Making the case for improved structural design : tornado outbreaks of 2011

A total of 1,625 tornadoes occurred in the United States in 2011, resulting in economic losses that exceeded $25 billion. Two tornado outbreaks stand out because they caused more than half of those losses. The tornadoes that cut through Tuscaloosa, Alabama, on April 27 and Joplin, Missouri, on May 22 were responsible for a combined 223 fatalities and more than 13,000 damaged buildings in the two cities. Although the economic losses associated with tornado damage are well documented, the writers argue that the overall impact should encompass longer term, broader considerations such as the social disruption and psychological effects that impact communities. This paper examines observations by tornado damage assessment teams led by the first author in these two medium-sized cities and suggests that the evolution of building codes and past approaches to construction have led to conditions that made this extent of damage possible. The authors outline a multidisciplinary path forward that incorporates engineering research and social and economic studies into a new design paradigm leading to building code changes and social practices that will improve resistance and mitigate future losses at a community level from tornadoes.

Vibration based baseline updating method to localize crack formation and propagation in reinforced concrete members

Structural Health Monitoring (SHM) schemes are useful for proper management of the performance of structures and for preventing their catastrophic failures. Vibration based SHM schemes has gained popularity during the past two decades resulting in significant research. It is hence evitable that future SHM schemes will include robust and automated vibration based damage assessment techniques (VBDAT) to detect, localize and quantify damage. In this context, the Damage Index (DI) method which is classified as non-model or output based VBDAT, has the ability to automate the damage assessment process without using a computer or numerical model along with actual measurements. Although damage assessment using DI methods have been able to achieve reasonable success for structures made of homogeneous materials such as steel, the same success level has not been reported with respect to Reinforced Concrete (RC) structures. The complexity of flexural cracks is claimed to be the main reason to hinder the applicability of existing DI methods in RC structures. Past research also indicates that use of a constant baseline throughout the damage assessment process undermines the potential of the Modal Strain Energy based Damage Index (MSEDI). To address this situation, this paper presents a novel method that has been developed as part of a comprehensive research project carried out at Queensland University of Technology, Brisbane, Australia. This novel process, referred to as the baseline updating method, continuously updates the baseline and systematically tracks both crack formation and propagation with the ability to automate the damage assessment process using output only data. The proposed method is illustrated through examples and the results demonstrate the capability of the method to achieve the desired outcomes.

Evaluation of material crack using acoustic emission technique

Cracks in civil structures can result in premature failure due to material degradation and can result in both financial loss and environmental consequences. This thesis reports an effective technique using Acoustic Emission (AE) technique to assess the severity of the crack propagation in steel structures. The outcome of this work confirms that combination of AE parametric analysis and signal processing techniques can be used to evaluate crack propagation under different loading configurations. The technique has potential application to assess and monitor the condition of civil structures.

Decision fusion for reliable flood mapping using remote sensing images

Flood extent mapping is a basic tool for flood damage assessment, which can be done by digital classification techniques using satellite imageries, including the data recorded by radar and optical sensors. However, converting the data into the information we need is not a straightforward task. One of the great challenges involved in the data interpretation is to separate the permanent water bodies and flooding regions, including both the fully inundated areas and the wet areas where trees and houses are partly covered with water. This paper adopts the decision fusion technique to combine the mapping results from radar data and the NDVI data derived from optical data. An improved capacity in terms of identifying the permanent or semi-permanent water bodies from flood inundated areas has been achieved. Computer software tools Multispec and Matlab were used.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

Review of vibration-based damage detection and condition assessment of bridge structures using structural health monitoring

As a part of vital infrastructure and transportation networks, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs always make the infrastructure owners difficult to undertake. Structural health monitoring (SHM) is set to assess condition and foresee probable failures of designated bridge(s), so as to monitor the structural health of the bridges. The SHM systems proposed recently are incorporated with Vibration-Based Damage Detection (VBDD) techniques, Statistical Methods and Signal processing techniques and have been regarded as efficient and economical ways to solve the problem. The recent development in damage detection and condition assessment techniques based on VBDD and statistical methods are reviewed. The VBDD methods based on changes in natural frequencies, curvature/strain modes, modal strain energy (MSE) dynamic flexibility, artificial neural networks (ANN) before and after damage and other signal processing methods like Wavelet techniques and empirical mode decomposition (EMD) / Hilbert spectrum methods are discussed here.

