Browsing by Author "Alotaibi, Abdulaziz"

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A DATA ANALYTICS FRAMEWORK TO SUPPORT DECISION MAKING IN RAILWAY INFRASTRUCTURE ASSET MANAGEMENT
(Saudi Digital Library, 2026) Alotaibi, Abdulaziz; Cardenas, Isidro
The process of management of the assets of the railway infrastructure is becoming increasingly dependent on the big amounts of the condition-monitoring information produced by the recent inspection technologies. Although this type of data can give a detailed picture of the track condition, it also brings issues of interpretation, prioritisation and decision making. The current asset-management methods usually are based on evaluating thresholds and disjointed analysis tools, which restrict their strengths in promoting proactive and data-driven maintenance practices. The study creates and assesses a combined visual analytics system in order to aid decision making in the management of railway infrastructure assets. The framework integrates data pre-processing, analytical intelligence, machine-learning, and interactive visual analytics to convert raw track geometry data into actionable decision-support products. The research design was a mixed-methods research design comprising of two large-scale case studies, one of them on the basis of the UK and Saudi Arabian railway networks, and the other one on the basis of expert validation. The data of track geometry measured by Network Measurement Trains and Track Geometry Inspection Vehicles was analysed to prove the relevance of the framework to the different operational and environmental conditions. The case study of the UK is a fully developed, regulation-based data environment whereas the Saudi Arabian case study is a developing network that is functioning in the harsh desert conditions. Findings indicate that the suggested framework improves the interpretability of complex condition data using integrated 2D, 3D, and GIS-based visual analytics. The unsupervised and supervised methods were combined to form machine-learning techniques which enhanced the performance of fault detection and classification and led to quantifiable reductions in false positive alerts compared with the baseline threshold-based methods. A comparative analysis shows that the framework can be adjusted to differences in data maturity, regulatory environment, and operational issues. The study brings on board a transferable and validated visual analytics model that provides the balance between advanced data analytics and feasible decision support in the management of railway infrastructure assets.
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Investigation and Development of Energy Concept for Assessing Impacts to Worker Safety and Work Quality
(Oregon State University, 2024-06-04) Alotaibi, Abdulaziz; Gambatese, John
Many duties and tasks may be physically and mentally demanding for construction workers. The performance of construction workers is critical to a project's success and impacted by various internal and external factors, conditions, resources, and activities. Previous studies have shown that construction worker perceptions of the conditions present on a construction site and task characteristics can influence their mental workload and performance with respect to safety and work quality. However, limited tools are available to construction practitioners to assess and quantify mental workload in order to improve safety and quality based on critical task-level factors. Further research is required to determine how a construction site's environment, operations, and work tasks impact the safety of construction workers and the quality of work due to the mental workload (MWL) that workers experience while performing tasks. The overarching goal of this research is to explore and develop a new method of quantifying and assessing the level of worker MWL using the energy concept to evaluate and predict worker safety and work quality on construction sites. The concept of energy is used as a means to quantify MWL. To achieve the research goal, a conceptual model from previous studies was used as a starting point. Three levels of task characteristics (constituents, components, and metrics) comprised the conceptual model used to measure worker MWL during construction activities. The present study utilized a multi-method approach consisting of insights from construction professionals, perspectives of construction workers, field observations, and a controlled experiment to clarify, identify, and quantify constituents, components, and metrics and confirm the developed tool. Through the incorporation of the constituents, components, and metrics into an assessment process to evaluate MWL using the energy concept, the Assessment Approach of Mental Workload Task Index (MWL-TX) was developed and applied on an actual construction site. The present research contributes to the body of knowledge and benefits the construction industry by enhancing understanding of the connection between task characteristics and work components, and creating an objective method for MWL quantification and evaluation. The current research also contributes to practice by providing a tool that construction practitioners can use to determine the extent to which task-related worker MWL is a concern during work operations and whether there is a potential impact of the MWL on worker safety and work quality.
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Sign-symmetry and frustration index in signed graphs
(Saudi Digital Library, 2023-11-29) Alotaibi, Abdulaziz; Sivaraman, Vaidyanathan
A graph in which every edge is labeled positive or negative is called a signed graph. We determine the number of ways to sign the edges of the McGee graph with exactly two negative edges up to switching isomorphism. We characterize signed graphs that are both sign-symmetric and have a frustration index of 1. We prove some results about which signed graphs on complete multipartite graphs have frustration indices 2 and 3. In the final part, we derive the relationship between the frustration index and the number of parts in a sign-symmetric signed graph on complete multipartite graphs.
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