Anchor plate bearing capacity in flexible mesh facings

This work addresses the problem of the loading capacity of an anchor plate coupled with a steel wire mesh in soil retaining applications. The interaction mechanism between the flexible mesh facing, the underlying soil layer and the plate is studied starting from the results of several laboratory punch tests involving both the plate and the mesh only, and the whole soil-mesh-plate system. The experimental tests have been reproduced by adopting a 3D discrete element model where also the wire mesh is discretized as an assembly of interconnected nodal particles. The interaction between these particles is ruled by elasto-plastic tensile force–displacement laws in which a distortion is introduced in a stochastic manner to account for the wires’ geometrical irregularities. The mesh model is then validated with reference to a set of punch tests in which the shape and size of the punching element as well as the nominal wire diameter were varied. Subsequently, the model is extended to a punch against soil test configuration permitting an insight into the nontrivial local mechanism between the mesh facing and the underlying granular layer. The good agreement between the numerical predictions and the experimental observations at the laboratory scale allowed us to extend the model towards more realistic field conditions for which the role of the mesh panel boundary conditions, the mesh mechanical properties, the soil mechanical properties and the anchor plate geometry is investigated.     

Comparative study of EICP treatment methods on the mechanical properties of sandy soil

This study analyzed the effect of different treatment methods in enzyme-induced carbonate precipitation (EICP) on the mechanical properties of soil. Soybean crude urease was used to catalyze the precipitation of calcium carbonate (CaCO₃). A multiple-phase method was proposed and further compared with commonly practiced EICP treatment methods (including the one-phase method, two-phase method, and premix-and-compact method) from the aspects of chemical conversion efficiency, CaCO₃ precipitation distribution, permeability, and unconfined compressive strength. Based on the findings, the characteristics of each method were further discussed and summarized. Although the enzymatic CaCO₃ precipitation generated from all the treatment methods could potentiate the soil strength to a great or less degree, using the proposed multiple-phase method could bring about a high chemical conversion efficiency, uniform distribution of CaCO₃ as well as preferable permeability retention. In addition, the multiple-phase method could significantly improve the efficiency of urease usage.     

Evaluation of back-calculated elastic moduli of unreinforced and geocell-reinforced unbound granular material from full-scale field tests

This paper presents a field-scale experimental track over a poor subgrade with an unreinforced section and a geocell-reinforced section subjected to in-situ performance tests. Plate load tests and Benkelman beam tests were carried out distributed in several unreinforced and reinforced layers. The objective was to: (1) examine the variability of the elastic modulus of unbound granular material (UGM) due the influence of its thickness and the presence of poor subgrade in its base, (2) evaluate the modulus improvement factor (MIF) generated by the geocell reinforcement in the UGM and (3) verify the most appropriate condition to apply the MIF to transport infrastructure design. The results showed that there is a significant influence of the thickness of the UGM layer on its elastic modulus when the layer is supported directly over a soft subgrade. The MIF values obtained in field suggest that its determination is mostly related to the UGM maximum elastic modulus rather than its decreased values (by virtue of poor subgrade or reduced thicknesses), and that the analytical formulation presented for MIF calculation has good predictive capability to be applied to pavement design.     

Experimental study on uplift behavior of shallow anchor plates in geogrid-reinforced soil

Geogrid reinforcement can significantly improve the uplift bearing capacity of anchor plates. However, the failure mechanism of anchor plates in reinforced soil and the contribution of geogrids need further investigation. This paper presents an experimental study on the anchor uplift behavior in geogrid-reinforced soil using particle image velocimetry (PIV) and the high-resolution optical frequency domain reflectometry (OFDR). A series of model tests were performed to identify the relationship between the failure mechanism and various factors, such as anchor embedment ratio, number of geogrid layers, and their location. The test results indicate that soil deformation and the uplift resistance of anchor plates are substantially influenced by anchor embedment ratio and location of geogrids, whereas the number of geogrid layers has limited influence. In reinforced soil, increasing the embedment ratio greatly improves the ultimate bearing capacities of anchor plates and affects the interlock between the soil and geogrids. As the embedment depth increases, the failure surfaces gradually change from a vertical slip surface to a bulb-shaped surface that is limited within the soil. The strain monitoring data shows that the deformations of geogrids are symmetrical, and the peak strains of geogrids can characterize the reinforcing effects.     

Tribological and mechanical characteristics of AA5083 alloy reinforced by hybridising heavy ceramic particles Ta2C & VC with light GNP and Al2O3 nanoparticles

In this study, AA5083 sheets were reinforced with four different hybrid nanoparticles by friction stir processing (FSP) for the development of surface nanocomposites used in advanced engineering applications. The present research focused on improving the properties and tribological behaviour of AA5083 alloy surfaces, including novel hybrid nanoparticles and the intermetallic phase formed during FSP. A tribometer tester with a constant normal load was used to examine the tribological performance of the hybrid composites. After the wear test, a surface profiler inspector was used to analyse the morphology and surface roughness of the examined materials. The Vickers micro-hardness of the base metal and the manufactured composites were measured. During FSP, a new intermetallic phase of AlV₃ was successfully formed at 300–400 °C in the hybrid nanocomposites containing VC particles. The reinforcements resulted in additional grain refining than FSP. The AA5083/Ta2C–Al₂O₃ exhibited the greatest grain refinement, a sixty-fold reduction in grain size compared to that of the base alloy. The results revealed that the hybrid nanocomposites containing VC particles demonstrated the most significant microhardness values inside the stirred zone as a result of the presence of the AlV₃ phase, which was increased by 25–30%. Moreover, the mechanical properties were significantly improved for all manufactured nanocomposites. The tensile strength was increased by 28% through the hybridisation of AA5083 using a hybrid of VC-GNPs. The dispersion of Ta₂C-GNPs and VC-GNPs in the matrix led to excellent interfacial adhesion, resulting in an enhancement in the mechanical properties. The AA5083/VC-GNPs surface composite outperformed other manufactured composites regarding wear resistance. In addition, due to GNPs soft nature, it reduced the coefficient of friction (COF) of the manufactured composites by 20–25% compared to other reinforcements.     

