GBP 1.5 billion by improving CLP by 10% in the UK, underscoring the need for an effective labor productivity monitoring approach on construction sites [3]. In this context, managing and monitoring CLP are critical for the success of construction projects. However, managing and monitoring CLP can be challenging, especially in large construction projects. Project cost overruns and delays caused by CLP issues may result in significant financial losses for project owners and contractors.
Construction productivity is generally defined as a ratio of output to input [4]. Construction productivity considers input from all resources in the measurement, including but not limited to time, cost, labor, and equipment. On the other hand, CLP focuses solely on the labor resource. Labor is the resource that directly handles material and creates construction products [5]. Concentrating on labor as a single resource measures productivity in a more controllable and accurate manner [6]. CLP monitoring entails tracking, measuring, and evaluating labor productivity to identify areas for improvement and ensure that project objectives are accomplished. Depending on the level of measurement, whether macro or micro, CLP can be assessed using various input and output variables [7]. Macro-level measurement is typically conducted at the project, industrial, and national levels to evaluate and benchmark the overall productivity, with less specificity of site working conditions needed [7]. Conversely, micro-level measurement involves assessing productivity at individual and crew (trade) levels, requiring in-depth data insights that necessitate more time and effort on site. Previous studies have defined CLP in different ways, such as output per labor cost [8], output per labor work hour [9], and productive time used to complete a task [10].
Numerous studies have delved into different facets of CLP monitoring, including the relationship between CLP and construction project success [11], CLP influencing factors [8[8][12][13],12,13], developing models for predicting and monitoring CLP [14,15,16,17][14][15][16][17], and proposing strategies for improving CLP [18,19,20,21][18][19][20][21]. CLP is influenced by a range of factors, both technological and non-technological. Technological advancements, such as the adoption of Building Information Modeling (BIM) [9[9][22],22], sensor technologies [23[23][24],24], computer vision technologies [25[25][26],26], and data analytics tools [27[27][28],28], have revolutionized CLP monitoring. Non-technological factors, such as workforce characteristics (such as skill levels, experience, well-being, and motivation), project-related elements (such as task complexity, material shortage, project type, and finances) [8[8][29][30],29,30], as well as external factors (including weather conditions and regulatory requirements) [13[13][31][32],31,32], significantly contribute alongside technological factors. Furthermore, non-technological and technological factors can interact in certain aspects. For example, sensor technologies can track labor’s physical demand [33] and monitor heat stress [34], demonstrating the use of technology in supporting and enhancing the monitoring of non-technological aspects.

2. Key Concepts of Productivity and Labor Productivity in the Construction Industry

Productivity can be defined as the quantity of output produced per unit of input and is a measure of how effectively resources are utilized to produce goods and services in the construction sector [35,37,61][35][36][37]. Labor productivity, another important concept in the construction industry, measures the amount of output produced per unit of labor input [6,36][6][38]. The analysis revealed that “labour productivity” was one of the top three keywords, with 55 occurrences, 37 links, and a total link strength of 77. Labor productivity is often used as an indicator of the overall productivity of the construction industry because labor is one of the primary inputs in construction projects. Despite the high occurrence of both “productivity” and “labour productivity”, the researchers did not find any significant association between these two terms as no links were found between them. However, the bibliometric map revealed that these two keywords co-occur with numerous other keywords that are similar, including “occupational health and safety”, “work sampling”, “productivity measurement”, “lean construction”, “efficiency”, “building information modelling”, and others. Despite the absence of any direct linkages between the two keywords, these data imply that there is some degree of association between them. This finding may be caused by inconsistencies in productivity measurement and reporting in the literature when researchers employ different definitions or metrics of productivity in their studies. For instance, some studies define labor productivity as the productive time used to produce the output [6,10][6][10], while others refer to it as the output per total working hours [9]. Furthermore, CLP can be measured at various levels, from macro to micro, with different measurement units at each level [7,62][7][39]. The authors recommend that future studies carefully define and operationalize the terms used to ensure consistent and reliable measurements of productivity in the field and facilitate better comparisons between studies.