Structural health monitoring : vibration-based damage detection and condition assessment of bridges

Structural health is a vital aspect of infrastructure sustainability. As a part of a vital infrastructure and transportation network, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs are a difficult burden for infrastructure owners. The structural health monitoring (SHM) systems proposed recently are incorporated with vibration-based damage detection techniques, statistical methods and signal processing techniques and have been regarded as efficient and economical ways to assess bridge condition and foresee probable costly failures. In this chapter, the recent developments in damage detection and condition assessment techniques based on vibration-based damage detection and statistical methods are reviewed. The vibration-based damage detection methods based on changes in natural frequencies, curvature or strain modes, modal strain energy, dynamic flexibility, artificial neural networks, before and after damage, and other signal processing methods such as Wavelet techniques, empirical mode decomposition and Hilbert spectrum methods are discussed in this chapter.

Assessment of damages for property damage

In Gagner Pty t/as Indochine Café v Canturi Corporation Pty Ltd (2009) 262 ALR 691, the assessment of damages awarded for the rectification work to the premises of the respondent was in issue. The appellant operated a restaurant above the respondent’s jewellery store in Sydney. When the kitchen of the restaurant flooded, water escaped causing damage to the jewellery store’s fit-out. The escape of the water was held to be due to the negligence of persons for whom the appellant was vicariously liable. The trial judge awarded damages, measured by the amount required to return the premises as close as was possible to the condition prior to the flood damage as well as an allowance for interruption to the business for 10 days. The 10 day allowance reflected the number of days the store would have been closed for if it was to be returned to its previous condition. The evidence was that the flooding has only affected approximately 10% of the floor area of the store. However, instead of having work carried out to bring the premises back to its condition as before the water damage, the respondent closed the business for 29 working days for a complete internal refurbishment – at a cost substantially more than simple rectification. On appeal it was argued that the trial judge had assessed the damages incorrectly as by undertaking a complete refurbishment had the effect that the respondent did not suffer any loss as a consequence of the negligence in relation to the fit-out. It was asserted that the claim for damages was in the circumstances a claim for betterment. It was also argued that the damages should not include a component for GST. Campbell JA gave reasons, with Macfarlan JA and Sackville AJA agreeing.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Case studies on vibration based damage identification : multi-criteria approach

Vibration based damage identification methods examine the changes in primary modal parameters or quantities derived from modal parameters. As one method may have advantages over the other under some circumstances, a multi-criteria approach is proposed. Case studies are conducted separately on beam, plate and plate-on-beam structures. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on flexibility and strain energy changes before and after damage are obtained and used as the indices for the assessment of the state of structural health. Results show that the proposed multi-criteria method is effective in damage identification in these structures.

A cell migration device that maintains a defined surface with no cellular damage during wound edge generation

Studying the rate of cell migration provides insight into fundamental cell biology as well as a tool to assess the functionality of synthetic surfaces and soluble environments used in tissue engineering. The traditional tools used to study cell migration include the fence and wound healing assays. In this paper we describe the development of a microchannel based device for the study of cell migration on defined surfaces. We demonstrate that this device provides a superior tool, relative to the previously mentioned assays, for assessing the propagation rate of cell wave fronts. The significant advantage provided by this technology is the ability to maintain a virgin surface prior to the commencement of the cell migration assay. Here, the device is used to assess rates of mouse fibroblasts (NIH 3T3) and human osteosarcoma (SaOS2) cell migration on surfaces functionalized with various extracellular matrix proteins as a demonstration that confining cell migration within a microchannel produces consistent and robust data. The device design enables rapid and simplistic assessment of multiple repeats on a single chip, where surfaces have not been previously exposed to cells or cellular secretions.

Vulnerability assessment of reinforced concrete columns subjected to vehicular impacts