Adaptive investigation of the optical properties of polymer fibers from mixing noisy phase shifting microinterferograms using deep learning algorithms

In this article, an adaptive denoising method is suggested to accurate investigate the optical and structural features of polymeric fibers from noisy phase shifting microinterferograms. The mixed class of noise that may produce in the phase-shifting interferometric techniques is established. To our knowledge, this is an early study considered the mixing noises that may occur in microinterferograms. The suggested method utilized the convolution neural networks to detect the noise class and then denoising, it according to its class. Four convolution neural networks (Googlenet, VGG-19, Alexnet, and Alexnet–SVM) are refined to perform the automatic classification process for the noise class in the established data set. The network with the highest validation and testing accuracy of these networks is considered to apply the suggested method on realistic noisy microinterferograms for polymeric fibers, polypropylene and antimicrobial polyethylene terephthalate)/titanium dioxide, recoded using interference microscope. Also, the suggested method is applied on noisy microinterferograms include crazing and nanocomposite material. The demodulated phase maps and the three-dimensional birefringence profiles are calculated for tested fibers according to the suggested method. The obtained results are compared with the published data for these fibers and found to be in good agreements.     

An IoT-Aware System for Managing Patients’ Waiting Time Using Bluetooth Low-Energy Technology

It is a common observation that whenever patients arrives at the front desk of a hospital, outpatient clinic, or other health-associated centers, they have to first queue up in a line and wait to fill in their registration form to get admitted. The long waiting time without any status updates is the most common complaint, concerning health officials. In this paper, UrNext, a location-aware mobile-based solution using Bluetooth low-energy (BLE) technology is presented to solve the problem. Recently, a technology-oriented method, the Internet of Things (IoT), has been gaining popularity in helping to solve some of the healthcare sector’s problems. The implementation of this solution could be illustrated through a simple example of when a patient arrives at a clinic for a consultation. Instead of having to wait in long lines, that patient will be greeted automatically, receive a push notification of an admittance along with an estimated waiting time for a consultation session. This will not only provide the patients with a sense of freedom but would also reduce the uncertainty levels that are generally observed, thus saving both time and money. This work aims to improve the clinics’ quality of services, organize queues and minimize waiting times, leading to patients’ comfort while reducing the burden on nurses and receptionists. The results demonstrate that the presented system is successful in its performance and helps achieves a pleasant and conducive clinic visitation process with higher productivity.     

Green Measurements for Software Product Based on Sustainability Dimensions

Software is a central component in the modern world and vastly affects the environment’s sustainability. The demand for energy and resource requirements is rising when producing hardware and software units. Literature study reveals that many studies focused on green hardware; however, limited efforts were made in the greenness of software products. Green software products are necessary to solve the issues and problems related to the long-term use of software, especially from a sustainability perspective. Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment, the mentioned benefits will not be attained. Currently, there are not enough works to address this problem, and the green status of software products is uncertain and unsure. This paper aims to identify the green measurements based on sustainable dimensions in a software product. The second objective is to reveal the relationships between the elements and measurements through empirical study. The study is conducted in two phases. The first phase is the theoretical phase, where the main components, measurements and practices that influence the sustainability of a software product are identified. The second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achieving green software products. This study has revealed seven green measurements of software product: Productivity, Usability, Cost Reduction, Employee Support, Energy Efficiency, Resource Efficiency and Tool Support. The relationships are statistically significant, with a significance level of less than 0.01 (p = 0.000). Thus, the hypothesised relationships were all accepted. The contributions of this study revolve around the research perspectives of the measurements to attain a green software product.     

Coal char gasification for co-production of fuel gas and methane decomposition catalysts

Partial gasification of coal char was conducted with addition of metal oxides for co-production of fuel gas and methane decomposition catalysts. Effect of the metal composition (Ni, Co and Fe based mono- or bi-metals) was investigated on the fuel gas production and the resultant catalyst surface and textural properties, morphology and performance in catalytic methane decomposition (CMD). Besides H₂-rich fuel gas production (the combustion energy up to 11.03–23.42 MJ/kg_char) from the gasification, the gasification residue can directly serve as the effective and efficient catalyst for CMD. The Fe and Fe–Co composite oxides were found to be better among the mono- and bi-metallic oxides for the fuel gas production during the gasification, respectively. The Ni-based mono-/bi-metallic catalysts could display high and stable methane conversion (up to 80%) during the 600-min CMD test at 850 °C. Promotional role of the second metal in CMD was discussed on the carbon diffusion, metal mobility and reducibility, formation and growth of the deposited carbons. The formed carbon morphology after CMD was analyzed based on the Kirkendall effect and Tammann temperature and further correlated to the potential catalyst deactivation.     

Shared human–robot path following control of an unmanned ground vehicle

This paper considers the shared path following control of an unmanned ground vehicle by a single person. A passive measure of human intent is used to blend the human and machine inputs in a mixed initiative approach. The blending law is combined with saturated super-twisting sliding mode speed and heading controllers, so that exogenous disturbances can be counteracted via equivalent control. It is proven that when the proposed blending law is used, the combined control signals from both the human and automatic controller respect the actuator magnitude constraints of the machine. To demonstrate the approach, shared control experiments are performed using an unmanned ground vehicle, which follows a lawn mower pattern shaped path.