Conference papers

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Embargo
Parade in the Virtual Dressing Room
(IEEE, 2018-08) Priyadharsun, S; Lakshigan, S; Baheerathan, S. S; Rajasooriyar, S; Rajapaksha, U. U. S. K; Harshanath, S. M. B
Fashion has always been in the forefront especially with the youngsters. The interest in fashion can vary according to the country, region, culture, age, seasons, climates, places visited, attitude, personal interests etc. Some of them, however, have difficulties finding out about suitable dressing styles for them. Meeting this need is the purpose of this application. On the other hand, social networks are an easy way to interact with the teenagers. In this new age social network site, users create a profile and enter their body measurements to create a virtual model of the particular user. They can also upload their photos to create a complete virtual model which includes face as well. It was necessary to add business value to the application along with the usual entertainment factors. Adding business value to entertainment factors is the main attraction in Fashion Fit to suit a new age of social networking.
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Tempcache: A Database Optimization Algorithm for Real-Time Data Handling in Indoor Spatial Environments
(IEEE, 2018-08-08) Jayakody, A; Murray, I; Hermann, J; Lokuliyana, S; Dunuwila, V. R
The unstable arrangement of modern indoor environments has made navigation within buildings a difficult task. Hence, this paper introduces the AccessBIM framework, which is an efficient real-time indoor navigation system that facilitates in generating a real-time indoor map by crowdsourcing spatial data through the sensors available in mobile devices of navigators. The framework is equipped with a database optimization algorithm known as “Tempcache” which reduces the time and cost of searching data by examining the AccessBIM database for previously navigated paths, thus enabling faster data retrieval through efficient query processing. A simulation of a virtual environment similar to an actual indoor environment was used to test the algorithm. The significance of the algorithm was validated by comparing the total map generation time before and after the algorithm was applied for which the results demonstrated a reduction in map generation time with the use of the algorithm. The framework is also capable of capturing localization information with the support of i-Beacons which is then stored in a cloud server.
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Policies Based Container Migration Using Cross-Cloud Management Platform
(IEEE, 2018-12-21) Janarthanan, K; Peramune, P. R. L. C; Ranaweera, A. T; Krishnamohan, T; Rupasinghe, L; Sampath, K. K; Liyanapathirana, C
Over the last decade, cloud computing has helped in variety of ways to humanity. Mainly in the ways of, achieving Disaster Recovery (DR) and in protecting the end users' data and Anywhere, Any device, Anytime access to the users' data. This research further helps people and organization to overcome common problems related to clouds such as, vendor-lock in and legal regulation. In today's world, more and more organizations are adopting the cloud services mainly because of the reliability and affordability provided by them. However, there are several drawbacks faced by the cloud users and cloud service providers. Apart from the security perspective, the cloud users are facing challenges in control and visibility, lack of standard service interfaces, difficulty in deploying applications across multiple clouds and vendor lock-in. Also, cloud service providers are facing challenges in degradation of the quality of service provided because of the distance between cloud data center and the end user and unexpected interruption of services etc. The above problems can be reduced to a greater extent or mitigated by adopting Multi Cross Cloud Infrastructure. This benefits the cloud users to receive the best quality services to increase their productivity. Hence, the main aim of this research is to build a common platform to manage the cross-cloud environment particularly Microsoft AZURE cloud and Amazon Web Services (AWS) with multiple features such as policies based container migration among the clouds and finding the best virtual machines (VM) across the clouds to deploy new containers. Cross-cloud management platform can be implemented within an organization or Enterprise and is used by the 3rd level support team such as Infrastructure team to provide multiple services (E.g. - Delivering application containers, Migration of containers on request) to end users based on some service level agreements (SLA) with more control and visibility.
