Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

     

Study on the size dependent effective Young modulus by EPI method based on modified couple stress theory

Microsystem Technologies - Various experimental and theoretical researches have been shown the size-dependence behavior of the effective Young modulus (EYM) in the micron and sub-micron scales. One...     

Preparation and characterization of activated carbon from oil sands coke

Several million tonnes of oil sands coke are generated each year in Alberta, Canada as a by-product of bitumen upgrading. Due to its high carbon content, oil sands coke can be a suitable precursor for the preparation of activated carbon. In this study, delayed and fluid oil sands coke were physically activated in a muffle furnace under select conditions of activation time (2–6 h), temperature (800–900 °C), steam rate (0.3–0.5 mL/min), and activation atmosphere (CO₂, CO₂ + steam, and N₂ + steam). The activated products were characterized using thermogravimetric analysis, X-ray diffraction, scanning electron microscopy, nitrogen adsorption, iodine and methylene blue tests. An increase in activation time and temperature resulted in higher surface areas in both delayed and fluid coke due to an enhanced etching of pores. An increase in steam rate led to the production of the highest specific surface area (577 m²/g) and iodine number (670 mg/g) within delayed coke; whereas, a lower steam rate resulted in the production of the highest specific surface area (533 m²/g) and iodine number (530 mg/g) in activated fluid coke samples.     

Desalination by solar-powered reverse osmosis in a remote area of the Sultanate of Oman

The Ministry of Water Resources successfully conducted an experimental study on the use of solar power to desalinate brackish ground water at their Heelat ar Rakah camp, a remote location some 900 km south of Muscat, the capital of Oman. The system comprises components for pre-treatment of pumped well water to separate hydrogen sulphide, acid dosing to correct the pH, cartridge filtration, a solar powered reverse osmosis unit, and a reject-water evaporation pond. The solar powered system comprises a 23.2 m² solar photovoltaic generator with a peak capacity of 3250 W_p, a boost charge battery of 200 Ah at 48 VDC, a charge controller, a sine-wave inverter of 3000 VA with an output of 230 V, 50 Hz, and necessary controls and instrumentation. The design water output of 5 m³/day during 5 h (of each day) was achieved, with the output sometimes exceeding 7.5 m³/day. The average cost of production is estimated at US$6.52/m³ over the 20-year lifetime of the equipment. The study has demonstrated that solar-powered reverse osmosis systems are particularly appropriate to remote locations that have limited or no access to supply services such as fuel, power or potable water.     

An asymmetric anthraquinone-modified carbon/ruthenium oxide supercapacitor

An asymmetric supercapacitor with improved energy and power density, relative to a symmetric Ru oxide device, has been constructed with anthraquinone-modified carbon fabric (Spectracarb 2225) as the negative electrode and Ru oxide as the positive electrode. The performance of the supercapacitor was characterized by cyclic voltammetry and constant current discharging. Use of the anthraquinone-modified electrode extends the negative potential limit that can be used, relative to Ru oxide, and allows higher cell voltages to be used. The maximum energy density obtained was 26.7 Wh kg⁻¹ and an energy density of 12.7 Wh kg⁻¹ was obtained at a 0.8 A cm⁻² discharge rate and average power density of 17.3 kW kg⁻¹. The C-AQ/Ru oxide supercapacitor requires 64% less Ru relative to a symmetric Ru oxide supercapacitor.     

Efficiency of ultraviolet/ozone treatments in the improvement of the dyeability and light fastness of wool

In this study, we evaluated the effect of ultraviolet/ozone treatments for different times on the characteristics of wool fabrics with respect to wettability, permeability, yellowness index, and weight loss. The beneficial effects of this treatment on dyeability, color parameters, light fastness characteristics, and the change in color difference after exposure of the treated dyed samples to artificial daylight for about 150 h was investigated. The results indicated that the improvement in wetting processes may have been due to to surface modifications; this meant that an increase in the amorphousity of the treated samples, the oxidation of the cystine linkage on the surface of the fabrics, and the formation of free‐radical species encouraged dye penetration and aggregation inside the fiber pores as well as bond formation. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 90: 3668–3675, 2003     

Concept of Functionally Graded Materials for Advanced Thermal Barrier Coating Applications

This feature article explores the concept of creating functionally graded metal-ceramic composite microstructures for thermal barrier coatings used in gas-turbine applications. From a thermomechanical perspective, this concept offers the possibility of significantly improving the life and reliability of thermal barrier coatings. However, prior research reveals that progress has been somewhat limited because of the oxidative instability exhibited by some metal-ceramic composite microstructures. The present study addresses some of the materials criteria and research issues associated with preparing chemically stable, yet mechanically durable, graded metal-ceramic microstructures for realistic application environments.     

Polyphenol extraction from Origanum dictamnus using low-transition temperature mixtures composed of glycerol and organic salts: Effect of organic anion carbon chain length

Novel low-transition temperature mixtures (LTTMs), composed of glycerol and sodium acetate, sodium propionate, and sodium butyrate, were synthesized with the aim to investigate the organic anion chain length effect on the performance of polyphenol extraction from the medicinal plant Origanum dictamnus. The LTTMs used as hydrogen bond donor:hydrogen bond acceptor molar ratio of 6:1 and after establishing optimal conditions of water content and liquid-to-solid ratio by response surface methodology, kinetics was performed to identify the highest efficient system. The results drawn indicated that the longer the anion chain length, the higher the requirement for water content to achieve optimal total polyphenol yield. Extractions with LTTMs comprising of sodium propionate and sodium butyrate gave virtually equal yields in total polyphenols, yet extraction with the former solvent was significantly less energy-demanding, with the activation energy being 8.77?kJ?mol^?1. Liquid chromatography-diode array-mass spectrometry analyses revealed that the extract obtained with glycerol/sodium propionate at 70?°C also displayed a richer polyphenolic profile, while the antioxidant activity of the extract was not negatively affected up to this temperature. This novel green solvent is therefore proposed as a highly efficient means of recovering bioactive polyphenols from plant material.     

Effects of the slip boundary condition on dynamics and pull-in instability of carbon nanotubes conveying fluid

This paper addresses the effects of the slip boundary condition on dynamics and pull-in instability of carbon nanotubes (CNTs) containing internal fluid flow. Both the clamped–clamped and the cantilever boundary conditions are considered. The structure of CNTs is modelled using the size-dependent strain gradient theory (SGT) of continuum mechanics. It is shown that the Knudsen number (Kn) has a significant effect on the static and dynamic CNT response due to pull-in voltage loading and the existence of the instability region.     

RBFNN versus FFNN for daily river flow forecasting at Johor River,Malaysia

Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting.