Processed sea sand has emerged as a viable alternative to traditional fine aggregates in the Sri Lankan construction industry. Despite its economic and environmental advantages, concerns over residual seashell content have limited its widespread adoption by local contractors. Residual seashell content, typically ranging from 1% to 3% after processing, has raised concerns about its impact on the performance of concrete. This study systematically investigates the influence of seashell fragments, with a content of up to 5%, on the fresh, mechanical, and durability properties of sea sand concrete and mortar. Experimental results indicate that workability remains stable, with minor variations across the tested range of shell content. Compressive strength remains relatively consistent from 0% to 5% seashells, indicating that seashell content does not significantly impact the strength within this range. Durability tests reveal minimal effects of shell content on concrete performance within the tested shell range, as indicated by results for water absorption, rapid chloride penetration, and acid exposure testing. Accelerated corrosion indicates that the typical shell content does not increase corrosion risk; however, high shell content (>3%) can compromise corrosion durability. Overall, these findings demonstrate that the mechanical and durability performance of sea sand concrete remains uncompromised at typical seashell content levels (1–3%), supporting the use of processed sea sand as a sustainable and viable alternative to traditional fine aggregates in Sri Lankan construction.

River sand is the commonly used fine aggregate for concrete production in Sri Lanka. However, the over-extraction of this resource has caused numerous environmental issues. Globally, sea sand is the most frequently used alternative for river sand. Based on its geography, Sri Lanka has great potential for using sea sand to fulfill the fine aggregate demand in the future. Despite it being used for several large-scale projects in Sri Lanka, sea sand remains underutilized in the local construction sector due to concerns about chloride contamination and impurities such as seashell fragments. This study aims to assess the current utilization of sea sand in Sri Lanka and evaluate the performance of sea sand concrete based on the current literature. A field study was conducted at the Muthurajawela main sand yard, other sea sand distribution sites, and testing laboratories, to understand the current practice. Currently, commercial-scale sea sand mining is carried out by the Sri Lanka Land Reclamation and Development Corporation (SLLRDC). Extracted sea sand is treated to remove the chloride and seashells at the Muthurajawela main sea sand yard. Following the Sri Lankan Standards, various quality control and quality assurance tests, such as water-soluble chloride content, shell content, and particle size distribution, are carried out before releasing sand to the market. This quality testing process ensures that sea sand is suitable for any construction application comparable to the use of river sand and exhibits comparable mechanical and durability performance to river sand concrete, as supported by various studies. According to the SLLRDC data, commercial utilization of sea sand peaked during the post-COVID-19 pandemic period and dropped due to the country's economic downturn. Moreover, using sea sand as an alternative to river sand offers several advantages, including availability on a large scale, which helps mitigate the issues of uncontrolled sand mining and diminishing river sand reserves. Altogether, sea sand presents a technically and economically viable solution for fine aggregate use in the Sri Lankan construction industry.

In tandem with global urbanization and infrastructure development, the escalating demand for concrete has prompted a search for eco-friendly alternatives against the extraction of river sand for fine aggregate. This study investigates the use of blended sea sand and manufactured sand (M-sand) as an alternative to river sand in concrete, with a focus on mechanical properties and durability. In Stage 1, three control concrete mixes—comprising river sand (RS), sea sand (SS), and M-sand (MS)-were tested alongside blends in which M-sand replaced sea sand at 25%, 50%, and 75% proportions. Evaluations included particle size distribution, compressive strength, workability, and durability via accelerated corrosion and chemical exposure tests. Results identified the 50% sea sand–50% M-sand blend as the optimal mix. In Stage 2, this optimal blend was further assessed against RS and SS concretes for mechanical properties (elastic modulus, splitting tensile strength) and durability characteristics (water absorption, density, void content, and chloride penetration resistance). Stage 3 focused on determining a permissible chloride content for the blend using accelerated corrosion testing. A chloride threshold of 0.05% was established, at or below which the durability performance remained comparable to the control mixes. These findings highlight the potential of SS–MS blended concrete as a sustainable alternative to traditional fine aggregates in construction.

Studying future precipitation behaviour in river basins is essential for proper water resources and land-use planning within them, as this will help to reduce the risk and mitigate disasters that can occur in the future. General Circulation Models (GCMs) are used to study future precipitation fluctuations, which simulate large-scale climate variations under the effect of greenhouse gas changes. The GCM runs at a coarse spatial resolution which cannot be directly used for climate impact studies. Therefore, downscaling is required to extract the sub-grid and local scale information. This study examines the use of the Long Short-Term Memory (LSTM) neural network for climate downscaling to the Mi-Oya river basin in Sri Lanka using CNRM-CM5 and HadCM3 GCMs and observed annual data for 35 years. The precipitation data were extracted to cover Sri Lanka. Current downscaling models mostly use Convolutional Neural Networks (CNNs) to downscale GCMs. Out of 42 GCMs, two appropriate GCMs were chosen using the data analysis tool Data Integration and Analysis System (DIAS). The best predictor variables were chosen using the LASSO regression method. In this research, Machine Learning models were implemented using the Google TensorFlow platform. The Nash–Sutcliffe coefficient, Pearson correlation coefficient, and root-mean-square error performance indices were used to evaluate the performances of different downscaling models. Statistical downscaling was performed on the data at RCP 2.6, 4.5, and 8.5 using a LSTM. Subsequently, the changes that would take place by the year 2100 were analysed. The results show that precipitation will be reduced in the 2nd and 3rd decades of the 21st century, and precipitation will increase toward the 22nd century.