Chloride ingress and sulphate attack are the primary drivers of Reinforced Concrete (RC) deterioration in coastal-industrial environments. While chlorides trigger reinforcement depassivation, sulphates degrade the concrete matrix through the formation of expansive products. Coastal structures face a simultaneous coupled attack that is significantly more aggressive than single-ion benchmarks. Literature establishes that, for single-ion diffusion, chloride coefficients (Dc) average 10-12 m2/s, while sulphate ingress is markedly slower, often near 10-14 m2/s. In both cases, ionic concentrations decrease with depth, and diffusion coefficients attenuate over time as continuous hydration densifies the pore structure. Combined ion ingress is not a linear process. Initially, the concrete exhibits competitive antagonism, where chloride and sulphate ions share restricted diffusion paths, effectively retarding each other’s penetration by 30–50%. During this stage, reaction products like Friedel’s salt and early ettringite fill the capillary pores, densifying the matrix and improving resistance. However, once these expansive products exceed the concrete's internal tensile capacity, the mechanism shifts. Micro-cracking transforms the matrix into a network of "preferential paths," allowing aggressive agents to bypass diffusion-control and reach the steel rapidly. Furthermore, ingressing sulphates chemically displace bound chlorides from Friedel’s salt due to their higher reactive priority, releasing them as free ions. Simultaneously, sulphate attack consumes portlandite, lowering the pore solution pH. This dual action significantly increases the Cl−/OH− ratio at the reinforcement level, accelerating reinforcement depassivation and electrochemical corrosion in marine structures. Experimental data collected from an industrial RC structure complex located in coastal environment confirms this severity; concrete in high-ingress zones suffered a 25–35% strength reduction, far exceeding the 10–15% loss typical of single sulphate exposure. Half-Cell Potential (HCP) mappings in these zones shifted aggressively to values more negative than -350 mV, signifying a 90% probability of active corrosion. This coupled aggression pushes coastal RC structures into the "Very Severe" exposure category (ACI 201.2R), necessitating advanced multi-ionic diffusion models for accurate service-life prediction.
Fixed wing Unmanned Aerial Vehicle (UAV) aircrafts capable of Automatic Vertical Take-Off and Landing (VTOL) have emerged as a promising solution to the limitations of both conventional fixed-wing and multirotor aircraft. This study presents the design, modelling, and implementation of a radio-controlled fixed-wing UAV which utilize same control surfaces as regular fixed wing aircrafts for vertical take-off and horizontal transition within three seconds under automatic control. Aerodynamic properties were analysed through 3D CAD models and XFLR5 software, and a state-space model was developed for simulations and control law derivation. A custom flight mode was developed to the ArduPlane firmware to enable automatic vertical to horizontal transition and a commercial flight controller board based on 32-bit ARM CPU was used to implement the system. The designed UAV demonstrates improved adaptability and performance for constrained operational environments, making it suitable for surveillance, mapping, environment monitoring and military applications. Experimental results confirm the feasibility of stable vertical take-off and smooth transition using the proposed control strategy.
Sri Lankan road networks have often been blamed for their infrastructure inadequacies and operational inefficiencies. Along these lines, unsignalized junctions face substantial challenges, including prolonged travel time, conflicts among road users, delays, and compromised road safety. Most traffic systems use fixed-time patterns, which are simple yet fail to accommodate fluctuating traffic volumes. To address these hurdles, studies developed adaptive control systems. However, such advanced systems have been underutilized due to high installation and operation costs. This study therefore remedies this limitation by proposing a cost-effective adaptive queue-length-based dynamic signal system (AQuLeDSS)at Julgaha Junction, Galle City, which is encompassed by the intersection of four trunk roads and experiences severe congestion during peak hours. The model was calibrated using field-collected traffic data in the SUMO to improve the fidelity. Key operational parameters were adjusted iteratively, thereby ensuring that the simulated environment closely replicated the observed traffic behaviour at the selected intersection form. The complete control algorithm, along with the You Only Look Once (YOLO)-based vehicle detection module, was implemented in Python in the SUMO environment. Signal phase allocation was determined based on predefined control rules and threshold values. The combined SUMO with Python package triggers AQuLeDSS, employing queue-length-amenable green, red, and vehicle clearance times. Thereunto, results indicate that the intersection capacity is not fully utilised when it is left unsignalized. AQuLeDSS implemented simulations achieved a 60–65% decrease in queue lengths, along with overall economic savings and a 48% reduction in emissions. This article recommends deploying an integrated system using edge-based GPU platforms and a Real-Time Operating System (RTOS)with carved-out zonal hub controls to enhance road safety in urban centres.
Accurate characterization of crack geometry is critical for assessing the durability and serviceability of Reinforced Concrete (RC) structures. Although Deep Learning (DL) has significantly improved automated crack segmentation, its effectiveness in supporting engineering measurements such as crack width remains insufficiently explored. This study proposes an integrated image-based comparative study combining semantic segmentation with quantitative crack width estimation. Four deep learning models, U-Net, Attention U-Net, DeepLabV3+, and YOLOv8-seg were evaluated using a dataset of 2000 annotated crack images under consistent training conditions. Segmentation performance was assessed using standard pixel-level metrics, while crack width predictions were validated against measurements obtained from laboratory-tested RC beams. Results show that encoder–decoder architectures achieve superior boundary delineation, leading to more reliable width estimation. U-Net and Attention U-Net demonstrated the best overall performance with an Intersection over Union (IoU) of 0.741 and 0.740, respectively. In contrast, YOLOv8-seg exhibited significantly lower segmentation accuracy and higher measurement error. The findings confirm that segmentation boundary precision plays a critical role in extracting accurate structural parameters, highlighting the suitability of encoder–decoder models for vision-based Structural Health Monitoring (SHM) applications.
Changes in temperature patterns are strong indicators of how much the climate is changing and is important to society, such as human health, agriculture and food security, water supply, transportation, energy and habitability. Colombo is the financial and administrative centre of the island with a population of over 6 million which signifies need to ascertain the temperature patterns in the area. Objective of this study is to determine recent trends in temperature patterns in Colombo, its negative impacts and the Engineering interventions needed for alleviation. Monthly Maximum and Minimum Temperatures for the period 1991 January to 2020 September from Department of Meteorology, Sri Lanka, taken as prime data were analysed using time-series plots, trend analysis, scatter-plots and descriptive statistics. Both the annual average Maximum and Minimum Temperatures and annual average temperature of Colombo were found to be increasing over time while increase in Minimum Temperature is faster than the Maximum Temperature. In line with the guidelines of World Meteorological Organization, data pertaining to Period 1 (from 1961 to 1990 – ‘standard reference period’ by WMO) was used to reference and compare with the prime data of Period 2 (1991 January to 2020 September) and it revealed an elevation of Average Annual Temperature at a rate of 0.0167°C/year in Period 2 with respect to Period 1. It was revealed that, at the present rate, projected Average Annual Temperature in Colombo would exceed the desired limit set by the ‘Paris Agreement’ before 2035. Engineering intervention through diverse technological aspects, policy implementations and awareness building is recommended to avert this eventuality.
Small and medium-sized enterprises (SMEs) are a major contributor to manufacturing output and employment in New Zealand. However, many manufacturing SMEs continue to experience productivity constraints arising from weak management capability, limited operational leadership, and low adoption of structured improvement practices. Existing operations and leadership frameworks are primarily designed for large organisations and often lack contextual relevance for SMEs. This paper proposes a practical operations leadership framework specifically tailored to New Zealand manufacturing SMEs employing 20–49 staff. The framework integrates leadership theory, operations management concepts, and SME capability perspectives to identify the leadership behaviours that support improved operational performance. This paper develops a conceptual framework informed by prior literature and a proposed empirical validation pathway. Drawing upon Resource-Based View, Lean Leadership Theory and Socio-Technical Systems Theory, the paper presents a conceptual framework paper and outlines a future empirical validation strategy. The proposed framework offers a context-sensitive model that can support productivity enhancement, leadership capability development, and operational excellence in SME manufacturing environments.
The Sri Lankan public sector has continued to struggle with operational inefficiencies, bureaucracy, low public trust and weak service responsiveness to citizens. This hinders effective governance. These issues necessitate structural transformations rather than haphazard technological upgrades. This study examines how Artificial Intelligence (AI) can enable Governance Process Reengineering (GPR) in Sri Lanka’s government sector. This research utilises a Systematic Literature Review, following the PRISMA 2020 approach, to select literature from 60 peer-reviewed articles across multiple databases, published between 2015 and 2025. The analysis is grounded in the integrated Technology-Organisation-Environment (TOE) framework and Technology Affordance and Constraints (TACT) Theory to provide a multidimensional assessment of AI adoption. The study discovered that challenges to AI adoption include outdated ICT infrastructure, legacy systems, poor data quality, limited specialised talent, organisational resistance, fragmented regulations, socio-ethical concerns, and weak citizen trust. AI has the potential to improve accountability, transparency, and efficiency; enhance service delivery; simplify administrative processes; create public value through redesign; and enhance data-driven decision-making and proactive governance. AI-driven GPR redesigns government processes to enhance public value through accountability, transparency, and public centricity. A proposed 10-year roadmap, tailored to Sri Lanka, adds value by eliminating fragmented and ineffective government structures and translating them into effective governance reforms.
The structural integrity of post-tensioned concrete girders is vital for the safety and durability of civil infrastructure, particularly in bridge construction. Grout failures in post tensioned duct of these girders can significantly compromise their performance, making early detection crucial for effective maintenance and prevention of potential failures. This study presents a novel method for the identification and localization of grout failures in post-tensioned duct-grouted concrete girders, utilizing vibration-based analysis in conjunction with Deep Learning (DL) and Machine Learning (ML) techniques. A Finite Element (FE) modelling approach is employed to generate synthetic data that simulates the dynamic responses of girders subjected to various damage conditions, with an emphasis on grout-related failures and other non-grout related scenarios. The generated dataset serves as the basis for training a deep learning model to accurately detect and localize grout failures based on the vibrational patterns observed. The generated dataset serves as the basis for training a deep learning model to accurately detect and localize grout failures based on vibrational patterns. The model demonstrated exceptional performance, achieving 98.70% accuracy on training, testing, and validation datasets with a mean squared error (MSE) of 0.0001. To evaluate its real-world applicability, the framework was subjected to various levels of stochastic Gaussian noise and ambient fluctuations, under which it maintained a robust prediction accuracy of approximately 88.74%. These results demonstrate the model's stability and its ability to transition from numerical simulations to noisy environmental conditions, ensuring structural integrity prior to girder erection. Additionally, 5-fold cross-validation with a standard deviation of 0.0023 demonstrates that the model performance is stable and not dependent on the training dataset, indicating its robustness and generalizability. These results highlight the potential of deep learning-driven approaches, combined with vibration-based analysis and FE modelling, for improving damage detection in post-tensioned duct-grouted concrete girders before their launch into position, ensuring their structural integrity prior to erection. The suggested framework was validated using experimentally tested beams with different grouting conditions, demonstrating its potential for effective damage detection, and supporting maintenance strategies to ensure the safety and integrity of critical infrastructure.
This paper investigates the Sri Lankan construction industry ecosystem through a structured three-level analytical framework. Level 1 examines the entire ecosystem comprising twelve stakeholder groups and maps their inter-relationships and theoretical underpinnings. Level 2 analyses the Construction Industry Development Authority (CIDA) as a selected institutional player, identifying its mandate and relationships with all other ecosystem actors. Level 3 maps the CIDA-contractor interaction strength across six CIDA activity domains using Social Network Analysis (SNA) in UCINET 6, with primary data collected through semi-structured interviews with registered contractors and CIDA. Key findings reveal a very high overall network density of 0.9500, with Publications and Standards emerging as the strongest domain (mean tie strength 0.964 out of 1), followed by Registration and Grading (0.868) and Industry Promotion and Advocacy (0.851). Dispute Resolution is the weakest domain (mean tie strength 0.150). A consistent and significant perception gap is identified across five of six domains where CIDA underestimates contractor engagement. Eleven evidence-based recommendations are developed for the identified gaps at all three research levels.
This research investigates the effectiveness of multi-agent system (MAS)-based control strategies in enhancing the controllability and stability of islanded microgrids. The study addresses the growing importance of microgrids in facilitating renewable energy integration and improving energy resilience. Through a quantitative research approach, simulation studies are conducted to model islanded microgrid systems and evaluate the performance of MAS-based control strategies in comparison to traditional centralized methods. The research identifies key themes and research gaps through a comprehensive literature review, highlighting the need for decentralized control approaches to address the dynamic operating conditions of islanded microgrids. The findings demonstrate that MAS-based control strategies significantly enhance microgrid controllability and stability, offering advantages in adaptability and resilience over traditional methods. Overall, the study contributes to advancing understanding and informing decision-making in microgrid control, with implications for improving energy resilience and reliability in islanded systems, demanded by Industry 4.0.