3. CLP Influencing Factors and Improvement Approaches

CLP monitoring is essential for improving project performance and profitability in the construction industry. By identifying and addressing areas of inefficiency, firms can increase their productivity, reduce costs, and gain a competitive advantage.  CLP research has focused on identifying the various factors that affect it, minimizing their impact, and improving work processes. Workforce-related factors, such as skill level, training, motivation, and health and safety, as well as project-related factors such as management, planning, design, and technology, are the most common factors affecting CLP [12,29,30,63,64,65][12][29][30][40][41][42]. However, some studies have only focused on specific geographic regions or types of construction projects, which limits their generalizability to other contexts. The literature has also paid less attention to other factors, such as political and economic issues, as well as cultural and social norms. Future research should consider a broader range of factors and contexts to develop more robust and context-specific strategies to improve CLP. Moreover, most existing studies used cross-sectional designs [12,29,30,63,64[12][29][30][40][41][42],65], which have limitations in determining causality and providing insight into changes over time. To better understand the causal relationships between variables and track changes in CLP over time, future research in the construction industry should incorporate longitudinal designs using real-time technology. This approach could provide valuable information for evaluating the effectiveness of interventions aimed at improving CLP and identifying the factors that contribute to long-term improvements. High occurrences of the keyword “Occupational health and safety” (25 occurrences) indicate that it is a key factor in improving CLP. This is further evidenced by the occurrence of keywords related to occupational health and safety, including “heat stress”, “safety climate”, “safety performance”, “accidents”, “well-being”, “risk management”, “labour and personnel issues”, and “ergonomics”. The integration of safety practices and productivity improvement strategies has been emphasized as a comprehensive approach that considers both safety and productivity [66,67,68][43][44][45]. According to [34[34][46],69], labor well-being significantly affects CLP, with heat stress having a significant detrimental effect. The use of wearable biosensors to measure worker stress levels has also been investigated, and the results indicate their potential for enhancing productivity and safety [70][47]. It is essential to highlight that by emphasizing safety and health alongside productivity, researchers and industry professionals in the construction sector may create a safer and more productive workplace. Productivity is crucial; however, worker safety must never be sacrificed. In multistory construction projects, a hybrid approach that assesses the impact of safety management practices on CLP brings attention to the relationship between productivity and safety [68][45]. However, the study by [67][44] also warns about the unanticipated effects of productivity development measures on safety behavior. Therefore, it is essential to explore new ways to comprehensively integrate safety and productivity in addition to current approaches. The construction industry can also consider incorporating Building Information Modeling (BIM), labor tracking technologies, and lean practices to improve safety and productivity. BIM has been demonstrated to assist in safety performance [71][48] and CLP monitoring [18]. Lean practices aim to maximize value and minimize waste, which can lead to improved safety and productivity [66][43]. Real-time tracking technologies can also be used to constantly monitor the well-being and activity status of labor, leading to increased CLP and performance [72,73][49][50]. Thus, a comprehensive approach utilizing BIM, labor tracking technologies, and lean practices can further enhance safety and productivity in the construction industry. Change orders are a common occurrence in the construction industry, where modifications to original project plans are requested by clients or stakeholders. The impact of change orders on CLP has been extensively studied in previous studies. Refs. [74,75,76][51][52][53] used different approaches, such as system dynamics modeling and evolutionary fuzzy support vector machine inference modeling, to predict the productivity loss caused by change orders. Understanding the impact of change orders on CLP is important because they can result in delays, increased costs, and decreased productivity, which can ultimately affect the success of a project [74,75,76][51][52][53]. Construction managers can more accurately analyze the potential impact of change orders and take necessary action to reduce their detrimental effects on productivity by establishing models to quantify this impact. However, the significance of project management techniques, including lean approaches, risk management, and communication strategies, in reducing the negative impact of change orders on CLP has received less attention in the present research field. More thorough research is required to better understand how change orders affect CLP and to develop effective strategies to minimize their impact. The comparison of the bibliometric findings with existing studies on factors influencing CLP reveals a notable absence of labor skill and experience in the keyword co-occurrence list, despite their commonly identified significance [8,29,30][8][29][30]. This discrepancy indicates a research gap in the emphasis on these factors within the literature captured by the analysis. This is attributed to the predominant use of questionnaire surveys as the common research method [8[8][29][30],29,30], which led to a lack of focused investigation on the identified top factors. Consequently, there is a need for further investigation and exploration of the role of labor skill and experience in CLP monitoring and improvement to bridge this research gap. Lean construction has emerged as a widely adopted strategy in the construction industry to reduce waste and optimize efficiency [77][54]. By leveraging strategies such as just-in-time delivery, continuous improvement, and standardized work, workflows can be streamlined, material waste can be reduced, and labor resources can be optimized. One common method used to achieve this is work sampling, which is also known as activity analysis. Work sampling has been used for decades to examine workflow efficiency, identify workflow variability, and eliminate non-value-adding work time [78,79,80,81][55][56][57][58]. However, there is still a lack of consensus