School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau

Permanent link for this collectionhttps://hdl.handle.net/10292/553

AUT is home to a number of renowned research institutes in engineering, and computer and mathematical sciences. The School of Engineering, Computer and Mathematical Sciences strong industry partnerships and the unique combination of engineering, computer and mathematical sciences within one school stimulates interdisciplinary research beyond traditional boundaries. Current research interests include:

Artificial Intelligence; Astronomy and Space Research;

Biomedical Technologies;

Computer Engineering; Computer Vision; Construction Management;

Data Science;

Health Informatics and eHealth;

Industrial Optimisation, Modelling & Control;

Information Security;

Mathematical Sciences Research; Materials & Manufacturing Technologies;

Networking, Instrumentation and Telecommunications;

Parallel and Distributed Systems; Power and Energy Engineering;

Software Engineering; Signal Processing; STEM Education;

Wireless Engineering;

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Now showing 1 - 20 of 1764

Interactive Visualisation of Complex Street Network Graphs from OSM in New Zealand
(MDPI AG, 2025-12-07) Ng, Jun Yi; Ma, Jing; Singh, Anuradha; Lai, Edmund M-K; Hayman, Steven
Street network graphs model interconnected land transport infrastructure, including roads and intersections, enabling traffic analysis, route planning, and network optimization. Directed network graphs (digraphs) add directionality to these connections, reflecting one-way streets and complex traffic flows. While OpenStreetMap (OSM) offers extensive data, visualizing large-scale directed networks with complex junctions remains computationally challenging for browser-based tools. This paper presents an interactive visualization tool integrating OSM data with the New Zealand Transport Agency’s National Network Performance (NNP) analysis toolbox using PyDeck and WebGL. We introduce a directional offset algorithm to resolve edge overlaps and a geometry-aware node placement method for complex intersections. Experimental results demonstrate that our PyDeck implementation significantly outperforms existing solutions like Bokeh and OSMnx. On standard datasets, the system achieves up to 238× faster processing speeds and a 93% reduction in output file size compared to Bokeh. Furthermore, it successfully renders metropolitan-scale networks (∼1.3 million elements) where traditional visualisation tools fail to execute. This visualisation approach serves as a critical debugging instrument for NNP, allowing transport modellers to efficiently identify connectivity errors and validate the structural integrity of large-scale transport models.
A Stacked Substrate-Integrated Waveguide-Based Pyramidal Horn Antenna for Terahertz Communications
(MDPI AG, 2025-12-04) Paudel, Biswash; Li, Xue Jun; Seet, Boon-Chong
The terahertz (THz) band offers ultra-wide bandwidth for next-generation high-speed wireless communication systems. However, achieving compact, high-gain, and beam-symmetric THz antennas remains challenging due to fabrication and propagation constraints. This paper presents a simulation-based design and optimization of a stacked substrate-integrated waveguide (SIW) pyramidal horn antenna achieving equal half-power beamwidths (HPBWs) in both E- and H-planes. The design employs vertically stacked SIW layers coupled through optimized slot apertures to ensure dominant TE10 mode propagation with minimal reflection. Using full-wave electromagnetic simulations, the effects of layer number, dielectric loading, amplitude tapering, and phase distribution are systematically analyzed. The optimized five-layer configuration exhibits 10 dBi gain, 41° HPBW, and sidelobe levels around −3.2 dB at 210 GHz. This framework aims to develop high-performance, beam-symmetric THz SIW antennas compatible with standard LTCC/PCB technologies.
Analysis of the Effect of Using a Variable Speed Drive on the Power Consumption of the ID Fan Drive Motor
(EDP Sciences, 2025-11-20) Kastawan, I Made Wiwit; Amanda, Rizki; Mulyadi, Ahmad Deni; Murniyati, Dyah Ayu Yuli; Zamora, Ramon
Cirebon Coal-Fired Power Plant Unit 1 uses an Induced Draft Fan (ID Fan) to regulate flue gas flow and maintain negative pressure inside the boiler. The current ID Fan control system still applies a blade pitch position mechanism with constant motor speed, resulting in high power consumption ranging from 2,547.9 kW to 4,311.2 kW. This condition is inefficient because the motor runs at constant speed regardless of changing load demands. This study aims to design a Variable Speed Drive (VSD) to control the ID Fan motor speed and analyze its impact on power consumption. The research was conducted using simulation with MATLAB/Simulink R2021a software. The VSD design consists of three main components: a three-phase rectifier, a buck converter-based DC link, and a three-phase PWM inverter, adjusted to meet the ID Fan operational pressure limit of 8.0 to 10.0 kPa. The simulation results show that with the implementation of VSD, the system pressure can be maintained within the safe range of 8.5 to 9.7 kPa, and the motor speed can be adjusted according to airflow demand. Power consumption decreased from 2,645.7 kW to 1,273.1 kW after implementing VSD, resulting in an energy saving of 62.1%. The application of VSD is proven to be effective in improving energy efficiency in the ID Fan system at the Cirebon Coal-Fired Power Plant Unit.
