Assessment of Mechanical Properties and Structural Morphology of Alkali-Activated Mortars with Industrial Waste Materials

Life Cycle Assessment of the Sustainability of Alkali-Activated Binders

Limiting the consumption of nonrenewable resources and minimizing waste production and associated gas emissions are the main priority of the construction sector to achieve a sustainable future. This study investigates the sustainability performance of newly developed binders known as alkali-activated binders (AABs). These AABs work satisfactorily in creating and enhancing the concept of greenhouse construction in accordance with sustainability standards. These novel binders are founded on the notion of utilizing ashes from mining and quarrying wastes as raw materials for hazardous and radioactive waste treatment. The life cycle assessment, which depicts material life from the extraction of raw materials through the destruction stage of the structure, is one of the most essential sustainability factors. A recent use for AAB has been created, such as the use of hybrid cement, which is made by combining AAB with ordinary Portland cement (OPC). These binders are a successful answer to a green building alternative if the techniques used to make them do not have an unacceptable negative impact on the environment, human health, or resource depletion. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) software was employed for choosing the optimal materials' alternative depending on the available criteria. The results revealed that AAB concrete provided a more ecologically friendly alternative than OPC concrete, higher strength for comparable water/binder ratio, and better performance in terms of embodied energy, resistance to freeze-thaw cycles, high temperature resistance, and mass loss due to acid attack and abrasion.

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

In the 21st century, numerous numerical calculation techniques have been discovered and used in several fields of science and technology. The purpose of this study was to use an artificial neural network (ANN) to forecast the compressive strength of waste-based concretes. The specimens studied include different kinds of mineral additions: metakaolin, silica fume, fly ash, limestone filler, marble waste, recycled aggregates, and ground granulated blast furnace slag. This method is based on the experimental results available for 1303 different mixtures gathered from 22 bibliographic sources for the ANN learning process. Based on a multilayer feedforward neural network model, the data were arranged and prepared to train and test the model. The model consists of 18 inputs following the type of cement, water content, water to binder ratio, replacement ratio, the quantity of superplasticizer, etc. The ANN model was built and applied with MATLAB software using the neural network module. According to the results forecast by the proposed neural network model, the ANN shows a strong capacity for predicting the compressive strength of concrete and is particularly precise with satisfactory accuracy (R² = 0.9888, MAPE = 2.87%).

An Experimental and Analytical Study on a Damage Constitutive Model of Engineered Cementitious Composites under Uniaxial Tension

Engineered cementitious composites (ECC) exhibit ultra-high ductility and post-cracking resistance, which makes it an attractive material in civil engineering. First, a monotonic uniaxial tensile test was performed, considering the effects of polyvinyl alcohol (PVA) fiber volume content and water-binder ratio. Then, the effects of the above variables on the tensile characteristics including the tensile stress-strain relationship, deformation capacity, and fracture energy were investigated based on test results; and when the water-binder ratio is 0.28 and the fiber volume content is 2%, the deformation performance of ECC is improved most significantly. Next, combined with damage mechanics theory, the damage evolution mechanism of ECC in monotonic uniaxial tension was revealed, based on which the damage factor and damage evolution equation of ECC were developed and the expressions of model parameters were proposed. Moreover, the comparison between the proposed model and test results demonstrated the accuracy of the proposed model. Finally, to further verify the feasibility of the proposed model, a finite element (FE) simulation analysis of the tensile performance of high-strength stainless steel wire rope (HSSWR) reinforced ECC by adopting the proposed model was compared with test results and the simulation analysis results by using anther existing model, the "trilinear model of ECC". The comparison shows that the proposed model in this paper can predict more accurately.

