Flexure Performance of Ferrocement Panels Using SBR Latex and Polypropylene Fibers with PVC and Iron Welded Meshes.

Ferrocement panels are thin-section panels that are widely used in lightweight construction. Due to lesser flexural stiffness, they are susceptible to surface cracking. Water may penetrate through these cracks and may cause corrosion of conventional thin steel wire mesh. This corrosion is one of the major factors which affect the load-carrying and durability of ferrocement panels. There is a need to improve the mechanical performance of ferrocement panels either through using some non-corrodible reinforcing mesh or through improving the cracking behavior of the mortar mix. In the present experimental work, PVC plastic wire mesh is employed to address this problem. SBR latex and polypropylene (PP) fibers are also utilized as admixtures to control the micro-cracking and improve the energy absorption capacity. The main idea is to improve the structural performance of ferrocement panels that may be utilized in lightweight, low-cost house construction and sustainable construction. The ultimate flexure strength of ferrocement panels employing PVC plastic wire mesh, welded iron mesh, SBR latex, and PP fibers is the subject of the research. Test variables are the type of mesh layer, the dosage of PP fiber, and SBR latex. Experimental tests are conducted on 16 simply supported panels of size 1000 × 450 mm and subjected to four-point bending test. Results indicate that the addition of latex and PP fibers only controls the initial stiffness and does not have any significant effect on ultimate load. Due to the increased bonding between cement paste and fine aggregates, the addition of SBR latex improves the flexural strength by 12.59% and 11.01% for iron mesh (SI) and PVC plastic mesh (SP), respectively. The results also indicate an improvement in the flexure toughness of specimens with PVC mesh as compared to specimens with iron welded mesh; however, a smaller peak load is observed (i.e., 12.21% for control specimens) compared with the specimen with welded iron mesh. The failure patterns of the specimens with PVC plastic mesh exhibit a smeared cracking pattern that shows that they are more ductile compared to samples with iron mesh.

Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches.

The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the nth estimators to achieve a robust R2. Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R2 of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.

A Study on Sustainable Concrete with Partial Substitution of Cement with Red Mud: A Review.

Every year, millions of tons of red mud (RDM) are created across the globe. Its storage is a major environmental issue due to its high basicity and tendency for leaching. This material is often kept in dams, necessitating previous attention to the disposal location, as well as monitoring and maintenance during its useful life. As a result, it is critical to develop an industrial solution capable of consuming large quantities of this substance. Many academics have worked for decades to create different cost-effective methods for using RMD. One of the most cost-effective methods is to use RMD in cement manufacture, which is also an effective approach for large-scale RMD recycling. This article gives an overview of the use of RMD in concrete manufacturing. Other researchers' backgrounds were considered and examined based on fresh characteristics, mechanical properties, durability, microstructure analysis, and environmental impact analysis. The results show that RMD enhanced the mechanical properties and durability of concrete while reducing its fluidity. Furthermore, by integrating 25% of RDM, the environmental consequences of cumulative energy demand (CED), global warming potential (GWP), and major criteria air pollutants (CO, NOX, Pb, and SO2) were minimized. In addition, the review assesses future researcher guidelines for concrete with RDM to improve performance.

Investigation of CFRP Reinforcement Ratio on the Flexural Capacity and Failure Mode of Plain Concrete Prisms.

The utilization of carbon-fiber-reinforced polymer (CFRP) composites as strengthening materials for structural components has become quite famous over the last couple of decades. The present experimental study was carried out to examine the effect of varied widths of externally bonded CFRP on the debonding strain of CFRP and the failure mode of plain concrete beams. Twelve plain concrete prims measuring 100 mm × 100 mm × 500 mm were cast and tested under identical loading conditions. The twelve specimens include two control prisms, i.e., without CFRP strips, and the remaining ten prisms were reinforced with CFRP strips with widths of 10 mm, 20 mm, 30 mm, 40 mm, and 50 mm, respectively, i.e., two prisms in each group. Four-point loading flexural testing was carried out, and the resulting data are presented in the form of peak load vs. midpoint displacement, load vs. concrete strain, and load vs. CFRP strain. The peak load was directly recorded from the testing machine, while the midpoint deflection was recorded through the linear variable differential transducer (LVDT) installed at the midpoint. To measure the strain, two separate strain gauges were installed at the bottom of each concrete prism, i.e., one on the concrete surface and the other on the surface of the CFRP strip. The results of this study indicate that the debonding strain is a function of CFRP strip width and that the failure patterns of beams are significantly affected by the CFRP reinforcement ratio.

PCA-Based Hybrid Intelligence Models for Estimating the Ultimate Bearing Capacity of Axially Loaded Concrete-Filled Steel Tubes.

