Infrastructure Engineering - Research Publications
Now showing items 1-12 of 1119
A multiclass TrAdaBoost transfer learning algorithm for the classification of mobile lidar data
(Elsevier BV, 2020-08)
A major challenge in the application of state-of-the-art deep learning methods to the classification of mobile lidar data is the lack of sufficient training samples for different object categories. The transfer learning technique based on pre-trained networks, which is widely used in deep learning for image classification, is not directly applicable to point clouds, because pre-trained networks trained by a large number of samples from multiple sources are not available. To solve this problem, we design a framework incorporating a state-of-the-art deep learning network, i.e. VoxNet, and propose an extended Multiclass TrAdaBoost algorithm, which can be trained with complementary training samples from other source datasets to improve the classification accuracy in the target domain. In this framework, we first train the VoxNet model with the combined dataset and extract the feature vectors from the fully connected layer, and then use these to train the Multiclass TrAdaBoost. Experimental results show that the proposed method achieves both improvement in the overall accuracy and a more balanced performance in each category.
Understanding streambeds as complex systems: review of multiple interacting environmental processes influencing streambed permeability
The permeability of sediments at the sediment–water interface is an important control on several stream ecosystem services. It is well known that streambed permeability varies over several orders of magnitude, however, the environmental processes influencing this variation have received little attention. This review synthesizes the state-of-art knowledge and gaps in our understanding of the key physical and biological processes which can potentially modify the streambed permeability. These processes include—(a) physical clogging due to fine sediments, (b) biological clogging due to microbial biomass, and (c) sediment reworking by in-stream fauna. We highlight that the role of biotic processes (bioclogging and sediment reworking processes) in modifying the streambed permeability has not been investigated in detail. We emphasize that complex feedback mechanisms exist between these abiotic and biotic processes, and an interdisciplinary framework is necessary to achieve a holistic understanding of the spatio-temporal variability in streambed permeability. To this end, we propose to develop a conceptual model for streambed evolution after a disturbance (e.g. floods) as this model could be valuable in comprehending the dynamics of permeability. We also outline the challenges associated with developing a widely applicable streambed evolution model. Nonetheless, as a way forward, we present a possible scenario for the evolution of a streambed following a high flow event based on the trajectory of responses of the above-mentioned environmental processes. Finally, we suggest future research directions that could assist in improving the fundamental understanding of the clogging and sediment reworking processes and consequently of the dynamics of streambed permeability.
A TWO-STEP CLASSIFICATION APPROACH to DISTINGUISHING SIMILAR OBJECTS in MOBILE LIDAR POINT CLOUDS
Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.
Seismic performance of slender RC U-shaped walls with a single-layer of reinforcement
(Elsevier BV, 2020-12)
Reinforced concrete walls are commonly used to resist the lateral loading induced by wind and earthquake actions. While most walls include two vertical reinforcement layers, some regions of the world construct slender, non-rectangular concrete walls with a single vertical layer of reinforcement. The seismic performance of such elements is largely unknown given the paucity of experimental research. This paper presents the results of two slender reinforced concrete U-shaped walls tested at the Earthquake Engineering and Structural Dynamics Laboratory (EESD Lab), École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Both wall specimens, designed similar to construction practice in Colombia, were tested using quasi-static cyclic loading to observe if out-of-plane instability would develop when deformations were limited to prevent the flange boundary ends crushing. Initial failure of both wall specimens corresponded with local out-of-plane buckling in the boundary ends of the flanges occurring on load reversal. The buckling lengths were approximately 700–800 mm, which corresponded to 44–50 bar diameters. The crack patterns were observed to be steepest in the web of the walls, demonstrating the increased shear demand in comparison to that of a rectangular wall. Both wall specimens reached ultimate drifts larger than 2.5–3.0% before global failure occurring in the web-flange intersection due to crushing. A small twist was subjected to one of the walls when centered and loaded diagonally, which showed that the decay in torsional stiffness is proportional to the decay in translational stiffness.
