Infrastructure Engineering - Research Publications
Now showing items 1-12 of 1294
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
(OXFORD UNIV PRESS, 2019-10-29)
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.
Improved Shear Strength Performance of Compacted Rubberized Clays Treated with Sodium Alginate Biopolymer
This study examines the potential use of sodium alginate (SA) biopolymer as an environmentally sustainable agent for the stabilization of rubberized soil blends prepared using a high plasticity clay soil and tire-derived ground rubber (GR). The experimental program consisted of uniaxial compression and scanning electron microscopy (SEM) tests; the former was performed on three soil-GR blends (with GR-to-soil mass ratios of 0%, 5% and 10%) compacted (and cured for 1, 4, 7 and 14 d) employing distilled water and three SA solutions-prepared at SA-to-water (mass-to-volume) dosage ratios of 5, 10 and 15 g/L-as the compaction liquid. For any given GR content, the greater the SA dosage and/or the longer the curing duration, the higher the uniaxial compressive strength (UCS), with only minor added benefits beyond seven days of curing. This behaviour was attributed to the formation and propagation of so-called "cationic bridges" (developed as a result of a "Ca2+/Mg2+ ⟷ Na+ cation exchange/substitution" process among the clay and SA components) between adjacent clay surfaces over time, inducing flocculation of the clay particles. This clay amending mechanism was further verified by means of representative SEM images. Finally, the addition of (and content increase in) GR-which translates to partially replacing the soil clay content with GR particles and hence reducing the number of available attraction sites for the SA molecules to form additional cationic bridges-was found to moderately offset the efficiency of SA treatment.
Ensemble model for estimating continental-scale patterns of human movement: a case study of Australia
(NATURE RESEARCH, 2021-02-26)
Understanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.
Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy
The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.
The Potential Use of Hypochlorous Acid and a Smart Prefabricated Sanitising Chamber to Reduce Occupation-Related COVID-19 Exposure
(DOVE MEDICAL PRESS LTD, 2021-01-01)
This work is part of a project on the development of a smart prefabricated sanitising chamber (SPSC) to provide extra measures against the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Stabilised hypochlorous acid (HOCl) is an approved disinfectant against SARS-CoV-2 by the Environmental Protection Association US in its liquid form on non-porous surfaces. This review is extended to cover its viricidal/bactericidal efficacy in aerosolised or sprayed form which showed an effective dose of as low as 20 ppm and the exposure duration of at least 60 s. The aerosolised application was also recommended with particle size of less than 200 μm to increase the contact with pathogens. The review also includes the safety and toxicity of HOCl with different concentrations. The review calls for more investigations into the effect of HOCl in mist and fog form on the respiratory system when transitioning through the proposed SPSC.
Risk mapping for COVID-19 outbreaks in Australia using mobility data
(ROYAL SOC, 2021-01-27)
COVID-19 is highly transmissible and containing outbreaks requires a rapid and effective response. Because infection may be spread by people who are pre-symptomatic or asymptomatic, substantial undetected transmission is likely to occur before clinical cases are diagnosed. Thus, when outbreaks occur there is a need to anticipate which populations and locations are at heightened risk of exposure. In this work, we evaluate the utility of aggregate human mobility data for estimating the geographical distribution of transmission risk. We present a simple procedure for producing spatial transmission risk assessments from near-real-time population mobility data. We validate our estimates against three well-documented COVID-19 outbreaks in Australia. Two of these were well-defined transmission clusters and one was a community transmission scenario. Our results indicate that mobility data can be a good predictor of geographical patterns of exposure risk from transmission centres, particularly in outbreaks involving workplaces or other environments associated with habitual travel patterns. For community transmission scenarios, our results demonstrate that mobility data add the most value to risk predictions when case counts are low and spatially clustered. Our method could assist health systems in the allocation of testing resources, and potentially guide the implementation of geographically targeted restrictions on movement and social interaction.
Fragranced consumer products: effects on autistic adults in the United States, Australia, and United Kingdom
Fragranced consumer products, such as cleaning supplies, air fresheners, and personal care products, can have adverse effects on both air quality and health. This study investigates the effects of fragranced products on autistic individuals ages 18-65 in the United States, Australia, and United Kingdom. Nationally representative population surveys (n = 1137; 1098; 1100) found that, across the three countries, 4.3% of adults (n = 142) report medically diagnosed autism (2.3%), an autism spectrum disorder (2.4%), or both. Of these autistic adults, 83.7% report adverse health effects from fragranced products, including migraine headaches (42.9%), neurological problems (34.3%), respiratory problems (44.7%), and asthma attacks (35.9%). In particular, 62.9% of autistic adults report health problems from air fresheners or deodorizers, 57.5% from the scent of laundry products coming from a dryer vent, 65.9% from being in a room cleaned with scented products, and 60.5% from being near someone wearing a fragranced product. Health problems can be severe, with 74.1% of these effects considered potentially disabling under legislation in each country. Further, 59.4% of autistic adults have lost workdays or lost a job, in the past year, due to fragranced product exposure in the workplace. More than twice as many autistic as well as non-autistic individuals would prefer that workplaces, health care facilities, and health care professionals were fragrance-free rather than fragranced. Results show that vulnerable individuals, such as those with autism or autism spectrum disorders, can be profoundly, adversely, and disproportionately affected by exposure to fragranced consumer products.
Application of FRP Bolts in Monitoring the Internal Force of the Rocks Surrounding a Mine-Shield Tunnel
Monitoring the internal force of the rocks surrounding a mine-shield tunnel for the initial support of a mine-shield tunnel, in complex geological and hydrological environments, requires bolts with specific features such as high tensile strength, low shear strength, good insulation and resistance to corrosion. As such, internal force monitoring has become an important issue in safety monitoring for such tunneling projects. In this paper, the adaptability of a mine-shield tunnel project in a corrosive environment is investigated. A fiberglass reinforced plastic (FRP) bolt with high tensile strength, low shear strength, resistance to fatigue, non-conductivity and resistance to corrosion is used as a probe in tandem with an anchor-head dynamometer to monitor the internal force of the rocks surrounding a mine-shield tunnel for initial support. Additionally, solar energy collection technology is introduced to create a remote monitoring system. Using a 2.5 km long railway tunnel located in the northeast of the Pearl River Delta of China as a case study, the present study shows that, compared with a conventional steel bolt, the FRP bolt has advantages, such as avoidance of the risks associated with the shield machine, insulation and resistance to corrosion. As a probe, the response of the FRP bolt to events such as a blasting vibration and a construction disturbance that results in internal changes in the surrounding rock demonstrates a clear pattern that is appropriate for monitoring the internal force of the rocks surrounding a mine-shield tunnel in a corrosive environment. FRP bolt-based monitoring not only provides new technological support for controlling the risk involved in the initial support of a mine-shield tunnel but can also be widely deployed in projects with special requirements for disassembly, conductivity and corrosion.
Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest
Uncertainty assessment techniques have been extensively applied as an estimate of accuracy to compensate for weaknesses with traditional approaches. Traditional approaches to mapping accuracy assessment have been based on a confusion matrix, and hence are not only dependent on the availability of test data but also incapable of capturing the spatial variation in classification error. Here, we apply and compare two uncertainty assessment techniques that do not rely on test data availability and enable the spatial characterisation of classification accuracy before the validation phase, promoting the assessment of error propagation within the classified imagery products. We compared the performance of emerging deep neural network (DNN) with the popular random forest (RF) technique. Uncertainty assessment was implemented by calculating the Shannon entropy of class probabilities predicted by DNN and RF for every pixel. The classification uncertainties of DNN and RF were quantified for two different hyperspectral image datasets—Salinas and Indian Pines. We then compared the uncertainty against the classification accuracy of the techniques represented by a modified root mean square error (RMSE). The results indicate that considering modified RMSE values for various sample sizes of both datasets, the derived entropy based on the DNN algorithm is a better estimate of classification accuracy and hence provides a superior uncertainty estimate at the pixel level.