Embargo
IoT-Based Smart Hydroponics for Automated Nutrient, Climate, Irrigation, and Health Monitoring
(Institute of Electrical and Electronics Engineers Inc., 2025) Ashik M.A.M; Bogahawatta C.A; Perera M.R.D; Dassanayake D.R.I.P; Jayakody, A; Gamage, N
This study presents HydroNutraLeaf as a selfgoverning hydroponic tower system built with Internet of Things technology to automate the critical aspects of hydroculture farming by uniting water supply management with environmental control and watering systems and plant health monitoring capabilities. The system unites multiple essential components to operate as one unit. The system incorporates an automatic plant disease detection system through real-time image acquisition which uses Convolutional Neural Network (CNN) algorithms and cloud-based warning protocols for classification purposes. An automated system comprising Raspberry Pi actuators, NPK sensors, and machine learning functions delivers nutrients at proper stages during plant growth. A reinforcement learning system directs the management of climate factors including temperature and humidity together with Light Emitting Diode (LED) spectrums to achieve superior yield production and product quality. The system includes a self-operated irrigation system with Electrical Conductivity (EC), potential of hydrogen (pH) regulation features which utilizes SVM-based prediction methods in combination with real-time monitoring to achieve optimum root environment conditions. Users can access a dashboard in Grafana to monitor and control the system by using cloud platforms which include Firebase and AWS. The experimental findings reveal water consumption decreased by 30% along with improved nutritional efficiency reaching 25% and enhanced crop yield reaching 15% with better health performance. The sustainable farming operations and commercial greenhouse implementation benefit from HydroNutraLeaf solution which operates through a scalable model based on data analysis and requires minimal human intervention.
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Deep Learning Based Sinhala Sign Language Recognition
(Institute of Electrical and Electronics Engineers Inc., 2025) Samarakoon, S.C; Weerasinghe, M
Deaf individuals in Sri Lanka rely primarily on Sinhala Sign Language (SSL) for communication due to hearing impairments. However, effective communication between the Deaf and hearing populations remains challenging due to the limited knowledge of SSL among hearing individuals. This research aims to address this gap by developing an SSL gesture recognition system using computer vision and deep learning techniques. Specifically, the study compares the performance of 3D Convolutional Neural Networks (3D-CNNs) and a hybrid 2D Convolutional Neural Network with Long Short-Term Memory (2D-CNN+LSTM) for classifying short-duration spatiotemporal SSL gestures. Additionally, the research emphasizes reducing computational complexity to ensure efficient operation of the system on low-end devices. These contributions advance the accessibility and practical usability of gesture recognition systems for the Sinhala Sign Language.
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Project HyperAdapt: An Agent-Based Intelligent Sandbox Design to Deceive and Analyze Sophisticated Malware
(Institute of Electrical and Electronics Engineers Inc., 2025) Perera, S; Dias, S; Vithanage, V; Dilhara, A; Senarathne, A; Siriwardana, D; Liyanapathirana, C
Malware increasingly employs sophisticated evasion techniques to bypass sandbox-based analysis, rendering traditional detection methods ineffective. This research presents Project HyperAdapt: Agent-Based Intelligent Sandbox, a framework that integrates both offensive and defensive machine learning models to enhance malware detection, deception, and behavioral analysis. The offensive RL model generates evasive malware samples, challenging the sandbox, while the defensive models including hybrid evasion detection, GAN-based behavior simulation, and a dynamically adapting RL agent work collectively to improve sandbox resilience. By continuously learning from evasive malware behavior, the defensive RL agent adapts in real-time, strengthening detection capabilities. Experimental results demonstrate that this approach enhances sandbox effectiveness, ensuring long-term adaptability against evolving malware threats.
