Damage quantification techniques in acoustic emission monitoring

Acoustic emission (AE) analysis is one of the several diagnostic techniques available nowadays for structural health monitoring (SHM) of engineering structures. Some of its advantages over other techniques include high sensitivity to crack growth and capability of monitoring a structure in real time. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). In AE technique, these stress waves are recorded by means of suitable sensors placed on the surface of a structure. Recorded signals are subsequently analysed to gather information about the nature of the source. By enabling early detection of crack growth, AE technique helps in planning timely retrofitting or other maintenance jobs or even replacement of the structure if required. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. Large amount of data is generated during AE testing, hence effective data analysis is necessary, especially for long term monitoring uses. Appropriate analysis of AE data for quantification of damage level is an area that has received considerable attention. Various approaches available for damage quantification for severity assessment are discussed in this paper, with special focus on civil infrastructure such as bridges. One method called improved b-value analysis is used to analyse data collected from laboratory testing.

Condition deterioration prediction of bridge elements using Dynamic Bayesian Networks (DBNs)

The ability of bridge deterioration models to predict future condition provides significant advantages in improving the effectiveness of maintenance decisions. This paper proposes a novel model using Dynamic Bayesian Networks (DBNs) for predicting the condition of bridge elements. The proposed model improves prediction results by being able to handle, deterioration dependencies among different bridge elements, the lack of full inspection histories, and joint considerations of both maintenance actions and environmental effects. With Bayesian updating capability, different types of data and information can be utilised as inputs. Expert knowledge can be used to deal with insufficient data as a starting point. The proposed model established a flexible basis for bridge systems deterioration modelling so that other models and Bayesian approaches can be further developed in one platform. A steel bridge main girder was chosen to validate the proposed model.

R&D investment in green building initiatives in Western Australia

This paper reports on a current case study of green building initiatives implemented by the Western Australian government in the past decade. The intent is to provide a qualitative understanding of past R&D investments in the Australian built environment. The case method was selected to illustrate three sector-based investments, one of which is reported on here. The conceptual framework underpinning interview design and data analysis uses dynamic capability, absorptive capacity and open innovation theories to better understand the organisational environment in which these initiatives were implemented. Data has been thematically coded to criteria identified from the literature to illustrate organisational characteristics which may have contributed to dissemination and impact. The results will be combined with two further case studies (construction safety and digital modelling), to inform this research. This industry supported project will conclude by developing policy guidelines for future R&D investment in the built environment.

Raman spectroscopic study of the mineral xonotlite Ca 6Si 6O 17(OH) 2-A component of plaster boards

The mineral xonotlite Ca 6Si 6O 17(OH) 2 is a crystalline calcium silicate hydrate which is widely used in plaster boards and in many industrial applications. The structure of xonotlite is best described as having a dreierdoppelketten silicate structure, and describes the repeating silicate trimer which forms the silicate chains, and doppel indicating that two chains combine. Raman bands at 1042 and 1070 cm -1 are assigned to the SiO stretching vibrations of linked units of Si 4O 11 units. Raman bands at 961 and 980 cm -1 serve to identify Si 3O 10 units. The broad Raman band at 862 cm -1 is attributed to hydroxyl deformation modes. Intense Raman bands at 593 and 695 cm -1 are assigned to OSiO bending vibrations. Intense Raman bands at 3578, 3611, 3627 and 3665 cm -1 are assigned to OH stretching vibrations of the OH units in xonotlite. Infrared spectra are in harmony with the Raman spectra. Raman spectroscopy with complimentary infrared spectroscopy enables the characterisation of the building material xonotlite.

Combined VEGF and PDGF treatment reduces secondary degeneration after spinal cord injury

Trauma to the spinal cord creates an initial physical injury damaging neurons, glia, and blood vessels, which then induces a prolonged inflammatory response, leading to secondary degeneration of spinal cord tissue, and further loss of neurons and glia surrounding the initial site of injury. Angiogenesis is a critical step in tissue repair, but in the injured spinal cord angiogenesis fails; blood vessels formed initially later regress. Stabilizing the angiogenic response is therefore a potential target to improve recovery after spinal cord injury (SCI). Vascular endothelial growth factor (VEGF) can initiate angiogenesis, but cannot sustain blood vessel maturation. Platelet-derived growth factor (PDGF) can promote blood vessel stability and maturation. We therefore investigated a combined application of VEGF and PDGF as treatment for traumatic spinal cord injury, with the aim to reduce secondary degeneration by promotion of angiogenesis. Immediately after hemisection of the spinal cord in the rat we delivered VEGF and PDGF and to the injury site. One and 3 months later the size of the lesion was significantly smaller in the treated group compared to controls, and there was significantly reduced gliosis surrounding the lesion. There was no significant effect of the treatment on blood vessel density, although there was a significant reduction in the numbers of macrophages/microglia surrounding the lesion, and a shift in the distribution of morphological and immunological phenotypes of these inflammatory cells. VEGF and PDGF delivered singly exacerbated secondary degeneration, increasing the size of the lesion cavity. These results demonstrate a novel therapeutic intervention for SCI, and reveal an unanticipated synergy for these growth factors whereby they modulated inflammatory processes and created a microenvironment conducive to axon preservation/sprouting.

