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Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders.

A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index.

The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”.

The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

This study assessed the key construction technology trends and tracked the impact of these developments on labor influencing factors in an effort to alleviate the limitations posed by skilled labor scarcity in offsite construction (OSC).

The study followed systematic literature review to acquisition initial data sets. The data retrieved form identified databases was then subjected to scientometric analysis. In next stage, disruptive factors were identified to produce a conceptual framework followed by its practical application for an OSC firm.

This study identified seven construction technologies with a potential to disrupt OSC in future. The study also developed a conceptual framework as a benchmark to help establish further frameworks for policy interns in OSC.

The review conducted only takes into account the literature from 2009 to March 2021. Any prior to the date and unpublished studies were not included in study.

A proper utilization of conceptual framework can contribute to technological adoption significantly lowering the need of labor in OSC industry. Moreover, the established discussion on understanding the purpose of deploying latest technology may reduce additional costs, management, time and allocation of labor resources.

This study provides a mechanism to intern OSC policymakers to impartially incorporate the latest technology in OSC.

This study fulfilled an identified need to study latest technological trends in OSC by utilizing bibliometric tools with SLR. The disruptive categories and factors highlighted by the study were not identified in a systematic manner in previous studies, which could further enhance the implementation of latest construction technology in OSC.

Labours are one of the key resources of construction projects having a significant impact on economic development. With time, the construction industry is getting a boom due to numerous construction projects in which labours’ contribution is significant. Project betterment is associated with the project productivity which relates to the labours. Wages play a vital role in retaining labours in the construction industry.

This study focuses on investigating the increasing and decreasing behaviour of labour wages in the construction industry and observing the impact of the inflation rate in deviating labour wages. Initially, the percentage deviation was calculated to observe the changing behaviour of the data and then the Spearman correlation test was used to find the relationship between the inflation rate and the labour wages.

It is evident that even with a good economy and a stable construction sector, still, the labour wages faced a decline over time, which is a matter of concern. Also, based on the correlation coefficient, it was revealed that several labour wages categories are moderately and strongly correlated with the inflation rate and can impact project cost if the inflation rate is not considered while finalizing the budget. Besides that, the changing behaviour of labour wages due to the inflation rate impacts significantly on gross domestic product (GDP); therefore, it requires vital attention.

Changes in labour wages are not considered in budget estimation which drag the project towards cost overrun. This study brings the attention of the stakeholders on the issue with experimental justification. Also, how the inflation rate is affecting the labour wages has also been addressed.

Roller compacted concrete (RCC) pavement is used in areas subjected to heavy impact loads; therefore, higher impact resistance is a desirable property of consideration. This study aims to investigate the effect of partial replacement of fine aggregate with crumb rubber (CR) and the addition of nanosilica (NS) by weight of cementitious materials on the impact resistance of roller compacted rubbercrete (RCR).

Four replacement levels of CR (0, 10, 20 and 30 per cent) and four addition levels of NS (0, 1, 2 and 3 per cent) were considered. The impact resistance test was carried out using the drop weight test recommended by ACI 544.

The results showed that the impact resistance of RCR increases with an increase in both CR and NS addition, though for CR above 20 per cent, sudden drop in impact resistance was observed. However, NS reduces the ductility of RCR by decreasing the post-cracking impact resistance. Response surface methodology was used to develop models for predicting the impact resistance of RCR, and the developed models showed a high degree of correlation. As a result of wide variations in the impact drop test data, two-parameter Weibull distribution function was used for the data analysis, and it was found that the probabilistic distributions of the first crack and ultimate failure impact resistance follow the two-parameter Weibull distribution function.

In this work, the effect of partial replacement of fine aggregate with CR and the addition of NS by weight of cementitious materials on the impact resistance of RCC pavement has been investigated. CR has been used to increase the impact resistance of RCC Pavement.

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

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

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

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