Study of the effectiveness of outrigger system for high-rise composite buildings for cyclonic region

The demands of taller structures are becoming imperative almost everywhere in the world in addition to the challenges of material and labor cost, project time line etc. This paper conducted a study keeping in view the challenging nature of high-rise construction with no generic rules for deflection minimizations and frequency control. The effects of cyclonic wind and provision of outriggers on 28-storey, 42-storey and 57-storey are examined in this paper and certain conclusions are made which would pave way for researchers to conduct further study in this particular area of civil engineering. The results show that plan dimensions have vital impacts on structural heights. Increase of height while keeping the plan dimensions same, leads to the reduction in the lateral rigidity. To achieve required stiffness increase of bracings sizes as well as introduction of additional lateral resisting system such as belt truss and outriggers is required.

Simulation of AE wave propagation in thin plate for source identification

In most materials, short stress waves are generated during the process of plastic deformation, phase transformation, crack formation and crack growth. These phenomena are applied in acoustic emission (AE) for the detection of material defects in wide spectrum areas, ranging from non-destructive testing for the detection of materials defects to monitoring of microeismical activity. AE technique is also used for defect source identification and for failure detection. AE waves consist of P waves (primary/longitudinal waves), S waves (shear/transverse waves) and Rayleight (surface) waves as well as reflected and diffracted waves. The propagation of AE waves in various modes has made the determination of source location difficult. In order to use the acoustic emission technique for accurate identification of source location, an understanding of wave propagation of the AE signals at various locations in a plate structure is essential. Furthermore, an understanding of wave propagation can also assist in sensor location for optimum detection of AE signals. In real life, as the AE signals radiate from the source it will result in stress waves. Unless the type of stress wave is known, it is very difficult to locate the source when using the classical propagation velocity equations. This paper describes the simulation of AE waves to identify the source location in steel plate as well as the wave modes. The finite element analysis (FEA) is used for the numerical simulation of wave propagation in thin plate. By knowing the type of wave generated, it is possible to apply the appropriate wave equations to determine the location of the source. For a single plate structure, the results show that the simulation algorithm is effective to simulate different stress waves.

Influence of plate bending stiffness on mechanical properties of healing fracture, mouse femora

Mechanically well-defined stabilization systems have only recently become available, providing standardized conditions for studying the role of the mechanical environment on mouse bone fracture healing. The aim of this study was to characterize the time course of strength recovery and callus development of mouse femoral osteotomies stabilized with either low or high flexibility (in bending and torsion) internal fixation plates. Animals were euthanized and femora excised at 14, 21, and 28 days post-osteotomy for microCT analysis and torsional strength testing. While a larger mineralized callus was observed in osteotomies under more flexible conditions at all time points, the earlier bridging of the mineralized callus under less flexible conditions by 1 week resulted in an earlier recovery of torsional strength in mice stabilized with low flexibility fixation. Ultimate torque values for these bones were significantly higher at 14 and 21 days post-osteotomy compared to bones with the more flexible stabilization. Our study confirms the high reproducibility of the results that are achieved with this new implant system, therefore making it ideal for studying the influence of the mechanical environment on murine fracture healing under highly standardized conditions.

Composite SaaS placement and resource optimization in Cloud computing using evolutionary algorithms

Software as a Service (SaaS) is gaining more and more attention from software users and providers recently. This has raised many new challenges to SaaS providers in providing better SaaSes that suit everyone needs at minimum costs. One of the emerging approaches in tackling this challenge is by delivering the SaaS as a composite SaaS. Delivering it in such an approach has a number of benefits, including flexible offering of the SaaS functions and decreased cost of subscription for users. However, this approach also introduces new problems for SaaS resource management in a Cloud data centre. We present the problem of composite SaaS resource management in Cloud data centre, specifically on its initial placement and resource optimization problems aiming at improving the SaaS performance based on its execution time as well as minimizing the resource usage. Our approach differs from existing literature because it addresses the problems resulting from composite SaaS characteristics, where we focus on the SaaS requirements, constraints and interdependencies. The problems are tackled using evolutionary algorithms. Experimental results demonstrate the efficiency and the scalability of the proposed algorithms.

Clustering composite SaaS components in cloud computing using a grouping genetic algorithm

Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA.

