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Despite global advancements, the Indian construction industry lags in adopting technologies like robots, artificial intelligence, drones, unmanned ground vehicles and Internet of Things sensors due to various barriers. This reliance on traditional practices leads to persistent issues such as labour shortages, low productivity, safety risks, cost overruns and delays. Identifying and addressing these barriers is crucial for adopting advanced technologies. Hence, this study aims to identify and assess the influential barriers to construction automation and robotics (AaR) in India using a systematic approach.

An extensive literature review identified key barriers, including technological, financial, regulatory, environmental and organisational. A questionnaire survey was conducted among industry professionals, and the Fuzzy Decision-Making Trial and Evaluation Laboratory technique was used to evaluate the interrelationships and relative significance of these barriers.

The results of the study reveal the most critical cause barriers as “high initial cost”, “rigidity in organisational processes and procedures”, “interoperability”, “lack of competency” and “lack of standardisation”. These barriers subsequently influence the top two effect barriers such as “market uncertainty” and “ethical concerns”.

This research offers a systematic and quantitative assessment, enabling stakeholders to make informed decisions and develop strategies to overcome barriers to AaR adoption, unlocking the transformative potential of AaR in India’s construction sector.

Despite potential benefits, the adoption of AaR in Indian construction remains limited due to various barriers. This study provides novel insights with the first comprehensive assessment of these barriers and their interrelationships.

Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future research themes. For a long time, CPM has been significantly researched with increasing enthusiasm. Although a few review studies have been carried out, there is non-existence of a quantitative review study that can deliver a holistic picture of CPM.

The science mapping-based scientometric analysis was systematically processed with 1,835 CPM-related journal articles retrieved from Scopus. The co-authorship analysis and direct citation analysis were carried out to identify the most influential researchers, countries and publishers of the knowledge domain. The co-occurrence analysis of keyword was assessed to reveal the most dominating research topics and research trend with the visual representation of the considered research domain.

This study reveals seven clusters of main research topics from the keyword co-occurrence analysis. The evolution of research confirms that CPM-related research studies were mainly focused on fundamental and traditional CPM research topics before 2007. The period between 2007 and 2020 has seen a shift of research efforts towards digitalization and automation. The result suggests Building Information Modelling (BIM) as the most common, growing and influential research topic in the CPM research domain. It has been used in combination with different data acquisition technologies (e.g. photogrammetry, videogrammetry, laser scanning, Internet of Things (IoT) sensors) and data analytics approaches (e.g. machine learning and computer vision).

This study provides the horizon of potential research in the research domain of CPM to researchers, policymakers and practitioners by availing of main research themes, current knowledge gaps and future research directions.

This paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.

The built-transfer and operate (BOT) toll model has been a common approach for highway construction in India. Due to large amount of investment, many stakeholders and long concession period, Indian BOT toll roads become susceptible to different risks. Risk assessment is one of the essential and difficult steps of risk management that enables professionals to quantify and analyze the risks that may hamper the BOT toll project performance in terms of cost, quality, safety and time. The purpose of this study is to identify and assess the risk factors by the fuzzy probabilistic model.

The comprehensive literature review has been carried out for identification of Indian BOT toll roads projects' risk factors. A structured questionnaire was prepared which was then filled by contractors, government officers, academicians, project managers and consultants. For risk assessment, a systematic quantitative-based fuzzy probabilistic model is proposed with the help of lab-view, as a risk assessment technique to simulate the impreciseness of human judgment and to improve the assessment accuracy.

The risk assessment is one of the difficult tasks because BOT toll roads have complex structure. In this study, total 71 risks have been identified and categorized under 14 risk factors on a basis of case studies of Indian toll roads and literature review. All risks have been assessed by the fuzzy probabilistic model by lab-view. The land acquisition is the most crucial risk of BOT toll roads project which is subsequently followed by construction time and cost over-run. Also, Indian BOT toll roads are facing the traffic shortfalls critically, which became a reason toward declination trend for investment in BOT toll projects by the private players. Other risks like political risks, contractual and social risk badly are affecting the project performance. Early risk identification and assessment can be beneficial for the project, which is required to prepare the risk response strategy before the project commencement.

This study throws light on comprehensive risk assessment and framework modeling of the risk assessment for the BOT toll roads project in India. This comprehensive risk assessor model can be used for BOT toll roads in Indian scenario for prioritization of the critical risk. It is important for the public and private sectors to establish an effective risk assessor model for public–private partnership (PPP) projects to achieve win-win condition for both.

Notwithstanding the Geographical Information System (GIS) being a fast-emerging green area of a digital revolution, the available studies focus on different subject areas of application in the construction industry, with no study that clarifies its knowledge strands. Hence, this systematic review analyses GIS core area of application, its system integration patterns, challenges and future directions in the construction industry.

A systematic review approach was employed, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. A total of 60 articles published between 2011 and 2022 were identified, thoroughly reviewed and analysed using thematic analysis.

The analysis revealed spatial planning and design, construction-task tracking, defect detection and safety monitoring as its four main application-based areas. The findings showed that the adoption of GIS technology is rapidly expanding and being utilised more in building projects to visual-track construction activities. The review discovered an integrated pattern involving data flow from a device and window-form application to GIS, the pathways to data exchange between platforms to platforms, where ArcGIS is the most used software. Furthermore, the study highlighted the lack of interoperability between heterogeneous systems as the crux impediment to adopting GIS in the built environment.

The research provides a deep insight into possible areas where GIS is adopted in the construction industry, identifying areas of extensive and limited application coverage over a decade. Besides, it demystifies possible pathways for future integration opportunities of GIS with other emerging technologies within the construction industry.

This paper is aimed at analyzing the inter-contextual relationships among the factors that led to inadequate management of electronic and electrical waste (WEEE) during COVID-19 using a subjective perspective.

Grey sets and a Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based approach has been employed to identify the causal association of intertwined WEEE management barriers.

Results reveal the lack of implementation of the legislative framework, extended producer responsibility and lesser corporate initiatives are some of the most challenging WEEE management challenges during the current pandemic.

The findings of the study would enable stakeholders of WEEE management toward building resilient policies and effective implementation plans during as well as post-crisis situations.

COVID-19 led challenges related to healthcare waste have attracted a significant amount of scholarly attention, but there has been lesser attention toward e-waste management challenges during the pandemic. Negligence toward e-waste management can pose threats to the environment as well as human well-being.