Novel corrosion sensor based on carbon nanotube composites for structural health monitoring

Bridges are important infrastructures of all nations and are required for transportation of goods as well as human. A catastrophic failure can result in loss of lives and enormous financial hardship to the nation. Hence, there is an urgent need to monitor our infrastructures to prolong their life span, at the same time catering for heavier and faster moving traffics. Although various kinds of sensors are now available to monitor the health of the structures due to corrosion, they do not provide permanent and long term measurements. This paper investigates the fabrication of Carbon Nanotube (CNT) based composite sensors for structural health monitoring. The CNTs, a key material in nanotechnology has aroused great interest in the research community due to their remarkable mechanical, electrochemical, piezoresistive and other physical properties. Multi-wall CNT (MWCNT)/Nafion composite sensors were fabricated to evaluate their electrical properties when subjected to chemical solutions, to simulate a chemical reaction due to corrosion and real life corrosion experimental tests. The electrical resistance of the sensor electrode was dramatically changed due to corrosion. The novel sensor is expected to effectively detect corrosion in structures based on the measurement of electrical impedances of the CNT composite.

A review of optimization techniques used in the design of fibre composite structures for civil engineering applications

Fibre composite structures have become the most attractive candidate for civil engineering applications. Fibre reinforced plastic polymer (FRP) composite materials have been used in the rehabilitation and replacement of the old degrading traditional structures or build new structures. However, the lack of design standards for civil infrastructure limits their structural applications. The majority of the existing applications have been designed based on the research and guidelines provided by the fibre composite manufacturers or based on the designer’s experience. It has been a tendency that the final structure is generally over-designed. This paper provides a review on the available studies related to the design optimization of fibre composite structures used in civil engineering such as; plate, beam, box beam, sandwich panel, bridge girder, and bridge deck. Various optimization methods are presented and compared. In addition, the importance of using the appropriate optimization technique is discussed. An improved methodology, which considering experimental testing, numerical modelling, and design constrains, is proposed in the paper for design optimization of composite structures.

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.

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.

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.

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.

Lateral movements in composite high-rise buildings under seismic action

Daring human nature has already led to the construction of high-rise buildings in naturally challenging geological regions and in worse environments of the world. However; literature review divulges that there is a lag in research of certain generic principles and rules for the prediction of lateral movement in multistorey construction. The present competitive trend orders the best possible used of available construction material and resources. Hence; the mixed used of reinforced concrete with structural steel is gaining prevalence day by day. This paper investigates the effects of Seismic load on composite multistorey building provided with core wall and trusses through FEM modelling. The results showed that increased rigidity corresponds to lower period of vibration and hence higher seismic forces. Since Seismic action is a function of mass and response acceleration, therefore; mass increment generate higher earthquake load and thus cause higher impact base shear and overturning movement. Whereas; wind force depends on building exposed, larger the plan dimension greater is the wind impact. Nonetheless; outriggers trusses noticeably contribute, in improving the serviceability of structure subjected to wind and earthquake forces.

Characterisation of multi wall carbon nanotube–polymer composites for strain sensing applications

Carbon nanotubes (CNTs) have excellent electrical, mechanical and electromechanical properties. When CNTs are incorporated into polymers, electrically conductive composites with high electrical conductivity at very low CNT content (often below 1% wt CNT) result. Due to the change in electrical properties under mechanical load, carbon nanotube/polymer composites have attracted significant research interest especially due to their potential for application in in-situ monitoring of stress distribution and active control of strain sensing in composite structures or as strain sensors. To sucessfully develop novel devices for such applications, some of the major challenges that need to be overcome include; in-depth understanding of structure-electrical conductivity relationships, response of the composites under changing environmental conditions and piezoresistivity of different types of carbon nanotube/polymer sensing devices. In this thesis, direct current (DC) and alternating current (AC) conductivity of CNT-epoxy composites was investigated. Details of microstructure obtained by scanning electron microscopy were used to link observed electrical properties with structure using equivalent circuit modeling. The role of polymer coatings on macro and micro level electrical conductivity was investigated using atomic force microscopy. Thermal analysis and Raman spectroscopy were used to evaluate the heat flow and deformation of carbon nanotubes embedded in the epoxy, respectively, and related to temperature induced resistivity changes. A comparative assessment of piezoresistivity was conducted using randomly mixed carbon nanotube/epoxy composites, and new concept epoxy- and polyurethane-coated carbon nanotube films. The results indicate that equivalent circuit modelling is a reliable technique for estimating values of the resistance and capacitive components in linear, low aspect ratio-epoxy composites. Using this approach, the dominant role of tunneling resistance in determining the electrical conductivity was confirmed, a result further verified using conductive-atomic force microscopy analysis. Randomly mixed CNT-epoxy composites were found to be highly sensitive to mechanical strain and temperature variation compared to polymer-coated CNT films. In the vicinity of the glass transition temperature, the CNT-epoxy composites exhibited pronounced resistivity peaks. Thermal and Raman spectroscopy analyses indicated that this phenomenon can be attributed to physical aging of the epoxy matrix phase and structural rearrangement of the conductive network induced by matrix expansion. The resistivity of polymercoated CNT composites was mainly dominated by the intrinsic resistivity of CNTs and the CNT junctions, and their linear, weakly temperature sensitive response can be described by a modified Luttinger liquid model. Piezoresistivity of the polymer coated sensors was dominated by break up of the conducting carbon nanotube network and the consequent degradation of nanotube-nanotube contacts while that of the randomly mixed CNT-epoxy composites was determined by tunnelling resistance between neighbouring CNTs. This thesis has demonstrated that it is possible to use microstructure information to develop equivalent circuit models that are capable of representing the electrical conductivity of CNT/epoxy composites accurately. New designs of carbon nanotube based sensing devices, utilising carbon nanotube films as the key functional element, can be used to overcome the high temperature sensitivity of randomly mixed CNT/polymer composites without compromising on desired high strain sensitivity. This concept can be extended to develop large area intelligent CNT based coatings and targeted weak-point specific strain sensors for use in structural health monitoring.

Investigation of sensitivity based load ranking with composite load model in power system

Load modeling plays an important role in power system dynamic stability assessment. One of the widely used methods in assessing load model impact on system dynamic response is through parametric sensitivity analysis. Load ranking provides an effective measure of such impact. Traditionally, load ranking is based on either static or dynamic load model alone. In this paper, composite load model based load ranking framework is proposed. It enables comprehensive investigation into load modeling impacts on system stability considering the dynamic interactions between load and system dynamics. The impact of load composition on the overall sensitivity and therefore on ranking of the load is also investigated. Dynamic simulations are performed to further elucidate the results obtained through sensitivity based load ranking approach.