on how to define and measure these metrics accurately. Further research is required to standardize the definitions and methodologies for measuring workflow variability. Additionally, the advancement of technology has transformed traditional work sampling into automated work sampling, with computer vision [28,82][28][59] and wearable sensors [83,84][60][61] showing potential for monitoring the physical and physiological conditions of labor. However, further investigation is required to ascertain the reliability and effectiveness of these technologies for real-world construction projects. Further investigation and study are needed to fully understand the direct impact of automated work sampling on CLP. The potential of technology such as the KanBIM workflow management system has also been demonstrated to improve craft time utilization. The integration of lean principles and BIM technology has produced encouraging improvements in workflow efficiency and error reduction [18]. Stimulation tools have also been investigated to detect construction workflow bottlenecks and assess the effectiveness of lean initiatives [20]. A study [85][62] that adopted lean principles during the COVID-19 pandemic further highlighted the value of using these principles to manage labor in the construction industry. Lean construction practices prioritize process improvement and waste minimization, which are helpful in ensuring labor safety and the ongoing development of construction projects during the pandemic [85][62]. In summary, the construction industry faces various challenges in managing and improving CLP, ranging from workforce-related to project-related factors. It is crucial to consider a thorough and integrated strategy that incorporates lean principles, modern technologies such as BIM, and the labor tracking approach, as well as safety measures and productivity improvement strategies. Moreover, future research should consider a broader range of factors and contexts to develop more reliable, robust, and context-specific strategies to enhance CLP. Adopting a proper strategy for boosting CLP benefits construction firms by reducing costs and gaining a competitive edge in the construction sector. The opportunities for enhancing CLP are limitless due to the rapid advancements in technology and continuous changes in the industry, highlighting the need to remain updated with the latest developments in the field.

4. Innovations and Technologies for CLP Data Collection

The construction business is a vibrant, constantly changing sector that seeks new ways to boost productivity and efficiency. The implementation of BIM, which has drawn significant attention in CLP monitoring, is one of the notable advances in this field. Specifically, the keyword “Building Information Modelling (BIM)” was used 11 times, and it appears together with “visualization”, “prefabrication”, “occupational safety and health”, “construction planning”, and “labor productivity” keywords. This indicates that BIM is increasingly being used for monitoring safety and health hazards and labor productivity in the construction industry, moving beyond its primary design and planning functions [9,13,86][9][13][63]. Previous research has demonstrated that the visualization capabilities of BIM can identify potential safety hazards and promote worker safety at construction sites [87][64]. Furthermore, prefabrication is another innovation that has emerged to enhance CLP, enabling the manufacture of building modules in a factory and their subsequent on-site assembly. This process results in improved productivity and quality and reduced construction time and waste. Moreover, studies on adopting BIM and Mixed Reality (MR) for prefabrication projects have been conducted, demonstrating the potential of MR technology for CLP improvement in the industry [88][65]. The highly cited paper “Towards a Mixed Reality System for Construction Trade Training” [54][66] exemplifies this potential. While the keyword “mixed reality” did not meet the threshold for bibliometric analysis, research findings revealed that the integration of BIM and MR positively influences CLP. However, further research is required to establish the optimal use of MR technology in the construction industry. Construction labor tracking technologies, as indicated by the keywords “computer vision”, “action recognition”, “activity recognition”, “location tracking”, “tracking”, and “wearable sensor”, have the potential to revolutionize the way labor productivity is monitored and managed in the construction industry, enabling real-time monitoring of labor productivity, and providing insights into every movement and activity. Currently, computer vision and wearable sensors are used for labor tracking. With computer vision, camera devices are employed to capture site conditions, including worker movements and activities. By leveraging machine learning and deep learning algorithms, the labor activity status can be recognized [25[25][67][68][69],89,90,91], with some studies even using computer vision for ergonomic posture monitoring [92,93][70][71]. On the other hand, different types of wearable sensors such as Radio Frequency Identification (RFID), Global Positioning System (GPS), Bluetooth Low Energy (BLE), accelerometers, heart rate sensors, and temperature sensors can be worn by laborers to track their presence in specific zones [94[72][73],95], recognize the activity status [83[60][74],96], and assess their well-being in terms of workload, heart rate, and working intensity [97][75], ultimately linking these factors to CLP [95,98][73][76]. However, despite the potential of wearable sensors, they were the least frequently occurring keyword in the bibliometric map, with only three occurrences. This suggests that there is still much to explore in terms of how wearable sensors can be used to monitor and optimize labor productivity in the construction industry. With further investigation, wearable sensors could be a game-changer for labor tracking, providing valuable data on worker movements and activities, and helping project managers identify areas for improvement and optimize resource allocation. However, the adoption and implementation of these technologies may be limited by cost, specialized expertise, privacy and security concerns, and potential social and ethical implications. Therefore, it is essential to refine and optimize the use of these technologies in the construction industry, considering broader social and ethical considerations. Although the discussion provides a comprehensive overview of innovations and technologies for CLP data collection in the construction industry, further investigation is necessary to ensure their optimal use and alignment with social and ethical considerations.

5. CLP Prediction Models

Various models have been developed and applied in construction projects to monitor and improve CLP. The findings suggest that machine learning and its subfields, such as deep learning and ANN, are popular keywords in the construction industry because of the growing availability of construction-related data and their potential to improve productivity at construction sites. The neural network model has been increasingly used for CLP monitoring, owing to its ability to learn the complex nonlinear relationships between variables. An ANN is commonly adopted to analyze the complex relationship between variables for CLP prediction, as shown in previous studies [15,16,99][15][16][77]. Meanwhile, deep learning methods such as recurrent neural networks and convolutional neural networks have been adopted for labor activity recognition, which involves work sampling to monitor CLP [100,101,102][78][79][80]. These neural network models have shown promise for improving the accuracy of productivity predictions. However, vast datasets are required to train the model. They are also often criticized for their lack of transparency, which makes it difficult for laypeople to understand how they came to their predictions. Regression analysis is one of the earliest modeling methods applied to analyze construction productivity. Its application to CLP analysis was initially published in 1899 [103][81] and is still widely used today. According to various studies [17,68[17][45][82],104], regression models are frequently used to measure and model CLP using historical data. However, regression models have limitations when addressing complicated and nonlinear connections between variables, which may impair their ability to accurately forecast future productivity. Fuzzy models that can account for uncertainty and imprecision in construction data, as well as the subjective nature of productivity factors, were developed using fuzzy logic and fuzzy set theory. For example, these models have been applied to evaluate the motivation for labor [105][83] and to predict context-specific labor productivity, where the data can have a high level of subjectivity [14,105][14][83]. However, fuzzy models can be difficult to interpret, and their correctness may be based on the caliber of the expert knowledge utilized to create them. Therefore, it is essential to evaluate models by using real-world information to determine their accuracy. System dynamics models consist of a complex, interrelated structure that uses feedback loops to model the dynamic relationships between the CLP-influencing factors. This model is useful for understanding the complex relationships between different CLP influencing factors [74,76][51][53]. System dynamics models employ a feedback loop to replicate the dynamic interactions between CLP-influencing factors. This approach helps comprehend the intricate relationships between the various factors that impact production [74,76][51][53]. However, system dynamics models may not be appropriate for real-time monitoring because they are complex and require significant expertise to develop, calibrate, and apply effectively. Data envelopment analysis is a useful method for measuring the efficiency and productivity of construction. However, its adoption in research and study is constrained by its complexity and specialized knowledge requirements. It is commonly used for benchmarking and comparing the performance of construction projects, companies, and the industry [11], making it more suitable for larger-scale analyses rather than predicting CLP at individual and trade levels. In summary, different modeling methods have been developed to monitor and improve CLP, based on data availability, relationship complexity, and required expertise. Future research should aim to develop transparent, real-time models that combine methods such as fuzzy, system dynamics, neural networks, and regression. Hybrid models should be developed to leverage the strengths of these methods and provide comprehensive insights into CLP.


  1. Smith, R.C. Estimating and Tendering for Building Work; Routledge: London, UK, 2013.
  2. Neve, H.; Wandahl, S.; Lindhard, S.; Teizer, J.; Lerche, J. Learning to see value-adding and non-value-adding work time in renovation production systems. Prod. Plan. Control 2022, 33, 790–802.
  3. Naoum, S.G. Factors Influencing Labor Productivity on Construction Sites: A State-of-the-Art Literature Review and a Survey. Int. J. Product. Perform. Manag. 2016, 65, 401–421.
  4. Hwang, B.-G.; Soh, C.K. Trade-Level Productivity Measurement: Critical Challenges and Solutions. J. Constr. Eng. Manag. 2013, 139, 04013013.
  5. Khanh, H.D.; Kim, S.-Y.; Van Khoa, N.; Tu, N.T. The relationship between workers’ experience and productivity: A case study of brick masonry construction. Int. J. Constr. Manag. 2021, 23, 596–605.
