https://repositori.mypolycc.edu.my/jspui/handle/123456789/6660 2026-10-19T08:43:49Z 2026-10-19T08:43:49Z Eybpoosh Chan https://repositori.mypolycc.edu.my/jspui/handle/123456789/11050 2026-10-19T05:52:57Z 2026-01-01T00:00:00Z

Title: AUTOMATION AND EQUIPMENT OPTIMIZATION STRATEGIES ENHANCING PRODUCTIVITY IN LARGE SCALE CONSTRUCTION OPERATIONS Authors: Eybpoosh Chan Abstract: Purpose – This study examines automation and equipment optimization strategies that enhance productivity in large-scale construction operations. It explores how robotics, artificial intelligence (AI), Internet of Things (IoT), and data-driven equipment management improve operational efficiency and project outcomes. Design/methodology/approach – A conceptual analytical approach is adopted, supported by empirical findings from prior studies. The study synthesizes research evidence on automation technologies and equipment optimization frameworks and integrates them into a structured productivity enhancement model. Findings – The analysis indicates that autonomous equipment, AI-based scheduling, IoT-enabled predictive maintenance, and robotics significantly improve productivity, reduce downtime, and enhance safety. Integrated optimization strategies increase equipment utilization rates and improve decision-making efficiency in large scale operations. Practical implications – Construction firms can improve cost efficiency, minimize delays, and enhance safety compliance by adopting structured automation frameworks and real-time equipment monitoring systems. Originality/value – This paper integrates automation technologies and equipment optimization strategies into a unified framework tailored specifically for large-scale construction productivity enhancement.

2026-01-01T00:00:00Z Eoghan, Shane https://repositori.mypolycc.edu.my/jspui/handle/123456789/11049 2026-10-19T05:51:21Z 2026-07-01T00:00:00Z

Title: OPTIMIZING GEOSPATIAL ALIGNMENT THROUGH PARTICIPATORY CAMPUS POSITIONING ASSESSMENTS ENHANCING INFRASTRUCTURE ADAPTABILITY OUTCOMES Authors: Eoghan, Shane Abstract: The increasing complexity of campus environments has intensified the need for accurate geospatial alignment to support infrastructure planning, navigation, accessibility, and sustainable development. Participatory campus positioning assessments have emerged as an effective approach for integrating stakeholder perspectives with geospatial technologies to improve the quality of infrastructure related decision-making. This study explores the role of participatory positioning assessments in optimizing geospatial alignment and enhancing infrastructure adaptability outcomes within higher education institutions. The research adopts a conceptual author-study approach supported by an extensive review of scholarly literature on geographic information systems, participatory mapping, spatial data integration, smart campus development, and infrastructure resilience. The findings indicate that collaborative geospatial assessment improves positional accuracy, strengthens stakeholder engagement, supports adaptive infrastructure management, and enhances institutional preparedness for changing environmental and operational conditions. The study further identifies critical success factors, including digital mapping technologies, stakeholder collaboration, spatial data quality, and continuous monitoring mechanisms. The proposed framework demonstrates how participatory positioning can contribute to efficient campus planning, improved accessibility, optimized resource allocation, and sustainable infrastructure development. The study concludes that integrating participatory geospatial assessment into campus infrastructure planning offers practical opportunities for creating adaptive, resilient, and data-driven educational environments capable of responding to evolving institutional needs.

2026-07-01T00:00:00Z Dinesh Kumar Jamatia Ashim Kanti Dey https://repositori.mypolycc.edu.my/jspui/handle/123456789/11048 2026-10-19T05:50:01Z 2026-09-01T00:00:00Z

Title: FEATURE TOKENIZER TRANSFORMER BASED ADVANCED DEEP LEARNING METHOD FOR PREDICTION OF THE BEARING CAPACITY OF BRANCHED PILES ON PEAT Authors: Dinesh Kumar Jamatia; Ashim Kanti Dey Abstract: Peat is regarded as one of the most complex category of soil, in terms of construction, owing to its very soft and highly compressible nature. Thus, in order to overcome shortcomings of peat, deep foundations are most commonly adopted to transfer load through soft deposits to harder strata. However, such techniques may be uneconomical, particularly for low to medium-rise structures, pavements, embankments, and other lightly loaded critical infrastructures. This shortcoming is of particular concern especially in peat deposits, where the highly compressible peat can induce substantial deformation even under relatively light structural loads. Hence, there is a need to develop alternative ground improvement techniques that are cost-effective, practically feasible, and capable of mitigating excessive deformation associated with peat while addressing limitations of conventional stabilization methods. In this regard, the present study evaluates the bearing capacity of peat reinforced with branched piles. Large-scale experimental studies were conducted to investigate the effectiveness of the proposed technique under controlled loading conditions, while machine learning (ML) techniques were simultaneously incorporated to develop predictive models for estimating bearing capacity and to assess the influence of key soil and pile parameters on the overall foundation response. To predict the bearing capacity of branched piles (BPs) and fine-tune the prediction outcomes, four ML models, namely the Feature Tokenizer Transformer (FTT), Bayesian Regularized Feed Forward Neural Network (BRFFNN), Generalized Regression Neural Network (GRNN), and Multi-Layer Perceptron Regressor (MLPR), were employed. The predictive performance of the four models was comparatively evaluated to identify the most accurate and reliable approach for estimating bearing capacity of branched piles (BPs). Model training was performed using a dataset comprising of 570 experimental observations, each representing five key bearing-related attributes of BPs subjected to static vertical loading. Among the four models, FTT demonstrated the highest predictive accuracy, outperforming the other approaches, as evidenced by the statistical evaluation with a coefficient of determination (R²) value of 0.9940, approaching unity. The high predictive efficiency of the FTT model was further substantiated through rank and sensitivity analyses, which provided insights into the relative importance and influence of the input parameters on BP bearing capacity.

2026-09-01T00:00:00Z Rozić, Željko https://repositori.mypolycc.edu.my/jspui/handle/123456789/11047 2026-10-19T05:48:49Z 2026-09-01T00:00:00Z

Title: SUSTAINABLE MANAGEMENT AND OPTIMIZATION OF WATER-RESOURCE USE IN THE KARST AREAS OF HERZEGOVINA Authors: Rozić, Željko Abstract: The problem of water resource scarcity and water supply in karst areas is significant and still insufficiently researched. Management of water resources is particularly demanding due to the high spatial and temporal variability of available water. The area of Herzegovina belongs to the Dinaric karst, one of the most developed karst regions in Europe. This paper analyzes the problem of sustainable water resource management in karst areas under climate change conditions, with particular reference to the Herzegovina region, for example the Municipality of Posušje and the Tribistovo Reservoir. The specific hydrogeological characteristics of karst, the pronounced seasonal variability of recharge, and the occurrence of longer dry periods can cause a reduction in available water quantities precisely during periods of increased demand. A conceptual dynamic model has been developed based on object-oriented modeling (OOM) and the application of the software system STELLA. The model links climatic and hydrological factors, available water resources, storage conditions, user demands, losses in the water supply system, and indicators of deficit and water supply security. Scenario analysis considers the reference state, the impact of climate change without optimization, and the state with the application of management and optimization measures. The proposed approach enables the assessment of critical periods, the analysis of the relationship between availability and demand, and the evaluation of measures such as loss reduction, consumption management, and optimized use of the Tribistovo Reservoir. The model represents a basis for the development of decision support systems and adaptive management of water resources in karst areas.

2026-09-01T00:00:00Z