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.

     

Improving of Wavelength Division Multiplexing Based on Free Space Optical Communication via Power Comparative System

Wireless Personal Communications - The names of the second and third authors in the initial online publication were not correctly typeset. The original article has been corrected.     

Study on optimization of the circumferential and axial wavy geometrical configuration of hydrodynamic journal bearing

This paper is focused on using GA genetic algorithm to find the optimal performance with respect to shape optimization in three dimensions for the hydrodynamic journal bearing. The mathematical model for film thickness was drawn using Fourier series function and axial waviness value ( $\bar \Delta $ ) D to represent the journal bearing in circumferential and axial direction, respectively. The objective was then to determine the Fourier coefficients and axial waviness value ( $\bar \Delta $ ) D that maximized the load capacity subjected to a given set of constraint. Optimized results show that the presence of cos wave in axial direction, with a positive dimensionless amplitude (+A) and waviness number m = 0.633, improves the load capacity by (8–10) % over the cylindrical plain bearing with the same arbitrary shape and size; in general, the increasing order of Fourier series (n), an axial dimensionless amplitude and L/D ratio cause the change in load capacity to become more evident.     

Mannose based polymeric nanoparticles for lectin separation

The aim of this work is to synthesize the original, new polymeric nanoparticles for concanavalin A (Con A) purification. Nanoparticles were synthesized by surfactant free emulsion polymerization. In the polymerization prosedure, 1-O-(2′-hydroxy-3′-acryloyloxypropyl)-2,3:5,6-di-O-isopropylidene-α-D-mannofuranose (Man-OPA) was used as co-monomer and 2-hydroxyethylmethacrylate (HEMA) was used as a monomer. Man-OPA was characterized by Fourier Transform Infrared Spectroscopy (FTIR), nuclear magnetic resonance and elemental analysis techniques. Poly(HEMA-Man-OPA) nanoparticles were characterized by scanning electron microscopy, FTIR and Zeta Sizer. In adsorption?desorption experiments, maximum Con A adsorption capacity of poly(HEMA-Man-OPA) nanoparticles was found 630.6 mg/g nanoparticle (pH 7.5, 1.0 mg/mL). Adsorption?desorption experiments were repeated in four times. According to results, these nanoparticles could be used several times without significant decrease in Con A adsorption capacity.     

Binary Cellulose Nanocrystal Blends for Bioinspired Damage Tolerant Photonic Films

Most attempts to emulate the mechanical properties of strong and tough natural composites using helicoidal films of wood‐derived cellulose nanocrystals (w‐CNCs) fall short in mechanical performance due to the limited shear transfer ability between the w‐CNCs. This shortcoming is ascribed to the small w‐CNC‐w‐CNC overlap lengths that lower the shear transfer efficiency. Herein, we present a simple strategy to fabricate superior helicoidal CNC films with mechanical properties that rival those of the best natural materials and are some of the best reported for photonic CNC materials thus far. Assembling the short w‐CNCs with a minority fraction of high aspect ratio CNCs derived from tunicates (t‐CNCs), we report remarkable simultaneous enhancement of all in‐plane mechanical properties and out‐of‐plane flexibility. The important role of t‐CNCs is revealed by coarse grained molecular dynamics simulations where the property enhancement are due to increased interaction lengths and the activation of additional toughening mechanisms. At t‐CNC contents greater than 5% by mass the mixed films also display UV reflecting behaviour. These damage tolerant optically active materials hold great promise for application as protective coatings. More broadly, we expect the strategy of using length‐bidispersity to be adaptable to mechanically enhancing other matrix‐free nanoparticle ensembles.     

Interfacial delamination of thin-film PTFE (polytetrafluoroethylene) coatings

Interfacial adhesion is a major concern with respect to successful performance of thin polymer films in developing new thin-film processes. Micro-indentation was used to induce interfacial delamination of polytetrafluoroethylene (PTFE) films deposited on glass substrates using hot filament chemical vapour deposition (HFCVD). Film thickness (1, 2, 3, 5, 10 µm) and indentation load (0.5, 0.75, 1, 2, 3 N) effects on the delamination diameter were investigated. A three-dimensional finite element model using shear material failure criterion and cohesive zone model (CZM) was developed to simulate the delamination. A normalized load–delamination radius relationship was obtained to evaluate the interfacial fracture toughness. The experimental observations showed that the delamination diameter depends on film thickness and indentation load. The numerical simulation indicates the delamination diameter depends on film thickness, material properties, and indentation force. The predictions of interfacial fracture toughness for 5- and 10-µm PTFE films are much smaller than those values using Rosenfeld et al.’s equation, which excludes the energy spent during the penetration.     