  6. Jarkas, A.M.; Horner, R.M.W. Creating a baseline for labour productivity of reinforced concrete building construction in Kuwait. Constr. Manag. Econ. 2015, 33, 625–639.
  7. Moohialdin, A.S.M.; Lamari, F.; Miska, M.; Trigunarsyah, B. Construction worker productivity in hot and humid weather conditions: A Review of Measurement Methods at Task, Crew and Project Levels. Eng. Constr. Arch. Manag. 2020, 27, 83–108.
  8. Hiyassat, M.A.; Hiyari, M.A.; Sweis, G.J. Factors affecting construction labour productivity: A case study of Jordan. Int. J. Constr. Manag. 2016, 16, 138–149.
  9. Lee, J.; Park, Y.-J.; Choi, C.-H.; Han, C.-H. BIM-assisted labor productivity measurement method for structural formwork. Autom. Constr. 2017, 84, 121–132.
  10. Kumar, Y.; Kumar, G.H.; Myneni, S.B.; Charan, C.S. Productivity Analysis of Small Construction Projects in India. Asian J. Appl. Sci. 2014, 7, 262–267.
  11. Nazarko, J.; Chodakowska, E. Labour efficiency in construction industry in Europe based on frontier methods: Data envelopment analysis and stochastic frontier analysis. J. Civ. Eng. Manag. 2017, 23, 787–795.
  12. Jarkas, A.M.; Bitar, C.G. Factors Affecting Construction Labor Productivity in Kuwait. J. Constr. Eng. Manag. 2012, 138, 811–820.
  13. Wu, Q.; Chen, L.; Shi, P.; Wang, W.; Xu, S. Identifying Impact Factors of MEP Installation Productivity: An Empirical Study. Buildings 2022, 12, 565.
  14. Tsehayae, A.A.; Fayek, A.R. Developing and Optimizing Context-Specific Fuzzy Inference System-Based Construction Labor Productivity Models. J. Constr. Eng. Manag. 2016, 142, 04016017.
  15. Nasirzadeh, F.; Kabir, H.D.; Akbari, M.; Khosravi, A.; Nahavandi, S.; Carmichael, D.G. ANN-based prediction intervals to forecast labour productivity. Eng. Constr. Arch. Manag. 2020, 27, 2335–2351.
  16. Badawy, M.; Hussein, A.; Elseufy, S.M.; Alnaas, K. How to predict the rebar labours’ production rate by using ANN model? Int. J. Constr. Manag. 2021, 21, 427–438.
  17. Ma, L.; Liu, C. Decomposition of temporal changes in construction labour productivity. Int. J. Constr. Manag. 2018, 18, 65–77.
  18. Rafael, S.; Ronen, B.; Biniamin, B.; Ury, G.; Ergo, P. KanBIM Workflow Management System: Prototype Implementation and Field Testing. Lean Constr. J. 2013, 19–35.
  19. Liu, M.; Ballard, G.; Ibbs, W. Work Flow Variation and Labor Productivity: Case Study. J. Manag. Eng. 2011, 27, 236–242.
  20. Bajjou, M.S.; Chafi, A. Lean construction and simulation for performance improvement: A case study of reinforcement process. Int. J. Prod. Perform. Manag. 2020, 70, 459–487.
  21. Khaleghian, H.; Shan, Y.; Lewis, P. A Case Study of Productivity Improvement by Electrical Prefabrication. In Proceedings of the Construction Research Congress 2016, San Juan, PR, USA, 31 May–2 June 2016; pp. 1753–1761.
  22. Arif, F.; Khan, W.A. A Real-Time Productivity Tracking Framework Using Survey-Cloud-BIM Integration. Arab. J. Sci. Eng. 2020, 45, 8699–8710.
  23. Rao, A.S.; Radanovic, M.; Liu, Y.; Hu, S.; Fang, Y.; Khoshelham, K.; Palaniswami, M.; Ngo, T. Real-time monitoring of construction sites: Sensors, methods, and applications. Autom. Constr. 2022, 136, 104099.
  24. Calvetti, D.; Mêda, P.; Gonçalves, M.C.; Sousa, H. Worker 4.0: The Future of Sensored Construction Sites. Buildings 2020, 10, 169.
  25. Luo, H.; Xiong, C.; Fang, W.; Love, P.E.; Zhang, B.; Ouyang, X. Convolutional neural networks: Computer vision-based workforce activity assessment in construction. Autom. Constr. 2018, 94, 282–289.