Unsupervised Thematic Context Discovery for Explainable AI in Fact Verification: Advancing the CARAG Framework
(SciTePress - Science and Technology Publications, 2025-12) Vallayil, Manju; Nand, P; Yan, Wei Qi; Allende-Cid, H
This paper introduces CARAG-u, an unsupervised extension of the Context-Aware Retrieval Augmented Generation (CARAG) framework, designed to advance explainability in Automated Fact Verification (AFV) architectures. Unlike its predecessor, CARAG-u eliminates reliance on predefined thematic annotations and claim-evidence pair labels, by dynamically deriving thematic clusters and evidence pools from unstructured datasets. This innovation enables CARAG-u to balance local and global perspectives in evidence retrieval and explanation generation. We benchmark CARAG-u against Retrieval Augmented Generation (RAG) and compare it with CARAG, highlighting its unsupervised adaptability while maintaining a competitive performance. Evaluations on the FactVer dataset demonstrate CARAG-u’s ability to generate thematically coherent and context-sensitive post-hoc explanations, advancing Explainable AI in AFV. The implementation of CARAG-u, including all dependencies, is publicly available to ensure reproducibility and support further research.
Bayesian Estimation of R-Vine Copula with Gaussian-Mixture GARCH Margins: An MCMC and Machine Learning Comparison
(MDPI AG, 2025-12-04) Khanthaporn, Rewat; Wichitaksorn, Nuttanan
This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions. The Bayesian estimation approach includes Markov chain Monte Carlo and variational Bayes with data augmentation. Although R-vines typically involve computationally intensive procedures limiting their practical use, we address this challenge through parallel computing techniques. To demonstrate our approach, we employ thirteen bivariate copula families within an R-vine pair-copula construction, applied to a large number of marginal distributions. The margins are modeled as exponential-type GARCH processes with intertemporal capital asset pricing specifications, using a mixture of Gaussian and generalized Pareto distributions. Results from an empirical study involving 100 financial returns confirm the effectiveness of our approach.
A Secure and Sustainable Transition from Legacy Smart Cards to Mobile Credentials in University Access Control Systems
(MDPI AG, 2025-12-04) Mustafa, Rashid; Khan, Toseef Ahmed; Sarkar, Nurul I
A secure and sustainable building access control system plays a vital role in protecting organisational assets worldwide. Physical access management at Auckland University of Technology (AUT) is still primarily done through traditional card-based authentication. The system is susceptible to replay and cloning attacks because the conventional Mifare Classic credentials employ outdated Crypto1 encryption. Such weaknesses provide significant threats in laboratories, engineering testing facilities, and research and technological areas that require strict security procedures. To overcome the above issues, we propose a secure and sustainable university building access control system using mobile app credentials. This research grounded a thorough risk analysis of the university’s current infrastructure, mapping potential operational continuity threats. We analyse card issuance records by identifying high-risk areas such as restricted laboratories and evaluating the resilience of the current Gallagher–Salto system against cloning and replay attacks. We quantify the distribution and usage of cards that are vulnerable. To evaluate the risks to operational continuity, the system architecture is examined. Additionally, a trial implementation of the Gallagher Mobile Connect platform was conducted, utilising cloud registration, multi-factor authentication (PIN or biometrics), and books. Pilot implementation shows that mobile-based credentials improve user experience, align with AUT’s environmental sustainability roadmap, and increase resilience against known attacks. Results have shown that our proposed mobile credentials can improve the system performance up to 80%.
AI-Driven Energy-Efficient Routing in IoT-Based Wireless Sensor Networks: A Comprehensive Review
(MDPI AG, 2025-12-05) Thakur, Sumendra; Sarkar, Nurul I; Yongchareon, Sira
Efficient routing remains the linchpin for achieving sustainable performance in Wireless Sensor Networks (WSNs) within the Internet of Things (IoT). However, traditional routing mechanisms increasingly struggle to cope with the growing complexity of network architectures, frequent changes in topology, and the dynamic behavior of mobile nodes. These issues contribute to data congestion, uneven energy consumption, and potential communication breakdowns, underscoring the urgency for optimized routing strategies. In this paper, we present a comprehensive review of over 100 studies of spanning conventional and AI-enhanced energy-efficient routing techniques. It covers diverse approaches, including metaheuristics, machine learning, reinforcement learning, and AI-based cross-layer methods aimed at improving the performance of WSN-IoT systems. The key limitations of existing solutions are discussed along with performance metrics such as scalability, energy efficiency, throughput, and packet delivery. We also highlight various research challenges and provide research directions for future exploration. By synthesizing current trends and gaps, we provide researchers and practitioners with a structured foundation for advancing intelligent, energy-conscious routing in next-generation IoT-enabled WSNs.
