PurposeThe 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/approachThis 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.FindingsA 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/valueThis review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques.
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.
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