Columns are one of the key load bearing elements that are highly susceptible to vehicle impacts. The resulting severe damages to columns may leads to failures of the supporting structure that are catastrophic in nature. However, the columns in existing structures are seldom designed for impact due to inadequacies of design guidelines. The impact behaviour of columns designed for gravity loads and actions other than impact is, therefore, of an interest. A comprehensive investigation is conducted on reinforced concrete column with a particular focus on investigating the vulnerability of the exposed columns and to implement mitigation techniques under low to medium velocity car and truck impacts. The investigation is based on non-linear explicit computer simulations of impacted columns followed by a comprehensive validation process. The impact is simulated using force pulses generated from full scale vehicle impact tests. A material model capable of simulating triaxial loading conditions is used in the analyses. Circular columns adequate in capacity for five to twenty story buildings, designed according to Australian standards are considered in the investigation. The crucial parameters associated with the routine column designs and the different load combinations applied at the serviceability stage on the typical columns are considered in detail. Axially loaded columns are examined at the initial stage and the investigation is extended to analyse the impact behaviour under single axis bending and biaxial bending. The impact capacity reduction under varying axial loads is also investigated. Effects of the various load combinations are quantified and residual capacity of the impacted columns based on the status of the damage and mitigation techniques are also presented. In addition, the contribution of the individual parameter to the failure load is scrutinized and analytical equations are developed to identify the critical impulses in terms of the geometrical and material properties of the impacted column. In particular, an innovative technique was developed and introduced to improve the accuracy of the equations where the other techniques are failed due to the shape of the error distribution. Above all, the equations can be used to quantify the critical impulse for three consecutive points (load combinations) located on the interaction diagram for one particular column. Consequently, linear interpolation can be used to quantify the critical impulse for the loading points that are located in-between on the interaction diagram. Having provided a known force and impulse pair for an average impact duration, this method can be extended to assess the vulnerability of columns for a general vehicle population based on an analytical method that can be used to quantify the critical peak forces under different impact durations. Therefore the contribution of this research is not only limited to produce simplified yet rational design guidelines and equations, but also provides a comprehensive solution to quantify the impact capacity while delivering new insight to the scientific community for dealing with impacts.

Assessment of retinal structure and visual function in association with diabetic peripheral neuropathy

Diabetes is an increasingly prevalent disease worldwide. Providing early management of the complications can prevent morbidity and mortality in this population. Peripheral neuropathy, a significant complication of diabetes, is the major cause of foot ulceration and amputation in diabetes. Delay in attending to complication of the disease contributes to significant medical expenses for diabetic patients and the community. Early structural changes to the neural components of the retina have been demonstrated to occur prior to the clinically visible retinal vasculature complication of diabetic retinopathy. Additionally visual functionloss has been shown to exist before the ophthalmoscopic manifestations of vasculature damage. The purpose of this thesis was to evaluate the relationship between diabetic peripheral neuropathy and both retinal structure and visual function. The key question was whether diabetic peripheral neuropathy is the potential underlying factor responsible for retinal anatomical change and visual functional loss in people with diabetes. This study was conducted on a cohort with type 2 diabetes. Retinal nerve fibre layer thickness was assessed by means of Optical Coherence Tomography (OCT). Visual function was assessed using two different methods; Standard Automated Perimetry (SAP) and flicker perimetry were performed within the central 30 degrees of fixation. The level of diabetic peripheral neuropathy (DPN) was assessed using two techniques - Quantitative Sensory Testing and Neuropathy Disability Score (NDS). These techniques are known to be capable of detecting DPN at very early stages. NDS has also been shown as a gold standard for detecting 'risk of foot ulceration'. Findings reported in this thesis showed that RNFL thickness, particularly in the inferior quadrant, has a significant association with severity of DPN when the condition has been assessed using NDS. More specifically it was observed that inferior RNFL thickness has the ability to differentiate individuals who are at higher risk of foot ulceration from those who are at lower risk, indicating that RNFL thickness can predict late-staged DPN. Investigating the association between RNFL and QST did not show any meaningful interaction, which indicates that RNFL thickness for this cohort was not as predictive of neuropathy status as NDS. In both of these studies, control participants did not have different results from the type 2 cohort who did not DPN suggesting that RNFL thickness is not a marker for diagnosing DPN at early stages. The latter finding also indicated that diabetes per se, is unlikely to affect the RNFL thickness. Visual function as measured by SAP and flicker perimetry was found to be associated with severity of peripheral neuropathy as measured by NDS. These findings were also capable of differentiating individuals at higher risk of foot ulceration; however, visual function also proved not to be a maker for early diagnosis of DPN. It was found that neither SAP, nor flicker sensitivity have meaningful associations with DPN when neuropathy status was measured using QST. Importantly diabetic retinopathy did not explain any of the findings in these experiments. The work described here is valuable as no other research to date has investigated the association between diabetic peripheral neuropathy and either retinal structure or visual function.