Perspective on the Role of AI in Shaping Human Cognitive Development
(MDPI AG, 2025-11-20) Abbosh, Amin; Al-Anbuky, Adnan; Xue, Fei; Mahmoud, Sundus S
The fourth industrial revolution, driven by Artificial Intelligence (AI) and Generative AI (GenAI), is rapidly transforming human life, with profound effects on education, employment, operational efficiency, social behavior, and lifestyle. While AI tools potentially offer unprecedented support in learning and problem-solving, their integration into education raises critical questions about cognitive development and long-term intellectual capacity. Drawing parallels to previous industrial revolutions that reshaped human biological systems, this paper explores how GenAI introduces a new level of abstraction that may relieve humans from routine cognitive tasks, potentially enhancing performance but also risking a cognitively sedentary condition. We position levels of abstraction as the central theoretical lens to explain when GenAI reallocates cognitive effort toward higher-order reasoning and when it induces passive reliance. We present a conceptual model of AI-augmented versus passive trajectories in cognitive development and demonstrate its utility through a simulation-platform case study, which exposes concrete failure modes and the critical role of expert interventions. Rather than a hypothesis-testing empirical study, this paper offers a conceptual synthesis and concludes with mitigation strategies organized by abstraction layer, along with platform-centered implications for pedagogy, curriculum design, and assessment.
Understanding Security Vulnerabilities in Private 5G Networks: Insights from a Literature Review
(MDPI AG, 2025-10-23) Fue, Jacinta; Gutierrez, Jairo A; Donoso, Yezid
Private fifth generation (5G) networks have emerged as a cornerstone for ultra-reliable, low-latency connectivity across mission-critical domains such as industrial automation, healthcare, and smart cities. Compared to conventional technologies like 4G or Wi-Fi, they provide clear advantages, including enhanced service continuity, higher reliability, and customizable security controls. However, these benefits come with new security challenges, particularly regarding the confidentiality, integrity, and availability of data and services. This article presents a review of security vulnerabilities in private 5G networks. The review pursues four objectives: (i) to identify and categorize key vulnerabilities, (ii) to analyze threats that undermine core security principles, (iii) to evaluate mitigation strategies proposed in the literature, and (iv) to outline gaps that demand further investigation. The findings offer a structured perspective on the evolving threat landscape of private 5G networks, highlighting both well-documented risks and emerging concerns. By mapping vulnerabilities to mitigation approaches and identifying areas where current solutions fall short, this study provides critical insights for researchers, practitioners, and policymakers. Ultimately, the review underscores the urgent need for robust and adaptive security frameworks to ensure the resilience of private 5G deployments in increasingly complex and high-stakes environments.
A Bibliographic Study of Integrating IoT and Geospatial Modelling for Sustainable Smart Agriculture in Developed Countries: Focus on Australia
(Elsevier, 2025-12-02) Mamun, Quazi; Zaman, Asaduz; Ip, Ryan HL; Haque, KM Shamsul
Integrating the Internet of Things (IoT) and geospatial modelling technologies is pivotal for advancing sustainable smart agriculture, particularly in resource-constrained environments like Australia. This systematic literature review examines the adoption and impact of these technologies in agriculture across Australia and select developed countries. Through an extensive analysis of 172 peer-reviewed articles published between 2013 and 2023, this study identifies key technological advancements such as unmanned aerial vehicles (UAVs), consumer-grade cameras (RGB cameras), and satellite platforms (Sentinel-2, LANDSAT-8) that have significantly influenced agricultural practices. The findings reveal Australia’s progress in adopting these technologies but also highlight gaps compared to countries like Germany and the USA, especially in using UAVs, Synthetic Aperture Radar (SAR) and RGB cameras. The study underscores Australia’s need to enhance its technological capabilities, particularly resource management, to foster more efficient and sustainable agricultural practices. This review provides valuable insights for policymakers, researchers, and technology providers, aiming to drive innovation and improve agricultural outcomes in the face of growing environmental challenges.