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An Integrated Deep Learning Framework for Early Detection of Vision Disorders
(Institute of Electrical and Electronics Engineers Inc., 2025) Jayathilaka, S; Balaruban, D; Kumanayake, I; Elladeniya, A; Wijendra, D; Krishara, J; De Silva, M
Vision impairment due to retinal diseases like Diabetic Retinopathy (DR), Age-Related Macular Degeneration (AMD), Glaucoma, and Retinal Vein Occlusion (RVO) poses a significant health challenge in Sri Lanka, where these conditions are leading causes of blindness. This research presents a novel multi-disease prediction system leveraging advanced deep learning techniques for early detection of DR, AMD, Glaucoma, and RVO. The study utilized publicly available datasets, including retinal fundus images from repositories such as RFMiD, IDRiD, APTOS validated by medical professionals to ensure diagnostic reliability. These images were preprocessed and augmented to train robust convolutional neural network (CNN) models tailored to each disease. The predictive models were developed and optimized using hybrid architectures, integrating attention mechanisms and feature fusion for enhanced performance. This approach achieved high accuracies 93% for DR, 92% for AMD, 94% for Glaucoma, and 94% for RVO demonstrating robustness and consistency across diverse retinal conditions. To validate real-world applicability, the models underwent further testing in clinical settings using a Sri Lankan dataset, reflecting local disease prevalence and imaging conditions. By combining validated public data with clinical testing, this scalable system supports ophthalmologists in early diagnosis, reducing diagnostic delays and improving patient outcomes. This work offers a reliable, innovative solution to mitigate the burden of blindness in Sri Lanka and beyond.
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Low-Cost, High-Precision Vibration Analysis: Enhancing SHM and Seismic Data Acquisition Systems
(Institute of Electrical and Electronics Engineers Inc., 2025) Sashik, D; Iddamalgoda, A; Manchanayake, N; Prasanna, R; Abeygunawardhana, P.K.W
The development of a low-frequency vibration detection device is essential for testing vibrations in crucial applications such as structural integrity, seismic activity detection and industrial machinery maintenance. Current data acquisition systems suffer from lack of sensitivity and high cost. This paper presents the design and development of a low-cost vibration detection device that uses a 3-axis accelerometer and a geophone with a resonant frequency of 4.5 Hz, where the signals are filtered using a second order low-pass filter with a 250 Hz cutoff frequency. The device is integrated with a 24-bit analog to digital (ADC) converter for ensuring a precise data acquisition and the device ensures to monitor real-time vibrations of x, y and z axis using a accelerometer and the z axis with a geophone. The effectiveness of the device was validated via experimental simulations. This developed device is a solid and cost-effective solution for engineers and professionals, where reliable low-frequency vibration monitoring is required in critical applications.
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Blockchain-Based Custody Evidence Management System for Healthcare Forensics
(Institute of Electrical and Electronics Engineers Inc., 2025) Jayasinghe R.D.D.L.K; Sasanka M.W.K.L; Athukorala D.A.S.M; Sandeepani M.A.D; Jayakody, A; Senarathna, A
As digital evidence increasingly growing in significance in healthcare forensics, safeguarding sensitive medical data's confidentiality, integrity, and limited access remains to be an important issue. Existing forensic evidence management systems are subject to data breaches and illegal access since they frequently lack significant privacy-preserving measures. In order to overcome such challenges, this research suggests a Blockchain-Based Custody Evidence Management System for Healthcare Forensics, which combines blockchain technology, machine learning, and encryption methods to improve security, privacy, and accessibility. To ensure accurate and efficient gathering of information, machine learning algorithms are used to extract handwritten and printed text from medical photographs. AES encryption ensures safe storage, while Fully Homomorphic Encryption (FHE) is used for dynamic access level control to protect gathered evidence. Identity verification is made possible via a web-based authentication system that uses Zero-Knowledge Proofs (ZKP) to protect privacy by preventing the disclosure of personal data. By preventing unintended modifications, blockchain technology is used to preserve the custody chain's integrity. Furthermore, machine learning-driven PII detection and masking methods balance the requirement for forensic investigation with privacy compliance by controlling data accessibility according to access entitlements. Based on permitted access levels, the system makes it possible to share safe evidence with law enforcement agencies, such as courts, the police, and other forensic groups. Using blockchain to guarantee data immutability, cryptographic security to restrict access, and artificial intelligence (AI) to safeguard data, this approach enhances the privacy, security, and dependability of handling forensic evidence in medical investigations