Drying Shrinkage, Sulphuric Acid and Sulphate Resistance of High-Volume Palm Oil Fuel Ash-Included Alkali-Activated Mortars

Nowadays, an alkali-activated binder has become an emergent sustainable construction material as an alternative to traditional cement and geopolymer binders. However, high drying shrinkage and low durability performance in aggressive environments such as sulphuric acid and sulphate are the main problems of alkali-activated paste, mortar and concrete. Based on these factors, alkali-activated mortar (AAM) binders incorporating high-volume palm oil fuel ash (POFA), ground blast furnace slag (GBFS) and fly ash (FA) were designed to enhance their durability performance against aggressive environments. The compressive strength, drying shrinkage, loss in strength and weight, as well as the microstructures of these AAMs were evaluated after exposure to acid and sulphate solutions. Mortars made with a high volume of POFA showed an improved durability performance with reduced drying shrinkage compared to the control sample. Regarding the resistance against aggressive environments, AAMs with POFA content increasing from 0 to 70% showed a reduced loss in strength from 35 to 9% when subjected to an acid attack, respectively. Additionally, the results indicated that high-volume POFA binders with an increasing FA content as a GBFS replacement could improve the performance of the proposed mortars in terms of durability. It is asserted that POFA can significantly contribute to the cement-free industry, thus mitigating environmental problems such as carbon dioxide emission and landfill risks. Furthermore, the use of POFA can increase the lifespan of construction materials through a reduction in the deterioration resulting from shrinkage problems and aggressive environment attacks.

Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

In the 21^st century, with the increasingly urgent requirements for lightweight in the fields of aviation, aerospace, and electronics, especially automobiles, many properties of magnesium alloy materials, especially the low-density performance characteristics, have been widely valued. In order to better use magnesium metal materials, it is very important to evaluate its mechanical properties. This article is based on 196 sets of mechanical performance experimental results and related data of AZ31 and AZ91 2 magnesium alloys. Based on data analysis and sorting, take deformation temperature, deformation rate, deformation coefficient, solid solution temperature, and solid solution time as input and take ultimate tensile strength (UTS), yield strength (YS), and elongation (ELO) as output. The 5-8-1 three-layer BP neural network forecast model optimized by the genetic algorithm is used for data training. The training results show that the prediction model can accurately predict the tensile strength, yield strength, and elongation. Compared with the general BP neural network prediction model, the BP neural network based on the genetic algorithm has small discreteness and high fitness: the average error of UTS and YS of AZ31 magnesium alloy is reduced to 0.88% and 3.3%, respectively. The most obvious is that the elongation of AZ31 ELO is reduced, and the error is reduced to 8.1%.

Hybrid Krill Herd-ANN Model for Prediction Strength and Stiffness of Bolted Connections

The behavior of beam-to-column connections significantly influences the stability, strength, and stiffness of steel structures. This is particularly important in extreme non-elastic responses, i.e., earthquakes, and sudden column removal, as the fluctuation in strength and stiffness affects both supply and demand. Accordingly, it is essential to accurately estimate the strength and stiffness of connections in the analysis of and design procedures for steel structures. Beginning with the state-of-the-art, the capacity of three available component-based mechanical models to estimate the complex mechanical properties of top- and seat-angle connections with double-web angles (TSACWs), with variable parameters, were investigated. Subsequently, a novel hybrid krill herd algorithm-artificial neural network (KHA-ANN) model was proposed to acquire an informational model from the available experimental dataset. Using several statistical metrics, including the corresponding coefficient of variation (CoV), correlation coefficient (R), and the correlation coefficient provided by the Taylor diagram, this study revealed that the krill herd-ANN model achieved the most reliable predictive accuracy for the strength and stiffness of top- and seat-angle connections with double web angles.

BAT Algorithm-Based ANN to Predict the Compressive Strength of Concrete—A Comparative Study

The number of effective factors and their nonlinear behaviour—mainly the nonlinear effect of the factors on concrete properties—has led researchers to employ complex models such as artificial neural networks (ANNs). The compressive strength is certainly a prominent characteristic for design and analysis of concrete structures. In this paper, 1030 concrete samples from literature are considered to model accurately and efficiently the compressive strength. To this aim, a Feed-Forward (FF) neural network is employed to model the compressive strength based on eight different factors. More in detail, the parameters of the ANN are learned using the bat algorithm (BAT). The resulting optimized model is thus validated by comparative analyses towards ANNs optimized with a genetic algorithm (GA) and Teaching-Learning-Based-Optimization (TLBO), as well as a multi-linear regression model, and four compressive strength models proposed in literature. The results indicate that the BAT-optimized ANN is more accurate in estimating the compressive strength of concrete.