Prandtl number scaling of the unsteady natural convection boundary layer adjacent to a vertical flat plate for Pr > 1 subject to ramp surface heat flux

It is found in the literature that the existing scaling results for the boundary layer thickness, velocity and steady state time for the natural convection flow over an evenly heated plate provide a very poor prediction of the Prandtl number dependency of the flow. However, those scalings provide a good prediction of two other governing parameters’ dependency, the Rayleigh number and the aspect ratio. Therefore, an improved scaling analysis using a triple-layer integral approach and direct numerical simulations have been performed for the natural convection boundary layer along a semi-infinite flat plate with uniform surface heat flux. This heat flux is a ramp function of time, where the temperature gradient on the surface increases with time up to some specific time and then remains constant. The growth of the boundary layer strongly depends on the ramp time. If the ramp time is sufficiently long, the boundary layer reaches a quasi steady mode before the growth of the temperature gradient is completed. In this mode, the thermal boundary layer at first grows in thickness and then contracts with increasing time. However, if the ramp time is sufficiently short, the boundary layer develops differently, but after the wall temperature gradient growth is completed, the boundary layer develops as though the startup had been instantaneous.

A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in Cloud computing

A composite SaaS (Software as a Service) is a software that is comprised of several software components and data components. The composite SaaS placement problem is to determine where each of the components should be deployed in a cloud computing environment such that the performance of the composite SaaS is optimal. From the computational point of view, the composite SaaS placement problem is a large-scale combinatorial optimization problem. Thus, an Iterative Cooperative Co-evolutionary Genetic Algorithm (ICCGA) was proposed. The ICCGA can find reasonable quality of solutions. However, its computation time is noticeably slow. Aiming at improving the computation time, we propose an unsynchronized Parallel Cooperative Co-evolutionary Genetic Algorithm (PCCGA) in this paper. Experimental results have shown that the PCCGA not only has quicker computation time, but also generates better quality of solutions than the ICCGA.

Prandtl number scaling of natural convection of the flow on an inclined at plate due to uniform surface heat flux

A new scaling analysis has been performed for the unsteady natural convection boundary layer under a downward facing inclined plate with uniform heat flux. The development of the thermal or viscous boundary layers may be classified into three distinct stages including an early stage, a transitional stage and a steady stage, which can be clearly identified in the analytical as well as numerical results. Earlier scaling shows that the existing scaling laws of the boundary layer thickness, velocity and steady state time scales for the natural convection flow on a heated plate of uniform heat flux provide a very poor prediction of the Prandtl number dependency. However, those scalings performed very well with Rayleigh number and aspect ratio dependency. In this study, a modifed Prandtl number scaling has been developed using a triple-layer integral approach for Pr > 1. It is seen that in comparison to the direct numerical simulations, the new scaling performs considerably better than the previous scaling.

Experimental studies of gypsum plasterboards and composite panels under fire conditions

Gypsum plasterboards are commonly used to protect the light gauge steel-framed walls in buildings from fires. Single or multiple plasterboards can be used for this purpose, whereas recent research has proposed a composite panel with a layer of external insulation between two plasterboards. However, a good understanding of the thermal behaviour of these plasterboard panels under fire conditions is not known. Therefore, 15 small-scale fire tests were conducted on plasterboard panels made of 13 and 16 mm plasterboards and four different types of insulations with varying thickness and density subject to standard fire conditions in AS 1530.4. Fire performance of single and multiple layers of gypsum plasterboards was assessed including the effects of interfaces between adjacent plasterboards. Effects of using external insulations such as glass fibre, rockwool and cellulose fibre were also determined. The thermal performance of composite panels developed from different insulating materials of varying densities and thicknesses was examined and compared. This paper presents the details of the fire tests conducted in this study and their valuable time–temperature data for the tested plasterboard panels. These data can be used for the purpose of developing and validating accurate thermal numerical models of these panels.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Measurement of in-service insulated rail joint tie plate response

This paper presents the results of a recent investigation into Insulated Rail Joint Tie Plate fatigue failures. In particular it focuses on the results of data obtained through field strain gauge and accelerometer measurements of in-service Insulated Rail Joint Tie Plates. These measurements have identified a significant variability in the strains present in similar joints operating under identical load conditions. This variability in stress invariably has a significant influence on the life of the joints. The results of rainflow counting and a fatigue analysis are also presented.