Construction activities have been a primary cause for depleting natural resources and are associated with stern environmental impact. Developing concrete mixture designs that meet project specifications is time-consuming, costly, and requires many trial batches and destructive tests that lead to material wastage. Computational intelligence can offer an eco-friendly alternative with superior accuracy and performance. In this study, coal waste was used as a recycled additive in concrete. The flexural strength of a large number of mixture designs was evaluated to create an experimental database. A hybrid artificial neural network (ANN) coupled with response surface methodology (RSM) was trained and employed to predict the flexural strength of coal waste-treated concrete. In this process, four influential parameters including the cement content, water-to-cement ratio, volume of gravel, and coal waste replacement level were specified as independent input variables. The results show that concrete incorporating 3% recycled coal waste could be a competitive and eco-efficient alternative in construction activities while attaining a superior flexural strength of 6.7 MPa. The RSM-modified ANN achieved superior predictive accuracy with an RMSE of 0.875. Based on the experimental results and model predictions, estimating the flexural strength of concrete incorporating waste coal using the RSM-modified ANN model yielded superior accuracy and can be used in engineering practice to save the effort, cost, and material wastage associated with trial batches and destructive laboratory testing while producing mixtures with enhanced flexural strength.

The main goal of the present study is to investigate the capability of hybridizing the imperialist competitive algorithm (ICA) with an intelligent, robust, and data-driven technique named the light gradient boosting machine (LightGBM) to estimate the compressive strength of geo-polymer concrete (CSGCo). The hyper-parameters of the LightGBM algorithm have been optimized based on ICA and its accuracy improved. The obtained results from the proposed hybrid ICA-LightGBM are compared with the traditional LightGBM model as well as four different topologies of artificial neural networks (ANN) comprising a multi-layer perceptron neural network (MLP), radial basis function (RBF), generalized feed-forward neural network (GFFNN), and Bayesian regularized neural network (BRNN). The results of these models were compared based on three evaluation indices of R², RMSE, and VAF for providing an objective evaluation of the performance and capability of the predictive models. Concerning the outcomes, the ICA-LightGBM with the R² of (0.9871 and 0.9805), RMSE of (0.4703 and 1.3137), and VAF of (98.5773 and 98.0397) for training and testing phases, respectively, was a superior predictor to estimate the CSGCo compared to the LightGBM with the R² of (0.9488 and 0.9478), RMSE of (0.9532 and 2.1631), and VAF of (94.3613 and 94.5173); the MLP with the R² of (0.9067 and 0.8959), RMSE of (1.3093 and 3.3648), and VAF of (88.9888 and 84.9125); the RBF with the R² of (0.8694 and 0.8055), RMSE of (1.4703 and 5.0309), and VAF of (86.3122 and 66.1888); the BRNN with the R² of (0.9212 and 0.9107), RMSE of (1.1510 and 2.6569), and VAF of (91.4168 and 90.5854); and the GFFNN with the R² of (0.9144 and 0.8925), RMSE of (1.1525 and 2.9415), and VAF of (91.4092 and 88.9088). Hence, the proposed ICA-LightGBM algorithm can be efficiently used in anticipating the CSGCo.

This paper presents a comprehensive evaluation on self-compacting (SC) mortars incorporating 0, 1, 3, and 5% alumina nanoparticles (NA) as well as 0% and 30% rice husk ash (RHA) used as Portland cement replacement. To evaluate the workability, mechanical, and durability performance of SC mortars incorporating NA and RHA, the fresh properties (slump flow diameter and V-funnel flow time), hardened properties (compressive strength, flexural strength, and ultrasonic pulse velocity), and durability properties (water absorption, rapid chloride permeability, and electrical resistivity) were determined. The results indicated that the addition of NA and RHA has negligible effect on the workability and water absorption rate of the SC mortars. However, significant compressive and flexural strength development was observed in the SC mortars treated with NA or the combination of NA and RHA. The introduction of RHA and NA also reduced the rapid chloride permeability and enhanced the electrical resistivity of the SC mortars significantly. It is concluded that the coexistence of 30% RHA and 3% NA as cement replacement in SC mortars can provide the best mechanical and durability performance.

The study researched the effectiveness of four polymer admixtures (3%, 5%, 10%, and 15% of water weight) on the fresh and hardened properties of self-compacting concrete (SCC) cast using recycled and natural aggregates. Results show that polymer additives had positive effects on the fresh properties of SCC using recycled aggregates. Incorporating polymer additives increased the filling ability of concrete by more than four times. All polymer modified SCCs had a 100% passing ability compared to the 80% passing ability of the control samples. The compressive strength of materials at similar polymer ratios decreased by about 50% when natural aggregates were replaced with recycled aggregates. The flexural strength of SCC including recycled aggregates with 15% polymer was maintained compared to the control SCC including natural aggregates. The addition of 15% polymer to recycled aggregates concrete could improve workability and maintain flexural strength.

Lime-cement concrete (LCC) is a non-structural concrete in which lime and cement are used as the main binders. However, although LCC has many applications in reducing the settlement of foundations and providing a support layer for shallow foundations, little research has been conducted to evaluate its behaviour in various moisture conditions. Previous researchers have studied the feasibility of using waste tires in conventional concrete to alleviate their negative environmental impacts. However, in field projects, rubber has not been widely used because its application leads to the strength reduction of concrete. In the case of LCC, attaining high strengths is not required and thus application of waste tire particles sounds reasonable. This research evaluated the impact of various rubber powder contents on the fresh, geotechnical and durability properties of LCC at different saturation degrees induced by the capillary action and groundwater level increment, which has not been studied before. The results of more than 320 tests showed that the application of tire powder increases workability and decreases the water absorption of LCC. Moreover, all 60-day cured specimens exposed to 100% saturation degree experienced a strength reduction of less than 10% by using rubber powder contents varying from 0 to 20%. Moreover, increasing the saturation degree from 0 to 100% decreased the average compressive strength by 13.5 and 22% for 60-day cured samples of two different mix designs. The results of this research confirm that LCC containing up to 10% rubber powder could be promisingly used underneath or close to the groundwater table without its strength and geotechnical properties being jeopardized due to rubber employment and/or exposure to ground moisture.

In this research, methods of increasing the corrosion resistance of reinforced concrete were experimentally investigated. The study used silica fume and fly ash at optimized percentages of 10 and 25% by cement weight, polypropylene fibers at a ratio of 2.5% by volume of concrete, and a commercial corrosion inhibitor, 2-dimethylaminoethanol (Ferrogard 901), at 3% by cement weight. The corrosion resistance of three types of reinforcements, mild steel (STt37), AISI 304 stainless steel, and AISI 316 stainless steel, was investigated. The effects of various coatings, including hot-dip galvanizing, alkyd-based primer, zinc-rich epoxy primer, alkyd top coating, polyamide epoxy top coating, polyamide epoxy primer, polyurethane coatings, a double layer of alkyd primer and alkyd top coating, and a double layer of epoxy primer and alkyd top coating, were evaluated on the reinforcement surface. The corrosion rate of the reinforced concrete was determined through results of accelerated corrosion and pullout tests of steel-concrete bond joints and stereographic microscope images. The samples containing pozzolanic materials, the corrosion inhibitor, and a combination of the two showed significant improvement in corrosion resistance by 7.0, 11.4, and 11.9 times, respectively, compared to the control samples. The corrosion rate of mild steel, AISI 304, and AISI 316 decreased by 1.4, 2.4, and 2.9 times, respectively, compared to the control sample; however, the presence of polypropylene fibers reduced the corrosion resistance by 2.4 times compared to the control.