Open Access
Model Optimization for Personalized Health Metrics Analysis
(Institute of Electrical and Electronics Engineers Inc., 2025) Perera, M; Wijesiriwardena, A; Pathirana, A; Gamaathige, L; Wijesiri, P; Jayakody, A
This paper investigates the development and application of four machine learning models designed to enhance personalized health management, specifically targeting young adults aged 15-30. The research addresses common health challenges, such as obesity and lifestyle-induced diseases, through data-driven methodologies that provide personalized meal plans, workout recommendations, and progress monitoring. The first model generates optimized personalized recommendations according to the user's health condition using Random Forest and Decision Tree algorithms. The second model utilizes an ensemble of Random Forest, LightGBM, and XGBoost, combined through a stacking technique with Linear Regression as the meta-model, to generate optimized personalized meal plans according to health condition. The third model generates optimized workout plans using Gradient Boosting and XGBoost classifiers, accounting for individual fitness objectives, body compositions, and medical conditions. A fourth model predicts goal achievement timelines by analyzing features such as caloric balance and hydration efficiency, providing users with actionable feedback using XGBoost. The integration of these AI-driven components into a scalable digital platform demonstrates the potential of machine learning in transforming health management. Future enhancements include improving model accuracy, enabling real-time feedback, and deploying the system as an accessible mobile application. ensemble
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Enhancing the Performance of Supply Chain using Artificial Intelligence
(Institute of Electrical and Electronics Engineers Inc., 2025) Wijedasa, S; Gnanathilake, K; Alahakoon, T; Warunika, R; Krishara, J; Tissera, W
Optimizing warehouse operations is essential to meet dynamic customer demands while maintaining efficiency in the rapidly changing supply chain landscape. Using four key components, this research presents a comprehensive AI-based approach to improve supply chain management performance. The first component uses Long Short-Term Memory (LSTM) networks to predict demand and returns, allowing for accurate forecasting of product demand and returns based on historical sales data. The second component uses Q-learning, a Reinforcement Learning (RL) technique that optimizes the scheduling of product replenishments by prioritizing critical stock shortages based on inventory levels and predicted demand. The third component analyzes customer purchasing patterns using FP Growth and clustering algorithms to analyze customer buying patterns, strategically placing items in aisles to reduce selection time and improve picking efficiency. The final component involves customer churn prediction using machine learning techniques to identify at-risk customers and facilitate proactive retention strategies. To bridge the gap between complex AI models and practical warehouse operations, a web-based application named 'OptiFlow AI' has been developed. This platform provides warehouse workers with user-friendly interfaces to access demand forecasts, replenishment priorities, optimized product placements, and customer retention insights. The proposed system significantly enhances operational efficiency, reduces time delays, and improves customer satisfaction, contributing to a more resilient and intelligent supply chain ecosystem.
Open Access
An integrated data-driven approach for Chronic Kidney Disease of Unknown Etiology (CKDu) risk profiling and prediction in Sri Lanka
(SPIE, 2025) Rajapaksha, N; Rajawasan, H; Ubeysinghe, R; Perera,S; Swarnakantha, N.H.P.R.S; Gamage, M; Nanayakkara, N; Wijayakulasooriya, J; Herath, D; Lakmali, M
Chronic kidney disease of unknown etiology is a significant public health issue in Sri Lanka, especially in rural farming communities. The exact causes remain unclear, with potential links to environmental and socio-economic factors. This research employs Biological Data and Geographic Information Systems to analyze risk factors such as water quality, agricultural practices, climatic conditions, Demographic Factors, Socio-economic Factors. This study uses data from government health records, the Centre for Research-National Hospital Kandy, and field surveys. By identifying patterns and correlations, the study aims to inform public health interventions and reduce the impact of CKDu, ultimately improving health outcomes for affected populations. This will greatly contribute to preventing the disease, reducing the risk, and identifying patients at an early stage.
