Geopolymers are novel binding materials produced by the alkaline activation of rich aluminosilicate materials. Geopolymer binders are considered as green building materials that have increasing potential to replace the ordinary Portland cement in the concrete industry. This study investigated the alkali solution effects on the physical and mechanical properties of the high calcium fly ash based geopolymers. The parameters involved in this study were the NaOH solution molarity and the Na2SiO3/NaOH ratio. Three NaOH concentrations (8, 10, and 12 molars) were investigated. The Na2SiO3/NaOH ratio was varied between 1 and 2.5. The results showed that the studied parameters significantly affected the properties of the produced geopolymer mortars. The workability and setting time were found to decrease by increasing the NaOH concentration or by increasing the Na2SiO3/NaOH ratio. The influence of the NaOH concentration was higher on the workability and setting time while the Na2SiO3/NaOH ratio highly affected the compressive strength. The initial setting time was in the range of 45-105minutes while the final setting occurred quickly after that within 70-115minutes. Oven curing method resulted in high strength at early ages where all of the mixes were able to achieve more than 75% of the 28 days strength within the first 3 days. The 28-days compressive strength ranged between 60-85MPa, which promotes the use of the high calcium fly ash for the production of early high strength geopolymer concrete

This research was conducted to compare class C and class F fly ash as cementitious materials. The fly ash was supplied by local power plants in Malaysia. The chemical composition analysis showed that class F fly ash has more Si and Al content compared to class C fly ash which affects the amount of the produced gel during the pozzolanic reactivity or/and geopolymerization. The chemical bonding analysis showed that class F fly ash has more bond structure (C–S–H, C–A–H, –Si–O–Al–, and –Si–O–Si–) than class C fly ash. Finally, the microstructure images indicated the similarity of the fly ash particles shape on both classes. Furthermore, the microstructure images showed a correlation between the chemical composition and the bond structure in terms of the high element content (Si and Al) forming the gel and the bond structure in the cementitious process during pozzolanic reaction or/and geopolymerization

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.

There is no doubt that the tremendous development of information technology was one of the driving factors behind the great growth of surveying and geodesy science. This has spawned modern geospatial techniques for data capturing, acquisition, and visualization tools. Digital elevation model (DEM) is the 3D depiction of continuous elevation data over the Earth’s surface that is produced through many procedures such as remote sensing, photogrammetry, and land surveying. DEMs are essential for various surveying and civil engineering applications to generate topographic maps for construction projects at a scale that varies from 1:500 to 1:2,000. GIS offers a powerful tool to create a DEM with high resolution from accurate land survey measurements using interpolation methods. The aim of this research is to investigate the impact of estimation techniques on generating a reliable and accurate DEM suitable for large-scale mapping. As a part of this study, the deterministic interpolation algorithms such as ANUDEM (Topo to Raster), inverse distance weighted (IDW), and triangulated irregular network (TIN) were tested using the ArcGIS desktop for elevation data obtained from real total station readings, with different landforms to show the effect of terrain roughness, data density, and interpolation process on DEM accuracy. Furthermore, comparison and validation of each interpolator were carried out through the cross-validation method and numerous graphical representations of the DEM. Finally, the results of the investigations showed that ANUDEM and TIN models are similar and significantly better than those attained from IDW.