Ethical and Societal Impacts of Generative AI in Higher Computing Education: An ACM Task Force Working Group to Develop a Landscape Analysis – Perspectives from the Global Souths and Guidelines for CS1/CS2/CS3
(ACM, 2025-06-17) Szabo, C; Sheard, J; Dake, DK; Falkner, NJG; Enock, M; Ogunyemi, O; Mbodila, M; Clear, T; Ola, O; Taukobong, T; Wadhwa, B
Generative AI has a wide range of impacts on how we access and use information, particularly as educational settings and perspectives differ greatly across different locations. These impacts extend to society and include impacts on intellectual and creative works and the potential infringement of authorship. Differences in institutional GenAI policies (and in funding) may create unequal access to AI tools, the potential disparity in student knowledge of AI tools, responsible uses of AI tools, ethical questions about AI tools, and uneven student knowledge of the benefits and limitations of AI tools. Generative AI introduces questions concerning academic integrity, bias, and data provenance. The training data’s source, reliability, veracity, and trustworthiness may be in doubt, creating broader societal concerns about the output of the Generative AI models. This working group will conduct a landscape analysis on Global South ethical questions related to the use of Generative AI tools in higher education contexts, identifying promising principles, challenges, and ways to navigate the implementation of Generative AI in ethical and principled ways.
An Experimental EACI-Based Localization Framework Using LQI and CNN for Consumer IoT
(Institute of Electrical and Electronics Engineers (IEEE), 2025-11-11) Ahmad, T; Hadi, MU; Li, XJ; Anwar, A; Ibrahim, MM; Khan, S
Precise indoor localization remains a challenge in wireless sensor networks (WSNs) due to multipath fading, interference, and signal fluctuations in different environments. Traditional methods depend on Received Signal Strength (RSS) also often struggle with accuracy in indoor scenario. This study presents an experimental localization framework that utilizes Link Quality Indicator (LQI) values and Convolutional Neural Networks (CNNs) within an Edge Computing-Assisted Consumer IoT (EACI) model. The proposed approach segments the network using a pyramid-loop algorithm and employs LQI-based measurements for more stable and accurate distance estimation. A CNN classifier is trained on normalized LQI data, including statistical features such as kurtosis, to predict node locations. The system is authenticated by a real-world testbed using Zigbee XB24C nodes. The experimental results show an overall localization error of 0.12m at zone 1 with a standard deviation of 0.89m. This reflects an improved localization accuracy and reduced error compared to RSS-based and existing CNN-based methods. The proposed technique effectiveness is observed for indoor localization in consumer IoT environments.
Contrasting Big Data Techniques in Exploring New Zealand Road Crash Data
(Auckland University of Technology, 2025-11-17) Thorpe, Stephen; Hu, Baosen (Edison)
Motor vehicle crashes result in high social and economic costs globally and in New Zealand. Therefore, accurate analysis of crash events is critical for evidence-based prevention and policy. This study explored the application of Big Data techniques, specifically Hadoop and MapReduce, to improve the analysis of the impact of weather and speed on motor vehicle crashes in New Zealand. Contemporary Big Data approaches were applied to address the limitations inherent in traditional methods of crash analysis. We used Hadoop’s distributed storage and MapReduce’s processing capabilities on the New Zealand Transport Agency’s Crash Analysis System (CAS) dataset to identify and visualize environmental and spatial trends to a higher degree of understanding. The project involved Elasticsearch and Kibana to make sense of unstructured data in geographic views, while Hue, Hive, and Power BI represented structured data with charts and dashboards. Results show that non-injury crashes, followed by minor crashes, are the most frequent, with over half happening at speed limits between 40–60 km/h. Geographically, Auckland represents crashes five times greater than in the other locations. Strong and extreme weather conditions appear to be a factor in the majority of reported fatal road accidents.
Improving Story Points Estimation Using Ensemble Machine Learning
(Springer Science and Business Media LLC, 2025-11-13) Ahmad, Z; Kuo, MMY
Agile software development (ASD) emphasizes iterative development, continuous feedback, and team collaboration, addressing the limitations of traditional methodologies. This research explores the application of machine learning (ML) to improve story point estimation in ASD, a critical practice for planning and prioritization. Traditional methods like Planning Poker often suffer from human biases and inconsistencies, leading to unreliable estimates. This study introduces an innovative ML-based ensemble stacking technique, combining RoBERTa, a transformer model for natural language processing, with BiLSTM, a neural network adept at handling sequential data. The research involves reviewing existing ML methodologies, developing the proposed model, and evaluating its effectiveness using 21,064 data points from 14 open-source projects. The model’s performance was assessed through Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results show that the proposed ensemble model achieved lower MAE and MAPE, with performance improvements ranging from 4% to 32% over state-of-the-art models. While promising, the study suggests there is still room for further refinement, indicating the potential for ongoing advancements. This research contributes to the integration of ML in software engineering, offering a path toward more accurate and efficient project management.