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LifeBeacon: Offline Emergency Communication and Victim Detection System for Disaster Areas
(IEEE Computer Society, 2025) Senaratna S.M.T.S; Widanage W.T.N; Muhandiramge M.D.A.D.; Bandara H.K.K.T; Pandithage, D; Abeygunawardhana, P; Wijayasekara, S
Natural disasters often compromise traditional communication infrastructure, significantly delaying emergency response and coordination efforts. This research presents a novel, disaster-resilient communication system designed to address these challenges through the integration of mobile ad-hoc networking and Wi-Fi-based victim localization. The proposed system comprises of a decentralized, infrastructure-less, self-healing mobile ad-hoc network (MANET) utilizing Bluetooth Low Energy (BLE) and Wi-Fi direct with an encrypted SOS messaging mechanism between end devices and a Wi-Fi probe request-based end device detection mechanism that estimates the location of affected individuals based on their mobile phones. If nodes disconnect, a self-healing algorithm guarantees automatic reconnection and uninterrupted message flow, while the ad-hoc network facilitates low-power, peer-to-peer communication without the need for traditional infrastructure. Both targeted and broadcast notifications are supported by the SOS messaging system, which is encrypted to protect the integrity and security of data. The victim localization system, meantime, makes precise estimates of the population and location of people in the disaster region using trilateration and Received Signal Strength Indicator (RSSI). Experimental evaluations conducted under simulated disaster conditions demonstrate the system's scalability, energy efficiency, and effectiveness in maintaining real-time communication and accurate victim tracking.
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An Adaptive E-Learning Platform for Individuals with Down Syndrome
(Institute of Electrical and Electronics Engineers Inc., 2025) Sandaruwan U.V.S.; Dias A.H.J.S.S; Shamindi H.M.H; Priyawansha N.G.D.; Chandrasiri L.H.S.S; Attanayaka B.
Children with Down Syndrome (DS) encounter varying degrees of learning disabilities within the traditional education framework, requiring personalized interventions. This paper presents Blooming Minds, an adaptive, Machine Learning (ML) driven e-learning platform designed to support the development of cognitive, linguistic, and motor skills in children with DS. Built on the VARK (Visual, Auditory, Reading/Writing, Kinematic) theory, the platform provides personalized activities using real-time feedback mechanisms. The system includes nine interactive modules that cover the above VARK theory. It uses ML algorithms, including Support Vector Machine (SVM) and Random Forest (RF) for screening, Convolutional Neural Networks (CNN) for handwriting and speech analysis, Long Short-Term Memory (LSTM) for sequence prediction, and Reinforcement Learning (RL) for adaptive difficulties. Handwritten letters and voice samples from children with DS, both domestic and international, were specifically considered as inputs for this research. Progress tracking dashboards provide visual insights for educators, parents, and caregivers, improving support and adaptability. The system achieved 91.26% accuracy in letter recognition and 88% in speech classification. This e-learning platform has been recognized as an effective solution in Sri Lanka, allowing for further correlations and investigations to assess the knowledge capacity and ability to express that knowledge in children with DS
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Intelligent Adaptive Lighting Control: Reinforcement Learning-Based Optimization for Smart Home Energy Efficiency
(Institute of Electrical and Electronics Engineers Inc., 2025) Hewakapuge M.M; Gamage W.G.T; Surendra D.M.B.G.D; Thejan K.G.T; Rajapaksha, S; Rajendran, K
This study introduces a novel research paper outlining a behavioral-based adaptive lighting system that aims to revolutionise smart home lighting by integrating user behavior tracking to enhance energy efficiency and user comfort. Unlike traditional motion-sensor-based lighting, the novelty of this approach is the ability to adapt dynamically to evolving user behaviors through reinforcement learning. The system utilises Wi-Fi-based positioning, GPS and accelerometer data to monitor user movements and classify different areas of the house. Users initially calibrate the home layout through a mobile application, marking room locations and lighting configurations. The system then collects movement data over time to predict optimal lighting schedules based on user routines and refines the predictions and updates lighting adjustments accordingly, minimising energy wastage while maximising user convenience. A serverless backend architecture ensures scalability, cost-effectiveness, and seamless data processing. The adaptive framework continuously refines lighting automation, responding to evolving behavioral patterns.
