The most commonly accepted method in evaluation of the mechanical properties of metals would be the tension test. Its main objective would be to determine the properties relevant to the elastic design of machines and structures. Investigation of the engineering and true Stress-strain relationships of three specimens in conformance with ASTM E 8-04 is the aim of this paper. For the purpose of achieving this aim, evaluation of values such as ultimate tensile strength, yield strength, percentage of elongation and area reduction, fracture strain and Young's Modulus was done once the specimens were subjected to uniaxial tensile loading. The results indicate that the properties of steel materials are independent from their thickness and they generally yield and fail at the same stress and strain values. Also, it is concluded that the maximum true stress values are almost 15% higher than that of the maximum engineering stress values while the maximum true strain failure values are 1.5% smaller than the maximum engineering strain failure values.

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Eco-friendly and sustainable materials that are cost-effective, while having a reduced carbon footprint and energy consumption, are in great demand by the construction industry worldwide. Accordingly, alkali-activated materials (AAM) composed primarily of industrial byproducts have emerged as more desirable alternatives to ordinary Portland cement (OPC)-based concrete. Hence, this study investigates the cradle-to-gate life-cycle assessment (LCA) of ternary blended alkali-activated mortars made with industrial byproducts. Moreover, the embodied energy (EE), which represents an important parameter in cradle-to-gate life-cycle analysis, was investigated for 42 AAM mixtures. The boundary of the cradle-to-gate system was extended to include the mechanical and durability properties of AAMs on the basis of performance criteria. Using the experimental test database thus developed, an optimized artificial neural network (ANN) combined with the cuckoo optimization algorithm (COA) was developed to estimate the CO2 emissions and EE of AAMs. Considering the lack of systematic research on the cradle-to-gate LCA of AAMs in the literature, the results of this research provide new insights into the assessment of the environmental impact of AAM made with industrial byproducts. The final weight and bias values of the AAN model can be used to design AAM mixtures with targeted mechanical properties and CO2 emission considering desired amounts of industrial byproduct utilization in the mixture.

Recycling of the waste rubber tire crumbs (WRTCs) for the concretes production generated renewed interest worldwide. The insertion of such waste as a substitute for the natural aggregates in the concretes is an emergent trend for sustainable development towards building materials. Meanwhile, the enhanced resistance of the concrete structures against aggressive environments is important for durability, cost-saving, and sustainability. In this view, this research evaluated the performance of several modified rubberized concretes by exposing them to aggressive environments i.e., acid, and sulphate attacks, elevated temperatures. These concrete (12 batches) were made by replacing the cement and natural aggregate with an appropriate amount of the granulated blast furnace slag (GBFS) and WRTCs, respectively. The proposed mix designs’ performance was evaluated by several measures, including the residual compressive strength (CS), weight loss, ultrasonic pulse velocity (UPV), microstructures, etc. Besides, by using the available experimental test database, an optimized artificial neural network (ANN) combined with the particle swarm optimization (PSO) was developed to estimate the residual CS of modified rubberized concrete after immersion one year in MgSO4 and H2SO4 solutions. The results indicated that modified rubberized concrete prepared by 5 to 20% WRTCs as a substitute to natural aggregate, provided lower CS and weight lose expose to sulphate and acid attacks compared to control specimen prepared by ordinary Portland cement (OPC). Although the CS were slightly declined at the elevated temperature, these proposed mix designs have a high potential for a wide variety of concrete industrial applications, especially in acid and sulphate risk.

The aim of this article is to predict the compressive strength of environmentally friendly concrete modified with eggshell powder. For this purpose, an optimized artificial neural network, combined with a novel metaheuristic shuffled frog leaping optimization algorithm, was employed and compared with a well-known genetic algorithm and multiple linear regression. The presented results confirm that the highest compressive strength (46 MPa on average) can be achieved for mix designs containing 7 to 9% of eggshell powder. This means that the strength increased by 55% when compared to conventional Portland cement-based concrete. The comparative results also show that the proposed artificial neural network, combined with the novel metaheuristic shuffled frog leaping optimization algorithm, offers satisfactory results of compressive strength predictions for concrete modified using eggshell powder concrete. Moreover, it has a higher accuracy than the genetic algorithm and the multiple linear regression. This finding makes the present method useful for construction practice because it enables a concrete mix with a specific compressive strength to be developed based on industrial waste that is locally available.

