In the geotechnical and terramechanical engineering applications, precise understandings are yet to be established on the off-road structures interacting with complex soil profiles. Several theoretical and experimental approaches have been used to measure the ultimate bearing capacity of the layered soil, but with a significant level of differences depending on the failure mechanisms assumed. Furthermore, local displacement fields in layered soils are not yet studied well. Here, the bearing capacity of a dense sand layer overlying loose sand beneath a rigid beam is studied under the plain-strain condition. The study employs using digital particle image velocimetry (DPIV) and finite element method (FEM) simulations. In the FEM, an experimentally characterised constitutive relation of the sand grains is fed as an input. The results of the displacement fields of the layered soil based DPIV and FEM simulations agreed well. From the DPIV experiments, a correlation between the slip surface angle and the thickness of the dense sand layer has been determined. Using this, a new and simple approach is proposed to predict theoretically the ultimate bearing capacity of the layered sand. The approach presented here could be extended more easily for analysing other complex soil profiles in the ground-structure interactions in future
priorities of materials research due to their promising properties, especially in the field of thermoelectricity. The efficiency or performance of thermoelectric devices is expressed in terms of the thermoelectric figure-of-merit (ZT) – a standard indicator of a material’s thermoelectric properties for use in cooling systems. The evaluation of ZT is principally determined by the thermoelectric characteristics of the nanomaterials. In this paper, a set of investigative computations was performed to study the thermoelectric properties of monolayer TMDCs according to the semiclassical treatment of the Boltzmann transport equation. It was confirmed that the thermoelectric properties of 2D materials can be greatly improved compared with thei
... Show MoreThe study was conducted to determine the effect of of cumin (Cuminum cyminum) and ginger (Zingiber officinale) powder on performance in Japanese quails. 630 bird (7 birds for each treatment) nine weeks of age were used. Experiment was continued for 12 weeks divided to six periods and the birds distributed randomly on nine combinations of cumin and ginger, three levels of each plant powder namely, 0, 0.5 and 1%. T1 (Control) contain the slandered diet (0% of cumin and 0% of ginger), T2: (0% of cumin and 0.5% of ginger), T3: (0% of cumin and 1% of ginger), T4: (0.5% of cumin and 0% of ginger), T5: (0.5% of cumin and 0.5% of ginger), T6: (0.5% of cumin and 1% of ginger), T7: (1% of cumin and 0% of ginger), T8: (1% of cumin and 0.5% of ginger),
... Show MoreBackground: The objectives of this study are to evaluate the effect of addition of Multi-Wall Carbon Nano Tubes (MWCNTs) of different concentrations (0.05 mg.mL-1,0.25 mg.mL-1,0.5 mg.mL-1and1 mg.mL-1) on dimethyl sulphoxide DMSO and distilled water (DW) on tooth enamel. It intends to evaluate enamel microhardness in (Kg. m-2) pre and post the application of Multi-Wall Carbon Nano Tubes (MWCNTs). Materials and Methods: Thirty specimens prepared for the present study to measure the hardness of the enamel. Results: The results showed that a significant increase in the enamel microhardness for groups 0.05 mg/mL (group B), 0.25 mg/mL (group C), 0.5 mg/mL (group D) and 1 mg/mL (group E) compared with control group (group A) in dimethyl sulphoxi
... Show Moresensor sampling rate (SSR) may be an effective and crucial field in networked control systems. Changing sensor sampling period after designing the networked control system is a critical matter for the stability of the system. In this article, a wireless networked control system with multi-rate sensor sampling is proposed to control the temperature of a multi-zone greenhouse. Here, a behavior based Mamdany fuzzy system is used in three approaches, first is to design the fuzzy temperature controller, second is to design a fuzzy gain selector and third is to design a fuzzy error handler. The main approach of the control system design is to control the input gain of the fuzzy temperature controller depending on the cur
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