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SmartSphere: Bridging AI-Powered Automation and Tesla Coil Wireless Energy for Smarter Living
(Institute of Electrical and Electronics Engineers Inc., 2025) Jurison Jenaraj, J.K; Umasuthan, A; Vigneswaran, V; Raveendran, K; Thiruthanigesan, K; Kasthuriarachchi, S
This paper presents SmartSphere, an intelligent, secure, and wire-free home automation system that synergizes artificial intelligence, multi-factor access control, and resonant wireless power transmission. Built on an edge-computing architecture using ESP32 and Arduino Mega 2560 microcontrollers, SmartSphere integrates facial recognition, fingerprint authentication (96.7% accuracy), and IR-based presence detection to reduce false activations by 75%. The system employs OpenCV and TensorFlow Lite for real-time anomaly detection and environmental personalization, including weather-responsive adjustments and emotion-aware lighting via facial expression analysis. A key innovation is the incorporation of a Tesla coil-based wireless power transmission module, which eliminates conventional wiring constraints and reduces installation cabling by 86%. Experimental validation demonstrates a 14% improvement in energy efficiency, 75% faster response time (0.3 s), and seamless compatibility with 92% of tested IoT devices. Through comprehensive testing and validation, this research establishes SmartSphere as a secure, intelligent, and sustainable solution for next-generation smart homes, addressing the limitations of traditional wired systems while enhancing the user experience through AI-driven personalization.
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The Impact of Interior Design Environment on Employee Satisfaction: An Insight on State Offices in Sri Lanka
(Springer Science and Business Media Deutschland GmbH, 2025) Kalpani K.I.; Ratnayake J.C; Wimalaratna P.L.; Wijesundara J
Job satisfaction is crucial in corporate settings, as it influences employees’ attitudes and performance. While previous studies have highlighted the importance of workplace conditions on job satisfaction across various countries, there is a notable lack of research within the Sri Lankan context, particularly in state offices. This research investigates the factors affecting employee satisfaction in Sri Lankan state offices, with a specific emphasis on interior design environment. The study aims to determine how specific interior design environmental cues impact employee satisfaction. Based on a comprehensive literature review, the independent variables identified include floor layout, furniture arrangement, lighting, colour scheme, air temperature, noise and acoustics. This study employs a mixed-method approach, combining quantitative and qualitative data, to explore the impact of the interior design environment on employee satisfaction in three high-profile state offices in Colombo and Sri Jayewardenepura. Primary data were collected through observations and structured questionnaires distributed across various departments, yielding 50 responses from each office, resulting in a total sample size of 150 participants. On-site measurements for lighting levels, temperature, and noise levels, were taken, while furniture, colour, and floor layout were assessed through visual inspections. Questionnaire responses were analysed using SPSS statistical software. The research found that floor layout, furniture, lighting, and colour significantly impact employee satisfaction, whereas temperature and noise have minimal impact. The study offers design recommendations to improve state office environments, emphasizing the importance of passive design techniques to enhance user-friendliness and environmental sustainability, ultimately increasing employee satisfaction. This research fills a critical gap in the literature and provides practical insights for improving the working conditions in Sri Lankan state offices.
