Recent Submissions
Digital Collaboration through BIM and its Influence on Project Success in Socio-Economic Contexts
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Liyana Arachchi, M.P; Perera B.K.C; Lokupanagodage, T
Building Information Modelling (BIM) has been a real revolution for the traditionally fragmented construction industry. Though BIM has emerged over a few decades, many users still find it challenging to utilise the exact benefits, especially in the aspect of design collaboration. Few scientific studies have been conducted in forming and enabling design collaboration environments in BIM projects. However, the impact towards project performance from BIM design collaboration is not addressed, which has hindered the practical applications and attainment of the benefits in construction projects. Therefore, this study attempts to understand the means of successful utilisation of design collaboration for improving project performance in a BIM-enabled environment. From the literature review, 12 project performance parameters were discovered. The level of design collaboration and their impact towards BIM project parameters were reviewed through the sequential exploratory mixed method. Initially, 8 semi-structured interviews and later a questionnaire survey were conducted. Findings revealed that the achievement of the design collaboration among different parties in a construction project differs, especially in terms of forming a BIM team, guidelines and management of a digital common data environment. Yet it is, found that many people have experienced a positive impact from design collaboration to project performance via BIM. Among all, digital coordination, project schedule performance, and stakeholder satisfaction record the highly influential project performance parameters. Further, a few strategies are proposed to identify barriers in achieving design collaboration for overcoming the challenges. The findings of this study promote BIM adoption for achieving positive project performance in the real.
Advanced Three-Axis Shake Table System for Comprehensive Earthquake Simulation and Structural Dynamics Research
(Institute of Electrical and Electronics Engineers Inc., 2025-10-27) Iddamalgoda, A; Sashik, D; Jayakody, A; Manchanayake, N; Abeygunawardhana, P.K.W; Prasanna, R
The development of a three-axis shake table is essential for advancing research in sensor validation, earthquake engineering, and structural dynamics. Traditional shake tables that exist provide one or two-dimensional motion where it limits the capability of three-dimensional (3D) movements in experiments conducted by engineers, researchers and users. This paper presents the design and development of a cost-effective 3-axis shake table which has the ability to produce independent motions in the X, Y and Z directions. The shake table consists of three dynamic plates, each axis supported by dual linear guides and driven by separate DC motors with a 12V voltage rating. This independent configuration of the axis allows the users for a precise control and generation of vibrations across all three axes, making it suitable for a wide range of testing applications. The design utilizes motor drivers for varying power to the motors and control the movement of the dynamic plates independently, which omits the requirement of encoders and reduce the production cost. The developed shake table offers a versatile and cost-effective solution for laboratories, earthquake engineering and structural health monitoring, where 3D motion capabilities are essential.
Intelligent Traffic Signal Optimization using Sound-Based Ambulance Detection and Green-Light Extension
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Wijesiri, P; Lokuliyana, S; Jayasena, S; Wijesiri, S; Jayakody, A; Abeygunawardhana, P.K.W
Urban traffic congestion significantly hinders ambulance mobility, particularly at intersections where static signals restrict timely passage. This study presents a sound-based intelligent traffic signal optimization framework that integrates siren detection with adaptive green-light extension. The system utilizes IoT audio sensors with Mel-Frequency Cepstral Coefficient (MFCC) feature extraction and a Support Vector Machine (SVM) classifier, achieving 98% detection accuracy in distinguishing ambulance sirens from urban noise. Direction identification reached 96.3% accuracy, while distance estimation maintained an average error of 5.8 m. A novel dynamic green-light timer, incorporating speed, distance, and error-correction metrics, reduced unnecessary signal holding by over 50%, lowering cross-traffic disruption from 5% to 2%. Field tests demonstrated sub-second latency and effective convoy handling for multiple ambulances. These results establish the framework as a lightweight, scalable, and cost-effective solution for real-time ambulance prioritization in resource-constrained urban environments.
Analysis of Deep and Graph Neural Networks for Enhanced DDoS Attack Detection: A Pathway to Hybrid Models
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Athif, S; Sharukesh M; Nidish S.R; Vignesh V.S; Amrita, S.S; Sasi, D; Jayakody, D.N.K
Distributed denial-of-service (DDoS) attacks pose significant threats to network security, targeting critical infrastructures with increasing complexity. Traditional defense mechanisms that depend on static rules and signatures struggle to address the dynamic nature of these attacks. Software-defined networking (SDN), integrated with machine learning (ML) and deep learning (DL) techniques, has emerged as a promising solution for DDoS detection and mitigation. This paper presents a comparative analysis of deep neural networks (DNNs) and graph neural networks (GNNs) within an SDN framework to detect DDoS attacks. Using real-time network monitoring and traffic anomaly identification, the models are evaluated using metrics such as accuracy, precision, recall, and F1-score against both known and novel attack patterns. Our findings underscore the superiority of GNNs in capturing complex and interconnected relationships within network traffic, offering enhanced detection capabilities. This study contributes to adaptive cybersecurity strategies, advancing the application of ML models in combating evolving cyber threats.
Secrecy Rate Performance Evaluation for Next-Generation Wireless Networks
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Chaudhary, U; Ali, M.F; Rajkumar, S; Jayakody, D.N.K
Secrecy rate optimization has emerged as a critical aspect of secure wireless communications to ensure confidential data exchange in the presence of eavesdroppers. However, increasing reliance on wireless technologies has also raised concerns about security and privacy. This paper demonstrates an analysis of secrecy performance through SNR and channel capacity, including the derivation of closed-form expressions and SOP under various channel conditions. The study assumes a uniform distribution for the location of the eavesdroppers, reflecting realistic interception scenarios. The calculated SOP serves as a crucial indicator of the likelihood that the secrecy rate drops below a secure threshold, thus highlighting potential vulnerabilities. To validate the analytical results, extensive simulations are conducted that demonstrate the accuracy and effectiveness of the derived expressions in different wireless environments. The findings underscore the importance of secrecy rate analysis in the design of secure wireless systems, providing valuable insights for optimizing information transmission while protecting against eavesdropping threats.
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