  26. Xu, S.; Wang, J.; Shou, W.; Ngo, T.; Sadick, A.-M.; Wang, X. Computer Vision Techniques in Construction: A Critical Review. Arch. Comput. Methods Eng. 2021, 28, 3383–3397.
  27. Nath, N.D.; Behzadan, A.H. Construction Productivity and Ergonomic Assessment Using Mobile Sensors and Machine Learning. Comput. Civ. Eng. 2017, 434–441.
  28. Ying, W.; Shou, W.; Wang, J.; Shi, W.; Sun, Y.; Ji, D.; Gai, H.; Wang, X.; Chen, M. Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning. Appl. Sci. 2021, 11, 4143.
  29. Jarkas, A.M. Factors influencing labour productivity in Bahrain’s construction industry. Int. J. Constr. Manag. 2015, 15, 94–108.
  30. Tuan Hai, D.; Van Tam, N. Analysis of Affected Factors on Construction Productivity in Vietnam. Int. J. Civ. Eng. Technol. 2019, 10, 854–864.
  31. Agrawal, A.; Halder, S. Identifying factors affecting construction labour productivity in India and measures to improve productivity. Asian J. Civ. Eng. 2020, 21, 569–579.
  32. Gurcanli, G.E.; Mahcicek, S.B.; Serpel, E.; Attia, S. Factors Affecting Productivity of Technical Personnel in Turkish Construction Industry: A Field Study. Arab. J. Sci. Eng. 2021, 46, 11339–11353.
  33. Hwang, S.; Lee, S.H. Wristband-type wearable health devices to measure construction workers’ physical demands. Automat. Constr. 2017, 83, 330–340.
  34. Chinnadurai, J.; Venugopal, V.; Kumaravel, P.; Paramesh, R. Influence of occupational heat stress on labour productivity–a case study from Chennai, India. Int. J. Prod. Perform. Manag. 2016, 65, 245–255.
  35. Yi, W.; Chan, A.P.C. Critical Review of Labor Productivity Research in Construction Journals. J. Manag. Eng. 2013, 30, 214–225.
  36. Adebowale, O.J.; Agumba, J.N. A scientometric analysis and review of construction labour productivity research. Int. J. Prod. Perform. Manag. 2022.
  37. Alaloul, W.S.; Alzubi, K.M.; Malkawi, A.B.; Al Salaheen, M.; Musarat, M.A. Productivity monitoring in building construction projects: A systematic review. Eng. Constr. Arch. Manag. 2021, 29, 2760–2785.
  38. Hamza, M.; Shahid, S.; Bin Hainin, M.R.; Nashwan, M.S. Construction labour productivity: Review of factors identified. Int. J. Constr. Manag. 2019, 22, 413–425.
  39. Chia, F.C.; Skitmore, M.; Runeson, G.; Bridge, A.; Skitmore, R. Economic development and construction productivity in Malaysia. Constr. Manag. Econ. 2014, 32, 874–887.
  40. Van Tam, N.; Toan, N.Q.; Hai, D.T.; Quy, N.L.D. Critical factors affecting construction labor productivity: A comparison between perceptions of project managers and contractors. Cogent Bus. Manag. 2021, 8, 1863303.
  41. Gupta, M.; Hasan, A.; Jain, A.K.; Jha, K.N. Site Amenities and Workers’ Welfare Factors Affecting Workforce Productivity in Indian Construction Projects. J. Constr. Eng. Manag. 2018, 144, 04018101.
  42. Odesola, I.A.; Idoro, G.I. Influence of Labour-Related Factors on Construction Labour Productivity in the South-South Geo-Political Zone of Nigeria. J. Constr. Dev. Ctries. 2014, 19, 93.
  43. Soltaninejad, M.; Fardhosseini, M.S.; Kim, Y.W. Safety climate and productivity improvement of construction workplaces through the 6S system: Mixed-method analysis of 5S and safety integration. Int. J. Occup. Saf. Ergon. 2022, 28, 1811–1821.
  44. Ghodrati, N.; Yiu, T.W.; Wilkinson, S.; Poshdar, M.; Talebi, S.; Elghaish, F.; Sepasgozar, S.M.E. Unintended Consequences of Productivity Improvement Strategies on Safety Behaviour of Construction Labourers; A Step toward the Integration of Safety and Productivity. Buildings 2022, 12, 317.
  45. Gurmu, A.T. Hybrid Model for Assessing the Influence of Safety Management Practices on Labor Productivity in Multistory Building Projects. J. Constr. Eng. Manag. 2021, 147, 04021139.
