One of the main drawbacks of utilizing mass concrete is the high amount of heat produced during the hydration of cementitious materials. Low-heat high-performance concrete (LHHPC) is a special type of concrete with low Portland cement content and low heat of hydration. The main aim of this research is to experimentally explore the potential use of blast furnace cement (CEM III) and fly ash (FA) in LHHPC. CEM III is a type of cement with low heat of hydration. FA was used at various dosages, namely 10%, 20%, 30%, and 40%, as a partial replacement of CEM III for producing more sustainable LHHPC. The mechanical and micro-structural characteristics of the LHHPC mixes were investigated. In addition, the concrete thermal conductivity and heat of hydration were predicted and compared using ANSYS finite element software. The experimental results showed that 40% FA as a CEM III partial replacement decreased the heat of hydration in LHHPC by 38.7%. In addition, the produced LHHPC showed low thermal conductivity, which indicates a decrease in early-age cracks. The produced LHHPC showed a constant compressive strength of 90 days compared with the corresponding 28-day compressive strength. The experimental results were supported by scanning electron microscope (SEM) analysis and the numerical analysis for the LHHPC. The 3D finite element model provided accurate predictions for temperature distribution. The results of this research indicated that FA and CEM III can successfully produce LHHPC with adequate strength and low heat of hydration.

An assessment of construction activities related to pollution needs to be conducted during the planning of a given project. Such an assessment is essential to ensure that the resulting pollution does not surpass the environmental threshold limits. This research provides an optimized pollution-based scheduling model in construction projects by applying Genetic Algorithms (GAs). The suggested approach figures out the pollution produced by gasses, noise, and dust for each activity in the project. Then, the whole project’s duration is minimized by optimizing the project schedule using GAs while keeping the different pollutants under threshold limits. In the developed model, each pollutant is handled as a dummy resource and is incorporated into the schedule of construction projects. When the emitted pollutants surpass the allowable limits, as per the given regulations, GAs re-schedule the project tasks so that the pollution levels are reduced and redistributed. The proposed framework is presented as being practically applicable through an actual case study. The results show that the proposed GA model improves the pollution leveling process more efficiently than the standard resource leveling technique in Microsoft Project, producing fewer pollution histogram moments of the X and Y axes with 9.4% and 2.2%, respectively. Sensitivity analysis reveals that the best solutions for this case study are obtained when population size, offspring generation, crossover rate, and mutation rate equal 100, 50, 0.95, and 0.05, respectively. The model can aid in reducing construction projects’ environmental impact during the project planning and construction stages, which benefits decision-makers and project planners.