Department of Civil Engineering

The utilization of Linz–Donawitz (LD) slag in cementitious applications has gained traction due to its widespread availability, offering a potential solution to reduce global warming. This study evaluates the impact of particle size fractions on the chemical, mineralogical, and dissolution characteristics of LD slag. Nine particle size fractions were analyzed, revealing significant variations in oxide content based on particle size. While CaO, Fe2 O3, and SiO2 contents remain similar in higher (+500??xm and +1,000??xm) and lower (+3??xm) size fractions, particles between +3??xm to +75??xm exhibit a 1.5% free lime content. Quantification using XRD-based Rietveld refinement indicates LD slag primarily consists of crystalline phases (quartz, calcite, portlandite, brownmillerite, wustite, and belite) alongside an amorphous phase, with amorphous content ranging from 40% to 60% across all sizes. The +3??xm size fraction exhibits the highest belite, brownmillerite, and wustite content, with comparatively lower free lime content than other size fractions. Dissolution analysis in an alkaline environment shows a slightly improved dissolution behavior with decreasing particle size from +150??xm to +3??xm. Calcium exhibits higher initial dissolution rates than iron and silicon within the first three hours, with silicon becoming more prominent after twelve hours. Overall, this study offers a comprehensive analysis of the correlation between particle size and chemical/mineralogical composition, highlighting the potential for converting industrial waste into ecofriendly products.

The current paper explores the possibility of producing alkali-activated fly ash-based lightweight blocks with adequate properties at lower curing temperatures to use in various construction applications. Sodium-based alkaline activators are utilized to activate the fly ash. Two different curing temperatures are employed, 60 °C and 85 °C. The properties of the block, such as strength, density, and thermal conductivity, are evaluated. Aeration experiments reveal that the addition of Al powder to the paste leads to the collapse of the bubble structure. However, the addition of nano clay aids in stabilizing the formed bubbles, thereby preventing their collapse and maintaining the integrity of the aerated structure over time. The amount of nano clay depends on the quantity of Al powder added to the paste. Across all samples, strength is found to be inversely proportional to density and thermal conductivity. At 0.3% and 0.4% Al powder dosages, samples attained similar ultimate strength regardless of curing temperature, while density and thermal conductivity varied. The block achieved a strength of approximately 10.60 MPa and a thermal conductivity of 0.090 W/mK at a corresponding density of 450 kg/m³, which is higher than the IS standard codal provisions. The blocks were characterized using various analytical techniques such as XRD, FTIR, and SEM. The strength of the block depends on the reaction product formed during the activation process, with all techniques indicating the formation of stable sodium aluminosilicate gel. For samples cured at 60 °C, two new crystalline phases, trona and sillimanite, were observed. Trona is formed at the age of 28 days, while sillimanite forms at an early age, with its quantity decreasing over time. In conclusion, the study offers crucial insights into producing alkaline-activated fly ash-based lightweight blocks with satisfactory performance at lower curing temperatures

This research addresses the growing scarcity of natural aggregates across India and explores the use of recycled asphalt pavement (RAP) aggregates as a sustainable alternative in road construction. The study investigates the impact of incorporating RAP on the compressive and tensile strength of concrete, with results indicating that the excessive use of RAP (over 50% of natural aggregate) adversely affects these properties. However, there is a notable gap in research on predicting the compressive strength of concrete mixes containing RAP aggregates. To bridge this gap, machine learning algorithms, specifically extreme gradient boosting (XGBoost) and random forest regressor (RFR), are employed to predict the concrete strength at various curing ages. The models are evaluated using three statistical performance indices: the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results demonstrate that the XGBoost model provides superior prediction accuracy (R2=0.937) compared to the random forest model. Additionally, sensitivity analysis reveals that washed recycled fine aggregates (WRFA) significantly contribute to the strength of cement concrete mixes. These findings suggest that WRFA aggregates can be considered a promising, sustainable alternative for concrete pavements, potentially reducing reliance on natural aggregates and offering cost-effective, environmentally friendly solutions for the construction industry

The scour phenomenon is highly complex, and its precise prediction remains a considerable challenge for hydraulic researchers. Traditionally, most of the studies relay on empirical approaches for scour prediction. However, with the rapid development of infrastructure and the increasing number of bridges, these empirical models are inadequate in providing precise scour prediction across diverse field conditions. Consequently, there is a rising requirement to adopt advanced Machine Learning (ML) based methodologies to achieve more precise and computationally efficient scour prediction. The primary objective of this study is to propose an alternate model to traditional scour prediction methods by employing ML-based tools, specifically Artificial Neural Networks (ANN), Support Vector Machines (SVM), and the Ensemble Tree Method. These ML-based models are capable of handling complex and nonlinear problems. In addition to simulation work, a detailed experimental investigation has been carried out for a single cylindrical pier. The results of the present study, along with existing literature data, have been utilized for training and testing of ANN, SVM, and Ensemble Tree models. To evaluate the performance of the proposed models, an in-depth statistical analysis has been carried out. The findings of this study highlight that all three models significantly outperform traditional methods, demonstrating their effectiveness in improving scour depth predictions