Source apportionment of PM(10) and PM(2.5) using positive matrix factorization and chemical mass balance in Izmir, Turkey

Atmospheric particulate matter (PM) fractions (PM(10) and PM(2.5)) were sampled concurrently between June 2004 and May 2005 at two sites (urban and suburban) in Izmir, Turkey. The elemental composition of PM (Al, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Sr, V, and Zn) was determined using inductively coupled plasma-optical emission spectrometer. Elemental compositions of several PM sources were also characterized. Positive matrix factorization (PMF) and chemical mass balance modeling (CMB) were applied to determine the PM sources and their contributions to air concentrations. The major contributors to PM were fossil fuel burning, traffic emissions, mineral industries and marine salt according to the PMF results. However, undetermined parts were more than 40%. On the other hand, the contributions to PM could be determined completely by CMB, and the dominant contributor was traffic with >70% at the two sites. Fossil fuel burning, mineral industries, marine salt and natural gas-fired power plant were the minor contributors.     

Elasticity, strength and resilience: A comparative study on mechanical signatures of α-Helix, β-sheet and tropocollagen domains

In biology, structural design and materials engineering is unified through formation of hierarchical features with atomic resolution, from nano to macro. Three molecular building blocks are particularly prevalent in all structural protein materials: alpha helices (AHs), beta-sheets (BSs) and tropocollagen (TC). In this article we present a comparative study of these three key building blocks by focusing on their mechanical signatures, based on results from full-atomistic simulation studies. We find that each of the basic structures is associated with a characteristic material behavior: AH protein domains provide resilience at large deformation through energy dissipation at low force levels, BS protein domains provide great strength under shear loading, and tropocollagen molecules provide large elasticity for deformation recovery. This suggests that AHs, BSs, and TC molecules have mutually exclusive mechanical signatures. We correlate each of these basic properties with the molecule’s structure and the associated fundamental rupture mechanisms. Our study may enable the use of abundant protein building blocks in nanoengineered materials, and may provide critical insight into basic biological mechanisms for bio-inspired nanotechnologies. The transfer towards the design of novel nanostructures could lead to new multifunctional and mechanically active, tunable, and changeable materials.     

An Experimental Study of Friction and Durability of a Thin PTFE Film on Rough Aluminum Substrates

Frictional and durability characteristics of 1-µm-thick polytetrafluoroethylene (PTFE) films deposited by hot filament chemical vapor deposition on aluminum substrates were investigated. A universal microtribotester was used to examine the frictional and durability properties using the ball-on-plate and ball-on-disk configurations, respectively. Effects of normal force (2.5, 5, 10, 15 N), sliding speed (0.1, 1, 5 mm/s), and surface roughness of the aluminum substrate (R_a = 0.01, 0.57, 1.28, 2.34 µm) on the coefficient of friction (COF) and the effects of normal force (2.5, 5 N), sliding speed (0.42, 4.19 mm/s), and surface roughness on the durability were investigated. It was shown that the COF of the PTFE-coated interface increases with increasing surface roughness or sliding speed. The COF depends on the normal force to a lesser extent than the other two parameters. The medium-level, O(0.5 µm), roughness of the substrate provides the longest durability, whereas the smoothest or very rough surface provides shorter durability. Analysis of variance (ANOVA) indicates that the surface roughness has the most significant effect on the COF and durability. In the case of a smooth interface, a relationship between COF, sliding speed, and normal force can be predicted. Results indicate an optimal surface roughness for improving durability.     

Reliability-aware Co-synthesis for Embedded Systems

As technology scales, transient faults have emerged as a key challenge for reliable embedded system design. This paper proposes a design methodology that incorporates reliability into hardware–software co-design paradigm for embedded systems. We introduce an allocation and scheduling algorithm that efficiently handles conditional execution in multi-rate embedded systems, and selectively duplicates critical tasks to detect or correct transient errors, such that the reliability of the system is improved. Two methods are proposed to insert duplicated tasks into the schedule. The improved reliability is achieved by utilizing the otherwise idle computation resources and taking advantage of the overlapping schedule for mutually exclusive tasks in the conditional task graph, such that it incurs no resource or performance penalty.