Accumulated Performance Comparison of Solar PV and Solar Thermal Water Heating in New Zealand
(Wiley, 2025-12-01) Lu, Wei; Wang, Jay
The rising global energy demand and environmental harm caused by reliance on fossil fuels highlight the urgent need for sustainable solutions in residential water heating. This study conducts a comparative numerical analysis of solar photovoltaic (PV) and solar thermal water heaters under New Zealand's typical weather conditions. By integrating the National Institute of Water and Atmospheric Research (NIWA) meteorological data, hourly simulation results were performed to analyze energy output, water temperature profiles inside storage tank, and levelized cost of electricity (LCOE) under varying seasonal conditions, including summer, normal, and winter. The highlight of this study is the evaluation of accumulated water performance within the storage tank under real‐life operating conditions. This includes the impact of actual weather fluctuations (including ambient temperature and solar radiation), target hot water temperature, heat losses from the tank, and typical household water usage patterns along with the replenishment of cold water. Under identical weather conditions, the simulations results indicate that the solar thermal system outperforms solar PV system across key performance metrics. For solar water heating applications, the thermal system generated 5847.59 kWh of energy annually per 10 m of collector area, compared to 2780.41 kWh from the PV system. Although the thermal system required 33.7% more auxiliary energy annually (964.59 kWh) compared to the PV system (721.38 kWh), it delivered better energy utilization efficiency. This is highlighted by its produced‐to‐used energy ratio of 6.06, which is 58% higher than the PV system's ratio of 3.85. The efficiency translated to better economic advantages, with thermal achieving a 61.9% lower LCOE for produced energy at 0.0766 NZD/kWh compared to PV's 0.2013 NZD/kWh, and a 40.2% lower LCOE for used energy at 0.4641 NZD/kWh versus PV's 0.7757 NZD/kWh.
A Mobile DevOps Project Management Framework
(Project Management Research Office, AUT, 2025-11-27) Khan, Farhan; Ma, Jing
Mobile applications have become critical infrastructure in finance, healthcare, and public services, yet their development and operations pipelines remain complex and disconnected. Although DevOps practices have matured in web and cloud systems, mobile environments continue to face fragmented monitoring, weak governance visibility, and inconsistent security assurance. This study addresses the problem of limited managerial control and transparency in mobile DevOps projects, where automation lacks connection to governance and decision making. The objective was to develop a unified model that integrates automation, monitoring, and governance to strengthen project oversight. Drawing from a Multivocal Literature Review of academic and practitioner studies and examined through a demonstration linking an iOS application with Sentry and Azure DevOps, the research produced the Mobile DevOps Project Management Framework. The demonstration illustrates how real time logging, alerting, and dashboards can provide continuous visibility for project managers. It illustrates how organisations can strengthen reliability, assurance, and transparency by embedding governance within continuous delivery cycles.
Managing Academic Integrity in the AI Era: A Project Management Lens on Academic Integrity
(Project Management Research Office, AUT, 2025-11-27) Ramesh, Kavya; Nguyen, Minh
This study investigates how AI-integrity initiatives are put into practice in higher education by focusing on IT project management, governance, and the delivery of educational technology. It explores the development of the Egg & Basket Quiz prototype as an example of technology-based learning intervention, while also addressing gaps in institutional policy and integrity. The research uses a mixed methods design to collect both quantitative and qualitative data from 25 students and 3 faculty members. The results reveal that AI is frequently used, with 72% of participants using it weekly. There is also uncertainty about institutional rules, which points to project risks related to governance, ethical compliance, and stakeholder involvement. The study is framed as a project of organizational change and digital transformation, based on PMBOK principles for managing risks and stakeholders, the Technology Acceptance Model (TAM) for user engagement, and insights from academic integrity literature. The findings show that adopting AI-integrity requires careful planning, leadership, stakeholder involvement, risk management, and step-by-step solution development, which are all key elements of IT project management. This research offers a project-management perspective on implementing ethical AI-integrity initiatives in higher education.