Sensitivity Analysis of Geometrical Parameters on the Aerodynamic Performance of Closed-Box Girder Bridges
In this study, the influence of two critical geometrical parameters (i.e., angles of wind fairing, α; and lower inclined web, β) in the aerodynamic performance of closed-box girder bridges was systematically investigated through conducting a theoretical analysis and wind tunnel testing using laser displacement sensors. The results show that, for a particular inclined web angle β, a closed-box girder with a sharper wind fairing angle of α = 50° has better flutter and vortex-induced vibration (VIV) performance than that with α = 60°, while an inclined web angle of β = 14° produces the best VIV performance. In addition, the results from particle image velocimetry (PIV) tests indicate that a wind fairing angle of α = 50° produces a better flutter performance by inducing a single vortex structure and a balanced distribution of the strength of vorticity in both upper and lower parts of the wake region. Furthermore, two-dimensional three-degrees-of-freedom (2D-3DOF) analysis results demonstrate that the absolute values of Part A (with a reference of flutter derivative A₂*) and Part D (with a reference of A₁*H₃*) generally decrease with the increase of β, while the change of the participation level of heaving degrees of freedom (DOF) in torsion-dominated coupled flutter initially increases, reaches its peak, and then decreases with the increase of β.
Fragranced consumer products: effects on asthmatics
(SPRINGER INTERNATIONAL PUBLISHING AG, 2018-01-01)
Fragranced consumer products, such as cleaning supplies, air fresheners, and personal care products, can emit a range of air pollutants and trigger adverse health effects. This study investigates the prevalence and types of effects of fragranced products on asthmatics in the American population. Using a nationally representative sample (n = 1137), data were collected with an on-line survey of adults in the USA, of which 26.8% responded as being medically diagnosed with asthma or an asthma-like condition. Results indicate that 64.3% of asthmatics report one or more types of adverse health effects from fragranced products, including respiratory problems (43.3%), migraine headaches (28.2%), and asthma attacks (27.9%). Overall, asthmatics were more likely to experience adverse health effects from fragranced products than non-asthmatics (prevalence odds ratio [POR] 5.76; 95% confidence interval [CI] 4.34-7.64). In particular, 41.0% of asthmatics report health problems from air fresheners or deodorizers, 28.9% from scented laundry products coming from a dryer vent, 42.3% from being in a room cleaned with scented products, and 46.2% from being near someone wearing a fragranced product. Of these effects, 62.8% would be considered disabling under the definition of the Americans with Disabilities Act. Yet 99.3% of asthmatics are exposed to fragranced products at least once a week. Also, 36.7% cannot use a public restroom if it has an air freshener or deodorizer, and 39.7% would enter a business but then leave as quickly as possible due to air fresheners or some fragranced product. Further, 35.4% of asthmatics have lost workdays or a job, in the past year, due to fragranced product exposure in the workplace. More than twice as many asthmatics would prefer that workplaces, health care facilities and health care professionals, hotels, and airplanes were fragrance-free rather than fragranced. Results from this study point to relatively simple and cost-effective ways to reduce exposure to air pollutants and health risks for asthmatics by reducing their exposure to fragranced products.
Optimising the computational domain size in CFD simulations of tall buildings
Recently, there has been a growing interest in utilizing computational fluid dynamics (CFD) for wind resistant design of tall buildings. A key factor that influences the accuracy and computational expense of CFD simulations is the size of the computational domain. In this paper, the effect of the computational domain on CFD predictions of wind loads on tall buildings is investigated with a series of sensitivity studies. Four distinct sources of domain error are identified which include wind-blocking effects caused by short upstream length, flow recirculation due to insufficient downstream length, global venturi effects due to large blockage ratios, and local venturi effects caused by insufficient clearance between the building and top and lateral domain boundaries. Domains based on computational wind engineering guidelines are found to be overly conservative when applied to tall buildings, resulting in uneconomic grids with a large cell count. A framework for optimizing the computational domain is proposed which is based on monitoring sensitivity of key output metrics to variations in domain dimensions. The findings of this paper help inform modellers of potential issues when optimizing the computational domain size for tall building simulations.