Coastal environments are dynamic systems shaped by the interplay of marine processes, terrestrial inputs, and human activities. The present study investigates the spatial and temporal variability of monsoon-driven sediment dynamics along the Western Coastal Region of Sri Lanka, with particular attention to longshore sediment transport, riverine sediment supply, coastal geomorphology, and sediment budget conditions. An integrated methodological framework was employed, including thermoluminescence analysis of feldspar grains to trace sediment transport pathways, estimation of sediment discharge from Western Coastal rivers, assessment of coastline characteristics using indentedness indices, and sediment budget analysis at both meso-cell and regional scales. Seasonal variations resulting from monsoonal wave climate and river flow were also incorporated. The results demonstrate a dominant northward longshore sediment transport regime, as indicated by decreasing thermoluminescence intensities with increasing distance from sediment sources. Riverine inputs, especially from the Kalu River, constitute the primary source of coastal sediment supply. Nevertheless, excessive river sand mining has substantially reduced sediment availability, with extraction rates exceeding natural replenishment rates. Geomorphological analysis identifies a predominantly straight coastline, which promotes efficient sediment redistribution under swell-wave conditions. Sediment budget analysis reveals a shift from historical equilibrium to current deficits across all coastal cells, attributed to reduced fluvial inputs, offshore losses linked to sea-level rise, and disruptions from coastal infrastructure. Seasonal analysis further indicates that peak sediment discharge during monsoonal periods coincides with high-energy wave conditions, thereby limiting effective beach accretion. These findings indicate that the Western Coastal Region sediment system has lost its natural equilibrium and is experiencing ongoing degradation. Current mitigation measures, such as beach nourishment, offer only temporary relief and do not address the fundamental sediment imbalances. The study highlights the need for integrated, source-to-sink coastal management strategies, including regulating sand mining, restoring sediment pathways, and incorporating climate change considerations into coastal planning. This research advances the understanding of sustainable sediment management in monsoon-dominated coastal environments.
This study assesses the offshore wind energy potential in three selected coastal sites around Sri Lanka through an evaluation of wind resources, turbine performance, and site-specific characteristics. Annual mean wind speeds were obtained from the Global Wind Atlas at various heights and extrapolated to turbine hub heights using the power law wind profile. Five reference turbine types ranging from 4 MW to 7 MW were analysed based on their power curves and hub heights. Results indicate that large rotor, low specific-power turbines, typically with rotor diameters of 200 m or more, achieve the highest capacity factors of around 54-60 % across all sites. Bathymetric analysis showed that in Area 2, around 75% of the area has a water depth less than 50 m, underscoring the suitability of monopile foundations for a starting project. Furthermore, the evaluated turbines demonstrated sufficient structural resilience under extreme wind conditions, with 3-second gust wind speeds around 70 m/s, ranking IEC Class 1 turbines suitable for storms around Sri Lanka’s coastline. Overall, the results highlight the strong potential for offshore wind development in Sri Lanka, particularly through the development of medium-to-large rotor turbines and floating foundation technologies in deeper offshore zones.
Governors are significant in maintaining power system frequency stability through the control of prime mover speed of synchronous generators. Accurate analysis and estimation of governor system parameters are necessary to accurately describe the power system dynamics. This paper investigates the parameter estimation of the governor system of the synchronous generator of the Sapugaskanda Power Station, Sri Lanka, and hence analyzes the dynamic response of the system. The modelling approach is consistent with the DEGOV1 governor model, which was retrieved from the Neplan library and is commonly used for representing diesel governor dynamics. The load rejection test was performed in field testing to analyze and estimate the parameters of the governor. MATLAB/Simulink was utilized to design the governor model for simulation purposes, while parameter estimation was carried out using the MATLAB Parameter Estimator tool. The study compared active power fluctuation curves from field measurements and simulation outcomes, identifying deviations of parameters from typical values and adjusting them iteratively until the model could accurately represent real system behaviour. The findings confirm that a model of the DEGOV1-based governor can simulate realistic dynamic system responses and that estimated parameters provide insights into the dynamic behaviour and performance of the synchronous generator and power system.
