An artificial intelligence platform for design optimization and data analysis: application for fire and ventilation problems

This thesis focuses on the development of novel multi-objective software platforms to assist engineering design and investigation, especially for simulation-based indoor environment problems, which always involve multiple evaluation criteria. In addition, this thesis aims to develop new methods to reduce the computational cost associated with the design process. In modern building design, engineers are constantly facing challenging to find an optimal design to maintain a high level of thermal comfort and indoor air quality for occupants while minimizing the system energy consumption. Over the past decades, several algorithms have been proposed and developed for optimizing the heating, ventilation and air conditioning (HVAC) system for indoor environment. Nevertheless, the majority of these optimization algorithms are focused on single objective optimization procedures and require a large training sample for surrogate modelling. For multi-objective HVAC design problems, previous studies introduced an arbitrary weighting factor to combine all design objectives into one single objective function. The near-optimal solutions were however sensitive to the chosen value of the weighting factor. In another hand, the computational cost is very heavy in the computer-aided investigation process of reverse engineering problems. Computational Fluid Dynamics (CFD) aided fire investigation is one of the reverse engineering. With the significant growth of the world population, our cities are becoming more and more crowding. In this situation, any fire occurring would cause severe consequences, including property damage and human injuries or even deaths. In assessing the fire cause, the fire origin determination is a crucial step identifying the origin of fire outbreak and the sequential fire and smoke propagation. Traditionally, fire investigators relied upon the visible fire damages at the fire scene to determine the location of fire originated based on their own professional experience. The fire investigation process is however subject to the expert interpretation inherently embedded in the qualitative analyses. In addition, we are living in an era of big data, where lots amount of data are generating every day, especially in engineering field. Traditional analysis methods are not suitable to handle large amount of data quickly and accurately. In contrast, new techniques such as machine learning are able to deal with big data and extract data features. The main body of this thesis is composed of seven chapters, and the details of each chapter are as the followings: The research background and a comprehensive literature review are described in the first two chapters where the research gaps found in the existing literatures are discussed. From Chapter 3 to Chapter 6, the main contributions of this research are demonstrated. In Chapter 3, a nondominated sorting-based particle swarm optimization (NSPSO) algorithm together with the Kriging method to perform optimization for the HVAC system design of a typical office room was developed. In addition, an adaptive sampling procedure was also introduced to enable the optimization platform to adjust the sampling point and resolution in constructing the training sample. Chapter 4 presents a Multi-fidelity Kriging algorithm to quantitatively determine the fire origin based on the soot deposition patterns predicted by the numerical simulations, which provides an unbiased and fast methodology to assist the fire investigation. A comprehensive multi-objective optimization platform of the ventilation system inside a typical high-speed train (HST) cabin is discussed in Chapter 5, where the NSPSO and the Multi-fidelity Kriging were combined together to reduce computational cost. Chapter 6 demonstrates a successful application of convolutional neural networks (CNN) in vegetation feature analysis to help cut powerline wildfire risk caused by vegetation conduction ignition. Finally, all the contributions in this research are summarised in Chapter 7

Cut-set and Stability Constrained Optimal Power Flow for Resilient Operation During Wildfires

Resilient operation of the power system during ongoing wildfires is challenging because of the uncertain ways in which the fires impact the electric power infrastructure (multiple arc-faults, complete melt-down). To address this challenge, we propose a novel cut-set and stability-constrained optimal power flow (OPF) that quickly mitigates both static and dynamic insecurities as wildfires progress through a region. First, a Feasibility Test (FT) algorithm that quickly desaturates overloaded cut-sets to prevent cascading line outages is integrated with the OPF problem. Then, the resulting formulation is combined with a data-driven transient stability analyzer that predicts the correction factors for eliminating dynamic insecurities. The proposed model considers the possibility of generation rescheduling as well as load shed. The results obtained using the IEEE 118-bus system indicate that the proposed approach alleviates vulnerability of the system to wildfires while minimizing operational cost

Aerial Image Analysis using Deep Learning for Electrical Overhead Line Network Asset Management

Electricity networks are critical infrastructure, delivering vital energy services. Due to the significant number, variety and distribution of electrical network overhead line assets, energy network operators spend millions annually on inspection and maintenance programmes. Currently, inspection involves acquiring and manually analysing aerial images. This is labour intensive and subjective. Along with costs associated with helicopter or drone operations, data analysis represents a significant financial burden to network operators. We propose an approach to automating assessment of the condition of electrical towers. Importantly, we train machine learning tower classifiers without using condition labels for individual components of interest. Instead, learning is supervised using only condition labels for towers in their entirety. This enables us to use a real-world industry dataset without needing costly additional human labelling of thousands of individual components. Our prototype first detects instances of components in multiple images of each tower, using Mask R-CNN or RetinaNet. It then predicts tower condition ratings using one of two approaches: (i) component instance classifiers trained using class labels transferred from towers to each of their detected component instances, or (ii) multiple instance learning classifiers based on bags of detected instances. Instance or bag class predictions are aggregated to obtain tower condition ratings. Evaluation used a dataset with representative tower images and associated condition ratings covering a range of component types, scenes, environmental conditions, and viewpoints. We report experiments investigating classification of towers based on the condition of their multiple insulator and U-bolt components. Insulators and their U-bolts were detected with average precision of 96.7 and 97.9, respectively. Tower classification achieved areas under ROC curves of 0.94 and 0.98 for insulator condition and U-bolt condition ratings, respectively. Thus we demonstrate that tower condition classifiers can be trained effectively without labelling the condition of individual components

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments

Measuring knowledge sharing processes through social network analysis within construction organisations

The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes

The 45th Australasian Universities Building Education Association Conference: Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment, Conference Proceedings, 23 - 25 November 2022, Western Sydney University, Kingswood Campus, Sydney, Australia

This is the proceedings of the 45th Australasian Universities Building Education Association (AUBEA) conference which will be hosted by Western Sydney University in November 2022. The conference is organised by the School of Engineering, Design, and Built Environment in collaboration with the Centre for Smart Modern Construction, Western Sydney University. This year’s conference theme is “Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment”, and expects to publish over a hundred double-blind peer review papers under the proceedings