Purpose: The unique nature of the construction sector makes it fall behind other sectors in terms of productivity. Monitoring construction productivity is crucial for the construction project's success. Current practices for construction productivity monitoring are time-consuming, manned and error prone. Although previous studies have been implemented toward reducing these limitations, a gap still exists in the automated monitoring of construction productivity.Design/methodology/approach: This study aims to investigate and assess the different techniques used for monitoring productivity in building construction projects. Therefore, a mixed review methodology (bibliometric analysis and systematic review) was adopted. All the related publications were collected from different databases, which were further screened to get the most relevant based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria.Findings: A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. Also, it was observed that current studies did not cover all the complex construction job sites and they were applied based on a small sample of construction workers and machines separately.Originality/value: This review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques.

The construction sector plays a significant role in contributing to uplifts in economic stability by generating employment and providing standardized social development. Economic sustainability in the construction sector has been less addressed despite its wide applicability in the economy. This study aimed to perform a comparative analysis to determine the application of a circular economy in the construction sector toward economic sustainability, along with its long-term forecasting. A time series analysis was used on the construction sector of the United States of America (USA), China, and the United Kingdom (UK) from 1970 to 2020, by taking into account individual effects to propose a framework with global validity. Statistical analysis was performed to analyze the dependence of the construction sector and determine its short- and long-term contributions. The results revealed that the construction sectors in these countries tend to bounce back to equilibrium in the case of short-term effects; however, the construction sector behaves differently with respect to each sector after experiencing long-term effects. The results show that the explanatory power of the forecasting model (R²) was found to be 0.997, 0.992, and 0.996 for the USA, China, and the UK. Based on the concept of the circular economy, it was concluded that the USA will become a leader in attaining sustainability in construction owing to its ability to recover quickly from shocks, and that the USA will become the largest construction sector in terms of GDP, with a USD 0.3 trillion higher GDP than that of the Chinese sector. Meanwhile, there will be no significant change in the construction GDP of the UK up to the end of 2050. Moreover, the speeds of the construction sector toward equilibrium in the long run in the USA, China, and the UK, and regaining of their original positions, is 0.267%, 1.04%, and 0.41% of their original positions, respectively. This study has a significance in acting as a guideline for introducing economic and environmental sustainability in construction policies, because of the potential of the construction sectors to recover from possible recessions in their respective countries.

The construction industry is hailed as the driver for the economic development of every country to a great extent due to its dynamic nature, yet it needs digital transformation to align with the fast-paced digital world economy. Hence, to evaluate the potential for the digital transformation of various segments of the construction industry, a systematic review was conducted in this study. English research articles were extracted from the Scopus database by using keywords of “Digital Transformation” AND “Construction Industry” correlated to the engineering field. After a detailed screening process as per the PRISMA statement, only 26 articles were selected for further assessment from a total of 67 extracted articles. The extracted articles were focusing on digital technologies e.g., Building Information Modeling (BIM), Smart Devices, Internet of things (IoT), Drones, Project studio (PS), and digital twins and their implication in the digital transformation of the construction industry. It was evident that digital transformation enhanced the productivity and efficiency of construction projects with better control over cost, time, and risk throughout the project life cycle. Therefore, it is recommended to establish top management interest for digital partnering with trusted organizations enabling the realization of digital technologies in the construction industry.

Fly oil shale ash (FOSA) is a waste material known for its pozzolanic activity. This study intends to investigate the optimum thermal treatment conditions to use FOSA efficiently as a cement replacement material. FOSA samples were burned in an electric oven for 2, 4, and 6 h at temperatures ranging from 550 °C to 1000 °C with 150 °C intervals. A total of 333 specimens out of 37 different mixes were prepared and tested with cement replacement ratios between 10% and 30%. The investigated properties included the mineralogical characteristics, chemical elemental analysis, compressive strength, and strength activity index for mortar samples. The findings show that the content of SiO₂ + Al₂O₃ + Fe₂O₃ was less than 70% in all samples. The strength activity index of the raw FOSA at 56 days exceeded 75%. Among all specimens, the calcined samples for 2 h demonstrated the highest pozzolanic activity and compressive strength with a 75% strength activity index. The model developed by RSM is suitable for the interpretation of FOSA in the cementitious matrix with high degrees of correlation above 85%. The optimal compressive strength was achieved at a 30% replacement level, a temperature of 700 °C for 2 h, and after 56 days of curing.

The Internet of Things (IoT) is pervasive across nearly all industries, and construction is no exception. The use of IoT in construction enhances the management, development, and monitoring of infrastructure and structures. It also reduces the drawbacks of traditional approaches, such as their proneness to errors, time and expense constraints, and risks. The study's goal is to look at different areas where IoT can be used in the building industry. The study examines numerous elements of IoT adoption in the construction industry, including safety, decision-making, monitoring, productivity, and performance applications, as well as the constraints of the technologies utilized. This aids in determining the areas in which the Internet of Things can be used to improve the building business. The use of IoT enhances the efficiency with which construction projects are completed, as well as how the use of new technologies for monitoring will result in more effective monitoring. As a result, data regarding the building site may be collected efficiently, correctly, and in real time, allowing management to make better decisions.

Comparing to other sectors, the construction sector suffers from low productivity, and it is not improving over time due to the unique nature of construction projects. It is believed that construction productivity cannot be improved without efficient monitoring and measuring, and this is crucial for project success. There are many limitations for the traditional construction productivity monitoring practices like time and cost consuming and error-prone. Although a lot of studies have been implemented to eliminate these limitations, a gap still exists in the automated monitoring of construction productivity. This study proposes an automated monitoring model for indoor productivity recognition in construction projects. The model will provide an instant evaluation of the project productivity which will enhance the optimum utilization of the project resources. The proposed model will be developed by first generating a baseline for the activities state which will be represented as baseline state model. Then the as-built model will be generated. Preliminary experimentation was performed on selected images where the number of tiles and bricks was obtained. The experimentation was performed using Open-Source Computer Vision Library (OpenCV). Preliminary results depict that by using the proposed model the automated monitoring of productivity is achievable. Although, there is a need of dedicated efforts for improvement and development of technique for more effective and efficient results.