Alkali-activated products composed of industrial waste materials have shown promising environmentally friendly features with appropriate strength and durability. This study explores the mechanical properties and structural morphology of ternary blended alkali-activated mortars composed of industrial waste materials, including fly ash (FA), palm oil fly ash (POFA), waste ceramic powder (WCP), and granulated blast-furnace slag (GBFS). The effect on the mechanical properties of the Al2O3, SiO2, and CaO content of each binder is investigated in 42 engineered alkali-activated mixes (AAMs). The AAMs structural morphology is first explored with the aid of X-ray diffraction, scanning electron microscopy, and Fourier-transform infrared spectroscopy measurements. Furthermore, three different algorithms are used to predict the AAMs mechanical properties. Both an optimized artificial neural network (ANN) combined with a metaheuristic Krill Herd algorithm (KHA-ANN) and an ANN-combined genetic algorithm (GA-ANN) are developed and compared with a multiple linear regression (MLR) model. The structural morphology tests confirm that the high GBFS volume in AAMs results in a high volume of hydration products and significantly improves the final mechanical properties. However, increasing POFA and WCP percentage in AAMs manifests in the rise of unreacted silicate and reduces C-S-H products that negatively affect the observed mechanical properties. Meanwhile, the mechanical features in AAMs with high-volume FA are significantly dependent on the GBFS percentage in the binder mass. It is also shown that the proposed KHA-ANN model offers satisfactory results of mechanical property predictions for AAMs, with higher accuracy than the GA-ANN or MLR methods. The final weight and bias values given by the model suggest that the KHA-ANN method can be efficiently used to design AAMs with targeted mechanical features and desired amounts of waste consumption.

Self-cured concrete is a type of cement-based material that has the unique ability to mitigate the loss rate of water and increase the capacity of concrete to retain water compared to conventional concrete. The technique allows a water-filled internal curing agent to be added to the concrete mixture and then slowly releases water during the hydration process. Many researchers have studied the composition of self-curing concrete using different materials such as artificial lightweight aggregate (LWA), porous superfine powders, superabsorbent polymers (SAP), polyethylene glycol (PEG), natural fibers, and artificial normal-weight aggregate (ANWA) as curing agents. Likewise, physical, mechanical, and microstructure properties, including the mechanisms of curing agents toward self-curing cement-based, were discussed. It was suggested that adopting self-curing agents in concrete has a beneficial effect on hydration, improving the mechanical properties, durability, cracking susceptibility behavior, and mitigating autogenous and drying shrinkage. The interfacial transition zone (ITZ) between the curing agent and the cement paste matrix also improved, and the permeability is reduced.

The current study attempts to recognise an adequate classification for a semi-rigid beam-to-column connection by investigating strength, stiffness and ductility. For this purpose, an experimental test was carried out to investigate the moment-rotation (M-θ) features of flush end-plate (FEP) connections including variable parameters like size and number of bolts, thickness of end-plate, and finally, size of beams and columns. The initial elastic stiffness and ultimate moment capacity of connections were determined by an extensive analytical procedure from the proposed method prescribed by ANSI/AISC 360-10, and Eurocode 3 Part 1-8 specifications. The behaviour of beams with partially restrained or semi-rigid connections were also studied by incorporating classical analysis methods. The results confirmed that thickness of the column flange and endplate substantially govern over the initial rotational stiffness of of flush end-plate connections. The results also clearly showed that EC3 provided a more reliable classification index for flush end-plate (FEP) connections. The findings from this study make significant contributions to the current literature as the actual response characteristics of such connections are non-linear. Therefore, such semirigid behaviour should be used to for an analysis and design method.