  46. Yi, W.; Chan, A.P.C. Effects of Heat Stress on Construction Labor Productivity in Hong Kong: A Case Study of Rebar Workers. Int. J. Environ. Res. Public Health 2017, 14, 1055.
  47. Jebelli, H.; Choi, B.; Lee, S. Application of Wearable Biosensors to Construction Sites. I: Assessing Workers’ Stress. J. Constr. Eng. Manag. 2019, 145, 04019079.
  48. Kim, K.; Cho, Y.; Zhang, S. Integrating work sequences and temporary structures into safety planning: Automated scaffolding-related safety hazard identification and prevention in BIM. Autom. Constr. 2016, 70, 128–142.
  49. Hashiguchi, N.; Yeongjoo, L.; Sya, C.; Kuroishi, S.; Miyazaki, Y.; Kitahara, S.; Kobayashi, T.; Tateyama, K.; Kodama, K. Real-time Judgment of Workload using Heart Rate and Physical Activity. In Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020), Kitakyushu, Japan, 27–28 October 2020; pp. 849–856.
  50. Gatti, U.C.; Migliaccio, G.C.; Bogus, S.M.; Schneider, S. An exploratory study of the relationship between construction workforce physical strain and task level productivity. Constr. Manag. Econ. 2014, 32, 548–564.
  51. Al-Kofahi, Z.G.; Mahdavian, A.; Oloufa, A. System dynamics modeling approach to quantify change orders impact on labor productivity 1: Principles and model development comparative study. Int. J. Constr. Manag. 2022, 22, 1355–1366.
  52. Cheng, M.-Y.; Wibowo, D.K.; Prayogo, D.; Roy, A.F.V. Predicting Productivity Loss Caused by Change Orders Using the Evolutionary Fuzzy Support Vector Machine Inference Model. J. Civ. Eng. Manag. 2015, 21, 881–892.
  53. Al-Kofahi, Z.G.; Mahdavian, A.; Oloufa, A. A dynamic modelling of labor productivity impacts arising from change orders in road projects. Can. J. Civ. Eng. 2022, 49, 159–170.
  54. Salem, O.; Solomon, J.; Genaidy, A.; Minkarah, I. Lean Construction: From Theory to Implementation. J. Manag. Eng. 2006, 22, 168–175.
  55. Thomas, H.R. Labor Productivity and Work Sampling: The Bottom Line. J. Constr. Eng. Manag. 1991, 117, 423–444.
  56. Josephson, P.-E.; Björkman, L. Why do work sampling studies in construction? The case of plumbing work in Scandinavia. Eng. Constr. Arch. Manag. 2013, 20, 589–603.
  57. Joshua, L.; Varghese, K. Classification of Bricklaying Activities in Work Sampling Categories Using Accelerometers. In Construction Research Congress 2012: Construction Challenges in a Flat World; American Society of Civil Engineers: New York, NY, USA, 2012.
  58. Hajikazemi, S.; Andersen, B.; Langlo, J.A. Analyzing electrical installation labor productivity through work sampling. Int. J. Prod. Perform. Manag. 2017, 66, 539–553.
  59. Liu, K.; Golparvar-Fard, M. Crowdsourcing Construction Activity Analysis from Jobsite Video Streams. J. Constr. Eng. Manag. 2015, 141, 04015035.
  60. Gong, Y.; Yang, K.; Seo, J.; Lee, J.G. Wearable acceleration-based action recognition for long-term and continuous activity analysis in construction site. J. Build. Eng. 2022, 52, 104448.
  61. Cheng, T.; Teizer, J.; Migliaccio, G.C.; Gatti, U.C. Automated task-level activity analysis through fusion of real time location sensors and worker’s thoracic posture data. Autom. Constr. 2013, 29, 24–39.
  62. Jiang, L.; Zhong, H.; Chen, J.; Cheng, J.; Chen, S.; Gong, Z.; Lun, Z.; Zhang, J.; Su, Z. Study on the construction workforce management based on lean construction in the context of COVID-19. Eng. Constr. Arch. Manag. 2022.
  63. Rui, Y.; Yaik-Wah, L.; Siang, T.C. Construction Project Management Based on Building Information Modeling (BIM). Civ. Eng. Arch. 2021, 9, 2055–2061.
  64. Park, J.W.; Kim, K.; Cho, Y.K. Framework of Automated Construction-Safety Monitoring Using Cloud-Enabled BIM and BLE Mobile Tracking Sensors. J. Constr. Eng. Manag. 2017, 143, 05016019.