With rapid societal development, there is an increasing trend of communities establishing settlements near townships to meet socio-economic needs. However, due to the scarcity of available lands for infrastructure and community development, low-lying marshy lands are often encroached upon. Constructing structures on these compressible, weak soil layers leads to excessive settlement, potential bearing failure, and stability issues. To mitigate these challenges, deep foundations with rock socketing are employed, transferring loads to a strong substratum. In deep foundations, upper weak soil layers contribute minimally to load-bearing capacity, while rock socket shaft friction and end-bearing resistance play a significant role. Estimating these capacities is complex, requiring consideration of rock properties, fracture conditions, weathering, and intact rock mass characteristics. Even though, various empirical and semi-empirical methods have been developed, making it challenging for designers to select the most suitable approach. Accordingly, this study assessed different methods for estimating the bearing capacity of rock-socketed piles in fractured gneissic rock to establish an optimized foundation design approach. Instrumented Pile Load Test (IPLT) data, obtained using vibrating wire strain gauges, were compared with empirical and semi-empirical equations to verify the reliability. This study conclusively demonstrates that currently available predictive models fail to provide accurate estimates of rock socket skin resistance for bored piles constructed in Sri Lankan gneissic rock. The proposed linear model, which incorporates a 45% reduction factor for bentonite effects, yielded predictions that closely matched the measured resistance values, indicating its suitability for local design applications.
Traffic signal design in developing countries frequently underperforms due to the direct application of classical models—Webster's delay formula, HCM, ARR123, and ORN13—developed for homogeneous, lane-disciplined traffic conditions. Sri Lankan intersections exhibit fundamentally different characteristics: heterogeneous vehicle streams comprising motorcycles, three-wheelers, non-motorized vehicles, and pedestrians operating without strict lane discipline under geometric constraints. This systematic review synthesizes empirical evidence explaining why conventional signal design methods fail in such contexts. Following PRISMA principles, 138 records were screened, yielding 41 high-quality references spanning foundational theory, classical assumptions, regional adaptations, and Sri Lankan empirical data. The review demonstrates that local intersections systematically violate foundational assumptions regarding lane usage, headway uniformity, start-up behavior, and pedestrian compliance. Observed saturation flows reach 2574 pcu/h/lane—substantially exceeding HCM benchmarks—while behaviors such as motorcycle seepage, lateral filtering, initial surge, and dynamic clustering create discharge patterns that static PCU values cannot capture. A mathematical framework is presented mapping these deviations to quantifiable variables: lateral efficiency, surge discharge dynamics, dynamic vehicle equivalence, headway expansion, and geometric constraints affecting intersection capacity. The paper concludes by proposing context-sensitive design pathways incorporating dynamic PCU factors, behavioral modeling, and simulation-based calibration using SIDRA and SUMO to support locally relevant signal control methodologies.
Prediction of life of a component made of a certain material would be beneficial to both the manufacturer and the user. Life prediction requires knowledge of material properties such as grain size and microstructure, as well as the stress levels induced by the maximum possible loads, particularly cyclic loads under in-vivo conditions. A mathematical model was developed to assess the lifetime of components made from Ti-6Al-4V; a medical-grade titanium alloy extensively used for implant manufacturing. Attention was given to crack initiation and growth in the microstructural short cracks and physically small cracks regions, where a significant portion of the fatigue life was accumulated. Rotary-bending experiments were conducted to assess the fatigue endurance limit of the material. The surface crack was replicated at specific stages of the life to assess its propagation and thereby the crack propagation rate was calculated. The threshold values of crack length and stress intensity factor determined in this study are in good agreement with existing literature, thereby confirming the accuracy of the results. MATLAB and Microsoft Excel were used to develop mathematical models and thereby to assess the material constants. Predicted fatigue lives were validated with the experimental results, with accuracies ranging from 0.7% to 14.5%.
Numerous factors affect the propensity of a metal to fatigue, especially the surface heterogeneities and the surface stress (residual stress) states. S-N curves are generally developed using smooth-polished specimens which give the fatigue limit of the microstructural short crack state. However, the threshold curves (Kitagawa-Takahashi type diagrams) provide a clear insight of the fatigue threshold conditions. These threshold curves correlate the S-N properties and fracture mechanics-based damage tolerant approach to understand the safe fatigue envelop of the materials. Specifically, according to the stress amplitude and surface damage (roughness), the fatigue threshold limits can be investigated. Rotary-bending tests were conducted using samples with four known surface roughness values (0.2 & micro;m, 6 & micro;m, 21 & micro;m and 52 & micro;m) with two subsets, annealed and without-annealed conditions. Residual stress states were examined, and they vary between-313 MPa and +175 MPa. Threshold curves were developed for four different stress ratios, and a clear variation of the threshold stress intensity factor (Delta Kth) was identified. The results can be used to understand the vulnerability of fatigue threshold limits and propagation rates based on the surface roughness and residual stress states, thereby to predict and minimise the in-vivo failures of components made of Ti-6Al-4V implant grade material.