Global Knowledge Management Practices in Information Technology: A Multivocal Literature Review
(Project Management Research Office, AUT, 2025-11-27) Veerabhadrachar, Deepa; Lal, Ramesh
The rapid pace of technological advancement has created a notable readiness gap within organizations. On one side, are experienced professionals who bring invaluable expertise yet often struggle to adapt to emerging technologies; on the other are recent graduates who possess up-to-date technical skills but lack practical organizational knowledge. This imbalance is frequently reflected in cycles of layoffs and new hires triggered by technological transitions. To mitigate this challenge, knowledge management must adopt specialized strategies that support both the upskilling of new entrants and the reskilling of seasoned employees, thereby strengthening project outcomes and sustaining competitiveness in the marketplace. This study examines the frameworks required for effective knowledge capture and dissemination, with attention to both explicit and tacit forms of knowledge. It highlights the critical role of technological tools and the value of diverse learning environments in facilitating the transfer of tacit knowledge, widely recognized as a key source of competitive advantage. Furthermore, the research explores how organizations can refine their knowledge management approaches to improve workforce performance, drawing on contemporary literature at the intersection of technology and knowledge management. The findings underscore the need for organizations to continuously align their knowledge management systems with evolving business objectives and technological innovations, including cloud computing and artificial intelligence. By systematically identifying, assessing, and enhancing knowledge management practices within project management, this research seeks to bridge existing knowledge gaps and ensure that organizations remain agile and responsive to shifting market demands and technological change.
Building the Blocks of Being: The Attributes and Qualities Required for Consciousness
(MDPI AG, 2023-06-22) Tait, I; Bensemann, J; Nguyen, Trung
For consciousness to exist, an entity must have prerequisite characteristics and attributes to give rise to it. We explore these “building blocks” of consciousness in detail in this paper, which range from perceptive to computational to meta-representational characteristics of an entity’s cognitive architecture. We show how each cognitive attribute is strictly necessary for the emergence of consciousness, and how the building blocks may be used for any entity to be classified as being conscious. The list of building blocks is not limited to human or organic consciousness and may be used to classify artificial and organisational conscious entities. We further explore a list of attributes that seem intuitively necessary for consciousness, but on further investigation, are neither required nor sufficient. The building blocks do not represent a theory of consciousness but rather a meta-theory on the emergence and classification of consciousness.
Experimental and Numerical Evaluation of Rocking Column Bases With Friction Connections and Vertical Web Plates
(Elsevier BV, 2025-10-24) Zhang, HT; Zhang, R; Yan, Zhenduo; Xie, JY; Liu, J; Xiang, P; Zhao, X; MacRae, GA; Clifton, GC; Dhakal, RP; Ramhormozian, Shahab; Rodgers, G; Quenneville, P; Jia, LJ
This paper investigates seismic performance of a low-damage steel rocking column base through experimental and numerical approaches. Asymmetric friction connections (AFCs) were designed at the column base with friction sliding parallel to the column flange. A pair of vertical web plates, which were shop welded to the shear keys then bolted into the foundation, were sandwiched the column web to restrain ultimate uplift and enhance the weak-axis performance. Quasi-static cyclic tests were conducted to investigate the as-built (INITIAL case), post-earthquake (AFTERSHOCK case) and immediate repaired (REPAIR case) performances in both strong and weak axes. It was found that with the presence of the vertical web plate, the moment resistance was increased by 10 % and 8 % for the strong and weak axes, respectively. The prying effect of vertical web plates well ameliorated the initial stiffness loss in the weak axis and can be more easily repaired to the as-built condition. A finite element (FE) model, verified by experimental results, was developed to quantitatively evaluate local plasticity, loss of AFC bolt pre-tension, and energy dissipation. Further analysis revealed the effect of the time-varying axial force on the hysteresis of rocking column base. In addition, in the weak axis, the axial shortening due to localized plastic deformation at flange tips led to a rocking interface with an arc edge and shifted the rocking pivot toward the column's neutral axis, thereby reducing rocking threshold and seismic performance.

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