Effective discrimination of acoustic emission source signals for structural health monitoring

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage‐related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests.

Characterisation of flexural bond strength in thin bed concrete masonry

This paper presents an experimental investigation of the flexural bond strength of thin bed concrete masonry. Flexural bond strength of masonry depends upon the mortar type, the techniques of dispersion of mortar and the surface texture (roughness) of concrete blocks. There exists an abundance of literature on the conventional masonry bond containing 10mm thick mortar; however, the 2mm polymer flue mortar bond is not yet well researched. This paper reports a study on the examination of the effect of mortar compositions, dispersion methods and unit surface textures to the flexural bond strength of thin bed concrete masonry. Three types of polymer modified glue mortars, three surface textures and four techniques of mortar dispersion have been used in preparing 108 four point flexural test specimens. All mortar joints have been carefully prepared to ensure achievement of 2mm layer polymer mortar thickness on average. The results exhibit the flexural bond strength of thin bed concrete masonry much is higher than that of the conventional masonry; moreover the unit surface texture and the mortar dispersion methods are found to have significant influence on the flexural bond strength.

Analysis of Rail Ends under Wheel Contact Loading

The effect of the discontinuity of the rail ends and the presence of lower modulus insulation material at the gap to the variations of stresses in the insulated rail joint (IRJ) is presented. A three-dimensional wheel – rail contact model in the finite element framework is used for the analysis. It is shown that the maximum stress occurs in the subsurface of the railhead when the wheel contact occurs far away from the rail end and migrates to the railhead surface as the wheel approaches the rail end; under this condition, the interface between the rail ends and the insulation material has suffered significantly increased levels of stress concentration. The ratio of the elastic modulus of the railhead and insulation material is found to alter the levels of stress concentration. Numerical result indicates that a higher elastic modulus insulating material can reduce the stress concentration in the railhead but will generate higher stresses in the insulation material, leading to earlier failure of the insulation material

Experimental studies of non-load bearing steel wall systems under fire conditions

Fire safety of buildings has been recognised as very important by the building industry and the community at large. Traditionally, increased fire rating is provided by simply adding more plasterboards to light gauge steel frame (LSF) walls, which is inefficient. Many research studies have been undertaken to investigate the thermal behaviour of traditional LSF stud wall systems under standard fire conditions. However, no research has been undertaken on the thermal behaviour of LSF stud walls using the recently proposed composite panel. Extensive fire testing of both non-load bearing and load bearing wall panels was conducted in this research based on the standard time-temperature curve in AS1530.4. Three groups of LSF wall specimens were tested with no insulation, cavity insulation and the new composite panel based on an external insulation layer between plasterboards. This paper presents the details of this experimental study into the thermal performance of non-load bearing walls lined with various configurations of plasterboard and insulation. Extensive descriptive and numerical results of the tested non-load bearing wall panels given in this paper provide a thorough understanding of their thermal behaviour, and valuable time-temperature data that can be used to validate numerical models. Test results showed that the innovative composite stud wall systems outperformed the traditional stud wall systems in terms of their thermal performance, giving a much higher fire rating.

Numerical solution of stress intensity factors of multiple cracks in great number with eigen COD boundary integral equations

Based on the eigen crack opening displacement (COD) boundary integral equations, a newly developed computational approach is proposed for the analysis of multiple crack problems. The eigen COD particularly refers to a crack in an infinite domain under fictitious traction acting on the crack surface. With the concept of eigen COD, the multiple cracks in great number can be solved by using the conventional displacement discontinuity boundary integral equations in an iterative fashion with a small size of system matrix. The interactions among cracks are dealt with by two parts according to the distances of cracks to the current crack. The strong effects of cracks in adjacent group are treated with the aid of the local Eshelby matrix derived from the traction BIEs in discrete form. While the relatively week effects of cracks in far-field group are treated in the iteration procedures. Numerical examples are provided for the stress intensity factors of multiple cracks, up to several thousands in number, with the proposed approach. By comparing with the analytical solutions in the literature as well as solutions of the dual boundary integral equations, the effectiveness and the efficiencies of the proposed approach are verified.