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Predictive Modeling for Identifying Early Warning Signs of Underperformance in Vocational Education
(Institute of Electrical and Electronics Engineers Inc., 2025) Hettiarachchi D.S.S; Harshanath S.M.B
This study focuses on developing a predictive modeling system to identify early signs of underperformance in vocational education, critical for building a skilled workforce. Addressing challenges like high dropout rates and inadequate graduate preparedness, the system utilizes machine learning techniques such as Neural Networks, Decision Trees, and Logistic Regression. Implemented in Python, it analyzes key features like academic records, attendance, engagement, and socioeconomic factors. Preprocessing steps, such as data cleaning and feature engineering, were implemented, and transfer learning was employed to adapt the model. This combination of feature engineering and transfer learning enables the transfer of knowledge from academic settings to vocational education by identifying and leveraging shared characteristics between the two domains. The system provides real time insights through automated reports and notifications, enabling targeted interventions to improve retention and graduation rates. This scalable approach advances educational technology and informs policies to enhance vocational education outcomes.
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The Influence of IT Infrastructure and Supply Chain Flexibility on Supply Chain Performance in the Apparel Manufacturing Sector
(Institute of Electrical and Electronics Engineers Inc., 2025) Kulasekara, D; Sandaruwan, D; Ekanayake, T; Perera, A; Wisenthige, K; Aluthwala, C
In a developing country, the apparel manufacturing sector needs to improve their performance, reduce costs, and satisfy the demands of a highly competitive global market. Thus, Supply Chain Management (SCM), particularly the influence of IT Infrastructure (ITI) and Supply Chain Flexibility (SCF) in driving Supply Chain Performance (SCP), focuses on their combine within the Sri Lankan apparel manufacturing sector. These relationships were evaluated using a quantitative research approach, with data gathered from Supply Chain (SC) professionals in apparel manufacturing companies. The study reveals that SCF mediates the relationship between ITI and SCP, indicating the importance of a flexible SC in moving ITI investments into functional performance improvements. Considering the current environment, apparel manufacturers should apply methods that relate ITI capabilities to the SC ability to respond to changing demands. Technological foundation influences SCF, enabling organizations to respond effectively to market demand and operational challenges. Consequently, SCF significantly contributes to SCP by improving flexibility, reducing costs and enhancing customer satisfaction. Some of the practical implication include the usage of advanced IT systems including prophetic analytics and enterprise resource planning tools to enhance SC responsiveness. In addition, development of collaborative relationship with suppliers and partners can enhance the effect of SCF and SCP, making Sri Lanka apparel manufacturers more competent to perform international standard. Future studies are encouraged to take these findings to other sectors and regions to explore the influence of new technologies and other external factors. In this way, it would be possible to achieve more advancements in the SCM practices in response to the current global market challenges.
Open Access
BARRIERS AND SOLUTIONS TO ADOPTING DIGITAL TWIN IN SRI LANKAN CONSTRUCTION
(Ceylon Institute of Builders, 2025) Indunil H.T.T.A; Bandara R.P.H.S; Buddhini P.H.Y.
In the Industry 4.0 era, Digital Twin (DT) technology has emerged as a key innovation, integrating physical assets with their digital counterparts to enhance coordination, safety, and cost management in construction. Despite its potential, DT adoption in construction remains limited, particularly in developing countries like Sri Lanka, due to high costs, technological barriers, and a lack of standardized integration methods. While DT has significantly improved efficiency in other sectors, the construction industry still lacks practical large-scale applications and a unified framework for implementation. This research aims to explore the applicability of DT in Sri Lankan construction projects. Moreover, semi-structured expert interviews were conducted with 7 experts to gather data. Accordingly, the literature review and insights gathered from interviews highlighted its current usage, benefits, and barriers, and proposed 8 practical solutions to facilitate broader adoption of DT in the Sri Lankan construction industry. The findings of this research are anticipated to contribute significantly to the effective integration of DT technology within the Sri Lankan construction sector. The results underscore the promising role that DT can fulfil in addressing a variety of barriers confronting the construction industry. This study not only emphasizes the critical importance of adopting Digital Twin technology but also seeks to raise awareness of its potential, thereby encouraging its utilization and ushering in a new era of innovation in the Sri Lankan construction industry.