In order to forecast the axial load-carrying capacity of concrete-filled steel tubular (CFST) columns using principal component analysis (PCA), this work compares hybrid models of artificial neural networks (ANNs) and meta-heuristic optimization algorithms (MOAs). In order to create hybrid ANN models, a dataset of 149 experimental tests was initially gathered from the accessible literature. Eight PCA-based hybrid ANNs were created using eight MOAs, including artificial bee colony, ant lion optimization, biogeography-based optimization, differential evolution, genetic algorithm, grey wolf optimizer, moth flame optimization and particle swarm optimization. The created ANNs' performance was then assessed. With R2 ranges between 0.7094 and 0.9667 in the training phase and between 0.6883 and 0.9634 in the testing phase, we discovered that the accuracy of the built hybrid models was good. Based on the outcomes of the experiments, the generated ANN-GWO (hybrid model of ANN and grey wolf optimizer) produced the most accurate predictions in the training and testing phases, respectively, with R2 = 0.9667 and 0.9634. The created ANN-GWO may be utilised as a substitute tool to estimate the load-carrying capacity of CFST columns in civil engineering projects according to the experimental findings.

Concrete Made with Dune Sand: Overview of Fresh, Mechanical and Durability Properties.

According to the authors' best information, the majority of research focuses on other waste materials, such as recycling industrial waste (glass, silica fume, marble and waste foundry sand), etc. However, some researchers suggest dune sand as an alternative material for concrete production, but knowledge is still scarce. Therefore, a comprehensive review is required on dune sand to evaluate its current progress as well as its effects on the strength and durability properties of concrete. The review presents detailed literature on dune sand in concrete. The important characteristics of concrete such as slump, compressive, flexural, cracking behaviors, density, water absorption and sulfate resistance were considered for analysis. Results indicate that dune sand can be used in concrete up to 40% without any negative effect on strength and durability. The negative impact of dune sand on strength and durability was due to poor grading and fineness, which restricts the complete (100%) substation of dune sand. Furthermore, a decrease in flowability was observed. Finally, the review highlights the research gap for future studies.

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches.

The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na2SiO3/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study's aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient (R2). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts.

Effect of Fineness of Basaltic Volcanic Ash on Pozzolanic Reactivity, ASR Expansion and Drying Shrinkage of Blended Cement Mortars.

This study focuses on evaluating the effect of the fineness of basaltic volcanic ash (VA) on the engineering properties of cement pozzolan mixtures. In this study, VA of two different fineness, i.e., VA fine (VF) and VA ultra-fine (VUF) and commercially available fly ash (FA) was used to partially replace cement. Including a control and a hybrid mix (10% each of VUF and FA), eleven mortar mixes were prepared with various percentages of VA and FA (10%, 20% and 30%) to partially replace cement. First, material characterization was performed by using X-ray florescence (XRF), X-ray powder diffraction (XRD), particle size analysis, and a modified Chappelle test. Then, the compressive strength development, alkali silica reactivity (ASR), and drying shrinkage of all mortar mixes were investigated. Finally, XRD analysis on paste samples of all mixes was performed to assess their pozzolanic reactivity at ages of 7 and 91 days. The results showed increased Chappelle reactivity values with an increase in the fineness of the VA. Mortars containing high percentages of VUF (20% and 30%) showed almost equal compressive strength compared to corresponding FA mortars at all ages, however, the hybrid mix (10% VUF + 10% FA) exhibited higher strength than that of the reference mix (100% cement), particularly, at 91 days. At low percentages (10%), ASR expansion in both VF and VUF mortars was higher compared to the corresponding FA mortar and the opposite behavior was observed at high percentages (20% and 30%). Among all the mixes including the control, mortar with VUF was found to be most effective in controlling drying shrinkages at all ages. The rate of consumption of calcium hydroxide (Ca(OH)2) for pastes containing VUF and FA was almost the same, while VF showed low Ca(OH)2 intensity. These results indicate that an increase in the fineness of VA significantly improvs performance, and therefore, it could be a feasible substitute for commercial admixtures in cement composites.

Flexural and Shear Strain Characteristics of Carbon Fiber Reinforced Polymer Composite Adhered to a Concrete Surface.

The use of Fiber Reinforced Polymer (FRP) composites for strengthening concrete structures has gained a lot of popularity in the past couple of decades. The major issue in the retrofitting of concrete structures with FRP is the accurate evaluation of flexural and shear strains of polymer composites at the bonding interface of epoxy and concrete. To address it, a comprehensive experimental study was planned and carbon fiber reinforced polymer (CFRP) composite was applied on the concrete surface with the help of adhesives. CFRP was used as an external mounted flexural and shear reinforcement to strengthen the beams. Flexural load tests were performed on a group of eight reinforced concrete beams. These beams were strengthened in flexural and shear by different reinforcement ratios of CFRP. The strain gauges were applied on the surface of concrete and CFRP strips to assess the strain of both CFRP and concrete under flexural and shear stresses. The resulting test data is presented in the form of load⁻deformation and strain values. It was found that the values of strains transferred to the FRP through the concrete are highly dependent on the surface tensile properties of concrete and debonding strength of the adhesive. The test results clearly indicated that the strength increment in flexural members is highly dependent on strain values of the CFRP.