Lessons from Flipping Subjects in Engineering: Effectiveness of Student Learning in a Flipped Environment at the University Level
(American Society of Civil Engineers (ASCE), 2021-01)
This paper outlines the subjective and quantitative outcomes of the introduction of the flipped classroom approach to two engineering subjects at the University of Melbourne. In this approach, lectures are delivered online as opposed to the traditional method of being provided in person. To facilitate learning, after each part of an online lecture, students completed an activity to reflect upon and review the content via compulsory questionnaires. Students would then attend formal classes in person (e.g., workshops) in which they would participate in interactive and collaborative activities related to the online lecture material. Surveys were provided to the students at the beginning of the semester to understand their perceptions of different learning activities. The surveys indicate that students who did well on the questionnaires also did well in the subject with a positive trend between questionnaire scores and final grades in both subjects. The survey results suggest that the flipped classroom method could provide students with better learning outcomes for subjects at the university level if implemented in a way that promotes active and student-centered learning. Some recommendations are provided based on the results of this paper for the implementation of the flipped classroom method for future subjects at the university level.
Spatial Metadata Usability Evaluation
(MDPI AG, 2020-07-21)
Spatial metadata is a critical part of any spatial data infrastructure, which enables the organising, sharing, discovery and use of spatial data. This paper highlights a knowledge gap in the usability of the metadata systems for the end–users. It then addresses the gap by applying the User Centred Design approach to investigate the usability of metadata records. The research engages with end–users concerning efficiency and effectiveness of metadata systems, and end–users’ satisfaction and expectations. The results indicate significant gaps with the effectiveness and efficiency of metadata systems for spatial data discovery and selection. Inconsistency and irrelevant information in the metadata records were found in the title, keywords, abstracts, data quality and other elements of the metadata. Additionally, essential improvements were identified for user interfaces. Discouraging presentation of the metadata is a prominent problem found in the interface of the metadata systems.
Biophilic Design Features in Vernacular Architecture and Settlements of the Naxi
2020 University of Nottingham Ningbo China (UNCC) Symposium Disruption vs Integration: Pathways to urban trans-formation, July 4
Predicting Current-Induced Drag in Emergent and Submerged Aquatic Vegetation Canopies
(Frontiers Media, 2018-12-04)
Canopies formed by aquatic vegetation, such as mangroves, seagrass, and kelp, play a crucial role in altering the local hydrodynamics in rivers, estuaries, and coastal regions, and thereby influence a range of morphodynamic and biophysical processes. Prediction of the influence of canopies on these hydrodynamic processes requires a fundamental understanding of canopy drag, which varies significantly with both flow conditions and canopy properties (such as density and submergence). Although our knowledge on canopy drag has increased significantly in recent decades, a conclusive, physics-based description for canopy drag that can be applied to both emergent and submerged canopies is currently lacking. Here, we extend a new theoretical canopy drag model (that employs the velocity between canopy elements as the reference velocity) to submerged aquatic canopies. The model is validated for the first time with direct measurements of drag forces exerted by canopies across broad ranges of flow conditions and canopy density and submergence. The skill and broader applicability of the model are further assessed using a comprehensive set of existing experimental data, covering a broad range of natural conditions (including flexible vegetation). The resulting model provides a simple tool to estimate canopy drag forces, which govern hydraulic resistance, sediment transport, and biophysical processes within aquatic ecosystems.
