Infrastructure Engineering - Research Publications
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ItemNo Preview AvailableInnovative composite structural systems for modular tall buildings(https://www.compositeconstructionix.com/, 2021)Modular or offsite construction is believed to shape the future of the construction industry as it possesses significant benefits over traditional onsite construction methods. However, most of its application are limited to steel or concrete buildings. Although steel-concrete composite structural system has many merits over the steel and concrete systems, its application in modular buildings is very limited. This paper explores recent developments of composite systems for modular high-rise buildings. They include modular units for resisting vertical gravity loads and lateral structural systems for resisting horizontal forces from wind and earthquake loadings and progressive collapse due to accidental loads such as fire, explosions and impact. Various inter-module joining methods developed in the literature will also be reviewed. Finally, a case study of the most efficient connection is presented to explore its applicability to high-rise modular buildings.
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ItemInnovative composite structural systems for modular tall buildings(https://www.compositeconstructionix.com/, 2021)Modular or offsite construction is believed to shape the future of the construction industry as it possesses significant benefits over traditional onsite construction methods. However, most of its application are limited to steel or concrete buildings. Although steel-concrete composite structural system has many merits over the steel and concrete systems, its application in modular buildings is very limited. This paper explores recent developments of composite systems for modular high-rise buildings. They include modular units for resisting vertical gravity loads and lateral structural systems for resisting horizontal forces from wind and earthquake loadings and progressive collapse due to accidental loads such as fire, explosions and impact. Various inter-module joining methods developed in the literature will also be reviewed. Finally, a case study of the most efficient connection is presented to explore its applicability to high-rise modular buildings.
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ItemIntegrating sustainability into higher education curricula(Engineers Australia, 2021)Education has been widely recognised as a key instrument to achieve sustainability. Integrating sustainability knowledge, skills and values are considered paramount to enable individuals to contribute to sustainable development. The paper presents a pilot study conducted at the University of Melbourne to investigate the links between the subjects offered by the University and sustainability. The pilot study is a part of the Sustainability in the Curriculum program, which addresses the Sustainability Plan Teaching and Learning Target 1, aimed to incorporate sustainability knowledge and values in the University's curricula. The 17 Sustainability Development Goals have been used as a framework to measure how well the curricula are linked to sustainability. A study first undertaken to establish the link between subjects and the Sustainability Development Goals is presented. The study involved data collection through published literature on Sustainable Development Goals and the University's subject handbook, followed by a survey involving the subject coordinators. The findings of the study show that the strength of linkages between subjects with sustainability varies, highlighting the challenge in some technical subjects in linking their contents with sustainability. Approaches adopted in the Faculty of Engineering and Information Technology in embedding sustainability in the curriculum are presented with some examples and discussions for the next steps.
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ItemIn-season crop classification using optical remote sensing with random forest over irrigated agricultural fields in Australia( 2021-03-03)<p>Timely classification of crop types is critical for agronomic planning in water use and crop production. However, crop type mapping is typically undertaken only after the cropping season, which precludes its uses in later-season water use planning and yield estimation. This study aims 1) to understand how the accuracy of crop type classification changes within cropping season and 2) to suggest the earliest time that it is possible to achieve reliable crop classification. We focused on three main summer crops (corn/maize, cotton and rice) in the Coleambally Irrigation Area (CIA), a major irrigation district in south-eastern Australia consisting of over 4000 fields, for the period of 2013 to 2019. The summer irrigation season in the CIA is from mid-August to mid-May and most farms use surface irrigation to support the growth of summer crops. We developed models that combine satellite data and farmer-reported information for in-season crop type classification. Monthly-averaged Landsat spectral bands were used as input to Random Forest algorithm. We developed multiple models trained with data initially available at the start of the cropping season, then later using all the antecedent images up to different stages within the season. We evaluated the model performance and uncertainty using a two-fold cross validation by randomly choosing training vs. validation periods. Results show that the classification accuracy increases rapidly during the first three months followed by a marginal improvement afterwards. Crops can be classified with a User&#8217;s accuracy above 70% based on the first 2-3 months after the start of the season. Cotton and rice have higher in-season accuracy than corn/maize. The resulting crop maps can be used to support activities such as later-season system scale irrigation decision-making or yield estimation at a regional scale.</p><p>Keywords: Landsat 8 OLI, in-season, multi-year, crop type, Random Forest</p>
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ItemUsing short-term ensemble weather forecast to evaluate outcomes of irrigation( 2021-03-03)<p>Irrigation water is an expensive and limited resource. Previous studies show that irrigation scheduling can boost efficiency by 20-60%, while improving water productivity by at least 10%. In practice, scheduling decisions are often needed several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs such as soil water, weather and crop conditions. Since each input can be subject to its own uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions.</p><p>This study aims to evaluate the outcomes of alternative irrigation scheduling decisions under uncertainty, with a focus on the uncertainties arising from short-term weather forecast. To achieve this, we performed a model-based study to simulate crop root-zone soil water content, in which we comprehensively explored different combinations of ensemble short-term rainfall forecast and alternative decisions of irrigation scheduling. This modelling produced an ensemble of soil water contents to enable quantification of risks of over- and under-irrigation; these ensemble estimates were summarized to inform optimal timing of next irrigation event to minimize both the risks of stressing crop and wasting water. With inclusion of other sources of uncertainty (e.g. soil water observation, crop factor), this approach shows good potential to be extended to a comprehensive framework to support practical irrigation decision-making for farmers.</p>