  65. Chalhoub, J.; Ayer, S.K. Using Mixed Reality for electrical construction design communication. Autom. Constr. 2018, 86, 1–10.
  66. Bosché, F.; Abdel-Wahab, M.; Carozza, L. Towards a Mixed Reality System for Construction Trade Training. J. Comput. Civ. Eng. 2016, 30, 04015016.
  67. Yang, J.; Shi, Z.; Wu, Z. Vision-based action recognition of construction workers using dense trajectories. Adv. Eng. Inform. 2016, 30, 327–336.
  68. Han, S.; Achar, M.; Lee, S.; Peña-Mora, F. Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring. Vis. Eng. 2013, 1, 1–13.
  69. Konstantinou, E.; Lasenby, J.; Brilakis, I. Adaptive computer vision-based 2D tracking of workers in complex environments. Autom. Constr. 2019, 103, 168–184.
  70. Chu, W.; Han, S.; Luo, X.; Zhu, Z. Monocular Vision–Based Framework for Biomechanical Analysis or Ergonomic Posture Assessment in Modular Construction. J. Comput. Civ. Eng. 2020, 34, 04020018.
  71. Chu, W.; Han, S.; Luo, X.; Zhu, Z. 3D Human Body Reconstruction for Worker Ergonomic Posture Analysis with Monocular Video Camera. In Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019), Banff, AB, Canada, 21–24 May 2019; pp. 722–729.
  72. Zhao, J.; Pikas, E.; Seppänen, O.; Peltokorpi, A. Using Real-Time Indoor Resource Positioning to Track the Progress of Tasks in Construction Sites. Front. Built Environ. 2021, 7, 661166.
  73. Zhao, J.; Seppänen, O.; Peltokorpi, A.; Badihi, B.; Olivieri, H. Real-time resource tracking for analyzing value-adding time in construction. Autom. Constr. 2019, 104, 52–65.
  74. Joshua, L.; Varghese, K. Accelerometer-Based Activity Recognition in Construction. J. Comput. Civ. Eng. 2011, 25, 370–379.
  75. Hashiguchi, N.; Kodama, K.; Lim, Y.; Che, C.; Kuroishi, S.; Miyazaki, Y.; Kobayashi, T.; Kitahara, S.; Tateyama, K. Practical Judgment of Workload Based on Physical Activity, Work Conditions, and Worker’s Age in Construction Site. Sensors 2020, 20, 3786.
  76. Alzubi, K.M.; Alaloul, W.S.; Malkawi, A.B.; Al Salaheen, M.; Qureshi, A.H.; Musarat, M.A. Automated monitoring technologies and construction productivity enhancement: Building projects case. Ain Shams Eng. J. 2022, 14, 102042.
  77. El-Gohary, K.M.; Aziz, R.F.; Abdel-Khalek, H.A. Engineering Approach Using ANN to Improve and Predict Construction Labor Productivity under Different Influences. J. Constr. Eng. Manag. 2017, 143, 04017045.
  78. Torabi, G.; Hammad, A.; Bouguila, N. Two-Dimensional and Three-Dimensional CNN-Based Simultaneous Detection and Activity Classification of Construction Workers. J. Comput. Civ. Eng. 2022, 36, 04022009.
  79. Bangaru, S.S.; Wang, C.; Aghazadeh, F. Automated and Continuous Fatigue Monitoring in Construction Workers Using Forearm EMG and IMU Wearable Sensors and Recurrent Neural Network. Sensors 2022, 22, 9729.
  80. Ogunseiju, O.R.; Olayiwola, J.; Akanmu, A.A.; Nnaji, C. Recognition of workers’ actions from time-series signal images using deep convolutional neural network. Smart Sustain. Built Environ. 2021, 11, 812–831.
  81. Handa, V.K.; Abdalla, O. Forecasting productivity by work sampling. Constr. Manag. Econ. 1989, 7, 19–28.
  82. Bonham, D.R.; Goodrum, P.M.; Littlejohn, R.; Albattah, M.A. Application of Data Mining Techniques to Quantify the Relative Influence of Design and Installation Characteristics on Labor Productivity. J. Constr. Eng. Manag. 2017, 143, 04017052.
  83. Yeheyis, M.; Reza, B.; Hewage, K.; Ruwanpura, J.Y.; Sadiq, R. Evaluating Motivation of Construction Workers: A Comparison of Fuzzy Rule-Based Model with the Traditional Expectancy Theory. J. Civ. Eng. Manag. 2016, 22, 862–873.