Impact of ENSO on dependence between extreme rainfall and storm surge
(Institute of Physics (IoP), 2019-12)
Dependence between extreme rainfall and storm surge can have significant implications for coastal floods, which are often caused by joint occurrence of these flood drivers (through pluvial or fluvial processes). The effect of multiple drivers leading to a compound flood event poses higher risk than those caused by a single flood-driving process. There is strong evidence that compound floods caused by joint occurrence of extreme storm surge and heavy rainfall are related to meteorological forcing (e.g. large scale pressure systems and wind) and climate phenomena (e.g. the El Niño Southern Oscillation or ENSO). Therefore, understanding how climate phenomena affect the co-occurrence of coastal flood drivers is an important step towards understanding future coastal flood risk under climate change. Here we examine the impact of one of the most important climate phenomena—ENSO—on dependence between storm surge and rainfall in Australia, using both observed surge and modelled surge from a linked ocean-climate model—the Regional Ocean Modeling System. Our results show that ENSO has a significant impact on the dependence between extreme rainfall and storm surge, thus flood risk resulted from these drivers. The overall dependence is largely driven by La Niña in Australia, with increased dependence observed during La Niña along most of the Australian coastline. However, there can be increased dependence during El Niño in some locations. The results demonstrate dependence is contributed by unequally-weighted mechanisms due to the interaction between climate phenomena and local features, indicating the need for greater understanding of composition of compound flood risk. Where climate phenomena are anticipated to change into the future, it is possible to use integrated process-driven models to establish a better understanding of whether extremes are more likely to co-occur and exacerbate compound flood risk.
Development of genetic-based models for predicting the resilient modulus of cohesive pavement subgrade soils
The accurate determination of resilient modulus (Mr) of pavement subgrade soils is an important factor for the successful design of pavement system. The important soil property Mr is complex in nature as it is dependent on several influential factors, such as soil physical properties, applied stress conditions, and environmental conditions. The aim of this study is to explore the potential of an evolutionary algorithm, i.e., genetic algorithm (GA), and a hybrid intelligent approach combining neural network with GA (ANN-GA), to estimate the Mr of cohesive pavement subgrade soils. To achieve this aim, a reliable database containing the results of repeated load triaxial tests (RLT) and other index properties of subgrade soils was utilized. GA was employed to develop a precise equation for predicting Mr of subgrade soils. In addition, GA was used as a tool for determining the optimal values of the weights and the bias of the ANN-GA approach. The developed ANN-GA model was then transferred to a functional relationship for further application and analyses. Several validation and verification phases were conducted to examine the performance of the developed models. The results indicated that both GA and ANN-GA models could accurately predict the Mr of cohesive subgrade soils, and performed better than other models in the literature. Finally, a sensitivity analysis was conducted to evaluate the effect of the utilized parameters on Mr.
Risks and opportunities for a Swiss hydroelectricity company in a changing climate
(Copernicus Publications, 2020-07-29)
Anticipating and adapting to climate change impacts on water resources requires a detailed understanding of future hydroclimatic changes and of stakeholders' vulnerability to these changes. However, impact studies are often conducted at a spatial scale that is too coarse to capture the specificity of individual catchments, and, importantly, the changes they focus on are not necessarily the changes most critical to stakeholders. While recent studies have combined hydrological and electricity market modeling, they tend to aggregate all climate impacts by focusing solely on reservoir profitability. Here, we collaborated with Groupe E, a hydroelectricity company operating several reservoirs in the Swiss pre-Alps, and we co-produced hydroclimatic projections tailored to support the upcoming negotiations of their water concession renewal. We started by identifying the vulnerabilities of their activities to climate change; together, we then selected streamflow and electricity demand indices to characterize the associated risks and opportunities. We provided Groupe E with figures showing the projected impacts, which were refined over several meetings. The selected indices enabled us to assess a variety of impacts induced by changes in (i) the seasonal water volume distribution, (ii) low flows, (iii) high flows, and (iv) electricity demand. This enabled us to identify key opportunities (e.g., the future increase in reservoir inflow in winter, when electricity prices have historically been high) and risks (e.g., the expected increase in consecutive days of low flows in summer and fall which is likely to make it more difficult to meet residual flow requirements). We highlight that the hydrological opportunities and risks associated with reservoir management in a changing climate depend on a range of factors beyond those covered by traditional impact studies. This stakeholder-centered approach, which relies on identifying stakeholder's needs and using them to inform the production and visualization of impact projections, is transferable to other climate impact studies, in the field of water resources and beyond.