Integrated sustainable urban infrastructure management : the Brisbane urban growth model

Sustainable urban development and the liveability of a city are increasingly important issues in the context of land use planning and infrastructure management. In recent years, the promotion of sustainable urban development in Australia and overseas is facing various physical, socioeconomic and environmental challenges. These challenges and problems arise from the lack of capability of local governments to accommodate the needs of the population and economy in a relatively short timeframe. The planning of economic growth and development is often dealt with separately and not included in the conventional land use planning process. There is also a sharp rise in the responsibilities and roles of local government for infrastructure planning and management. This increase in responsibilities means that local elected officials and urban planners have less time to prepare background information and make decisions. The Brisbane Urban Growth Model has proven initially successful in providing a dynamic platform to ensure timely and coordinated delivery of urban infrastructure. Most importantly, this model is the first step for local governments in moving toward a systematic approach to pursuing sustainable and effective urban infrastructure management.

Traffic and climate change impacts on water quality : measuring build-up and wash-off of heavy metals and petroleum hydrocarbons

Understanding the impacts of traffic and climate change on water quality helps decision makers to develop better policy and plans for dealing with unsustainable urban and transport development. This chapter presents detailed methodologies developed for sample collection and testing for heavy metals and total petroleum hydrocarbons, as part of a research study to investigate the impacts of climate change and changes to urban traffic characteristics on pollutant build-up and wash-off from urban road surfaces. Cadmium, chromium, nickel, copper, lead, iron, aluminium, manganese and zinc were the target heavy metals, and selected gasoline and diesel range organics were the target total petroleum hydrocarbons for this study. The study sites were selected to encompass the urban traffic characteristics of the Gold Coast region, Australia. An improved sample collection method referred to as ‘the wet and dry vacuum system’ for the pollutant build-up, and an effective wash-off plan to incorporate predicted changes to rainfall characteristics due to climate change, were implemented. The novel approach to sample collection for pollutant build-up helped to maintain the integrity of collection efficiency. The wash-off plan helped to incorporate the predicted impacts of climate change in the Gold Coast region. The robust experimental methods developed will help in field sample collection and chemical testing of different stormwater pollutants in build-up and wash-off.

A particle-based micromechanics approach to simulate structural changes of plant cells during drying

This paper is concerned with applying a particle-based approach to simulate the micro-level cellular structural changes of plant cells during drying. The objective of the investigation was to relate the micro-level structural properties such as cell area, diameter and perimeter to the change of moisture content of the cell. Model assumes a simplified cell which consists of two basic components, cell wall and cell fluid. The cell fluid is assumed to be a Newtonian fluid with higher viscosity compared to water and cell wall is assumed to be a visco-elastic solid boundary located around the cell fluid. Cell fluid is modelled with Smoothed Particle Hydrodynamics (SPH) technique and for the cell wall; a Discrete Element Method (DEM) is used. The developed model is two-dimensional, but accounts for three-dimensional physical properties of real plant cells. Drying phenomena is simulated as fluid mass reductions and the model is used to predict the above mentioned structural properties as a function of cell fluid mass. Model predictions are found to be in fairly good agreement with experimental data in literature and the particle-based approach is demonstrated to be suitable for numerical studies of drying related structural deformations. Also a sensitivity analysis is included to demonstrate the influence of key model parameters to model predictions.

A multivariate approach to the identification of surrogate parameters for heavy metals in stormwater

Stormwater is a potential and readily available alternative source for potable water in urban areas. However, its direct use is severely constrained by the presence of toxic pollutants, such as heavy metals (HMs). The presence of HMs in stormwater is of concern because of their chronic toxicity and persistent nature. In addition to human health impacts, metals can contribute to adverse ecosystem health impact on receiving waters. Therefore, the ability to predict the levels of HMs in stormwater is crucial for monitoring stormwater quality and for the design of effective treatment systems. Unfortunately, the current laboratory methods for determining HM concentrations are resource intensive and time consuming. In this paper, applications of multivariate data analysis techniques are presented to identify potential surrogate parameters which can be used to determine HM concentrations in stormwater. Accordingly, partial least squares was applied to identify a suite of physicochemical parameters which can serve as indicators of HMs. Datasets having varied characteristics, such as land use and particle size distribution of solids, were analyzed to validate the efficacy of the influencing parameters. Iron, manganese, total organic carbon, and inorganic carbon were identified as the predominant parameters that correlate with the HM concentrations. The practical extension of the study outcomes to urban stormwater management is also discussed.