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ItemImproving the representation of cropland sites in the Community Land Model (CLM) version 5.0( 2021-03-04)<p>The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5; (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding the two crop functional types (CFT) for sugar beet and potatoes to the list of actively managed crops in CLM5; (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity to reduce erosion and improve soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop specific parameterization, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes (RMSE reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes and seasonal cycle of LAI, and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water and energy fluxes.</p>
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ItemOn the relationship between the variability of catchment hydroclimate and physiography, and the uncertainty of runoff generation hypotheses( 2021-03-04)<p>Question #20 of the UPH aspires to disentangle and reduce model prediction uncertainty. One feasible approach is to first formulate the relationship between variability (of real-world hydrological processes and catchment characteristics) and uncertainty (of model components and variables), which links the UPH theme of &#8220;modelling methods&#8221; to &#8220;time variability and change&#8221; and &#8220;space variability and scaling&#8221;. Building on this premise, we explored the relationship between runoff generation hypotheses, derived from a large ensemble of catchment model simulations, and catchment characteristics (physiographic, climatic, and streamflow response characteristics) across a large sample of 221 Australian catchments. Using ensembles of 10<sup>6&#160;</sup>runs of SIMHYD model for each catchment, runoff generation hypotheses were formulated based on the interaction of 3 runoff generating fluxes of SIMHYD, namely intensity-based, wetness-based, and slow responses. The hypotheses were derived from model runs with acceptable performance and sufficient parameter sampling. For model performance acceptability, we benchmarked Kling-Gupta Efficiency (KGE) skill score against the calendar day average observed flow, a catchment-specific and more informative benchmark than the conventional observed flow mean. The relative parameter sampling sufficiency was also defined based on the comparative efficacy of two common model parameterisation routines of Latin Hypercube Sampling and Shuffled Complex Evolution for each catchment. Across 186 catchments with acceptable catchment models, we examined the association of uncertain runoff generation hypotheses (i.e. ensemble of modeled runoff fluxes) with 22 catchment attributes. We used the Flux Mapping method (https://doi.org/10.1029/2018WR023750) to characterise the uncertainty of runoff generation hypotheses, and a range of daily and annual summary statistics to characterise catchment attributes. Among the metrics used, Spearman rank correlation coefficient (R<sub>s</sub>) was the most informative metric to capture the functional connectivity of catchment attributes with the internal dynamics of model runoff fluxes, compared to linear Pearson correlation and distance correlation coefficients. We found that streamflow characteristics generally have the most important influence on runoff generation hypotheses, followed by climate and then physiographic attributes. Particularly, daily flow coefficient of variability (Qcv) and skewness (Q Skewness), followed by the same summary statistics of precipitation (Pcv and&#160;P Skewness), were most important. These four attributes are strongly correlated with one another, and represent the dynamics of the rainfall-runoff signal within a catchment system. A higher&#160;Pcv&#160;denotes a higher day-to-day variability in rainfall on the catchment, responded by a higher&#160;Qcv&#160;flow response. A higher variability in rainfall propagates through the catchment model and translates into a higher degree of equifinality in model runoff fluxes, which implies larger uncertainties of runoff generation hypotheses at catchment scale, and hence a greater challenge for reliable/realistic simulation and prediction of streamflow.</p>
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Item3D CITYLUR: MODELLING 3D CITY LAND-USE REGULATIONS to SUPPORT ISSUING A PLANNING PERMIT(ISPRS, 2021-10-07)The applications and understanding of Land-use Regulations (LuR) are more communicable when they are linked to the digital representation of the physical world. In order to support issuing a planning permit and move towards the establishment of automated planning permit checks, this paper investigates how LuRs related to a planning permit process can be modelled in 3D called 3D CityLuR. 3D CityLuR serves as a 3D model for representing LuRs' legal extents on a city scale. It is formed based on multiple geometric modelling approaches representing LuRs, which can provide a better cognitive understanding of LuRs and subsequently facilitate LuR automatic checks. To this purpose, according to LuRs' descriptions and characteristics explained in related planning documents, key parameters representing LuRs' extent are identified (e.g. maximum distance in overlooking or maximum allowed height in building height regulations). Accordingly, to automatically model each LuR, a geometric modelling approach (e.g. Boundary Representation (B-Rep), CSG, and extrusion) that best fits with the identified key parameters is proposed. In addition, to combine 3D CityLuR with an integrated BIM-GIS environment, the level of information need in terms of geometries and semantics is specified. Finally, the paper results in a showcase for five LuRs including building height, energy efficiency protection, overshadowing open space, overlooking, and noise impacts regulations. The showcase is a proof of concept for determining how these LuRs can be modelled in 3D and combined with 3D city models based on the selected geometric modelling approaches, identified parameters, and level of information need.