Plastic-aggregates are made up from unused or waste plastic and natural aggregates which have recently been emerged as a significant addition to the existing emerging contaminants list mainly in the coastal environment. The transformation from plastics/microplastics to Plastic-aggregates signifies a crucial shift in our understanding and use of plastics and prompting us to reconsider their fundamental characteristics along with possible environmental threats. When plastic waste is incinerated for the purpose of disposal, it combines with organic and inorganic substances present in the surrounding environment, leading to a new type of material. Besides, some natural factors (physical, chemical, biological or in combination) also act upon discarded plastics to combine with rocks and other earthen materials to form plastic-aggregates. Our research aims to build fundamental knowledge and critically review the possible formation process, classification, and possible degradation of all such polymer-rock compounds along with their impact on the ecosystem. The knowledge gap related to the degradation and release of secondary pollutants from these agglomerates is to be addressed urgently in future research. Development and standardization of proper sampling and reporting procedures for plastic-aggregates can enhance our understanding related to their impacts on human health as well as to the entire environment as these aggregates contain different toxic chemicals

Wastewater treatment plants (WWTPs) are major contributors to the release of volatile organic chemicals (VOCs), many of which pose significant risks to human health through both non-carcinogenic and carcinogenic pathways. These chemicals, along with plastic-derived compounds, pesticides, and pharmaceuticals and personal care products (PPCPs), have emerged as critical environmental pollutants. Their widespread release through urban wastewater systems, combined with their hydrophilic nature and limited removal efficiency in conventional WWTPs, allows these pollutants to persist throughout the water cycle, often contaminating drinking water supplies. Despite increasing global awareness of the environmental and health risks associated with these contaminants, data on their occurrence, transport, and fate in Mexico's wastewater systems are still limited. To address this knowledge gap, the present study analyzed 54 VOCs in wastewater samples collected from 17 WWTPs across different provinces of Mexico. Among these, 38 VOCs were detected at significant levels, with the highest concentrations recorded for Toluene (21.39 µg/L), 1,1,2,2-Tetrachloroethane (28.02 µg/L), followed by p-Isopropyltoluene (27.24 µg/L), and Trichloromethane (17.56 µg/L). Additionally, pesticides and related chemicals such as 2-Chlorotoluene, Naphthalene, 1,2-Dichlorobenzene, and n-Butylbenzene were prevalent, underscoring the extensive use of these compounds in agricultural practices. These chemicals not only bioaccumulate in soil but can also leach into groundwater systems, exacerbating contamination risks and increasing their persistence in the environment. Furthermore, many of the detected compounds, such as Toluene, its derivatives, and Trichloromethane, are known endocrine disruptors (EDCs) capable of causing hormonal imbalances, drug resistance, and reduced primary productivity in ecosystems. Their bioaccumulation in organisms and persistence in water further exacerbate their environmental impact, making them critical candidates for regulatory scrutiny. Therefore, this study underscores the urgent need for enhanced regulatory monitoring and management strategies targeting VOCs and EDCs in Mexico’s wastewater systems. By providing valuable insights into the prevalence and distribution of these hazardous pollutants, the findings highlight the importance of incorporating pesticides and PPCPs into comprehensive monitoring frameworks. Such efforts are essential for mitigating the environmental and health impacts of these contaminants and ensuring the sustainable management of water resources. The results also offer a foundation for developing targeted interventions aimed at reducing pollutant loads in wastewater and preventing their long-term accumulation in aquatic ecosystems

Protecting wetlands from various human activities requires a deep understanding of their aquatic limnology. This calls for continuous monitoring, which generates extensive and complex datasets. By applying statistical analyses and modelling techniques, these datasets can be effectively interpreted to uncover, define, and gain critical insights into the functions and processes that drive aquatic ecosystems. The present study aims to integrate water quality, sedimentology, aquatic toxicology and modelling techniques to present a detailed and comprehensive assessment of different components of Deepor Beel's (a Ramsar site) ecosystem. Deepor Beel is situated on the banks of the Brahmaputra River in the northeastern region of India and holds immense significance to the city of Guwahati. Originally spanning across more than 40 sq. km area, rampant encroachment and anthropogenic disturbances have not only degraded the wetland ecosystem but also reduced its effective area to now a meagre four sq. km. Large-scale eutrophication due to the discharge of untreated municipal wastewater has played a significant role in the wetland's deterioration. Although several restoration measures were undertaken in the past, they could have been more effective as they lacked prognosis. Hence, we carried out systematic monitoring (the first such extensive monitoring was undertaken) of four components of Deepor Beel's ecosystem, i.e., water, sediment, fish, and aquatic weeds, to understand the governing factors responsible for the wetland's deterioration. We employed different multivariate statistical techniques to understand the sampling site's characterization and behaviour under various environmental and climatic stresses and identify and quantify latent pollution sources contributing to wetland pollution. In addition, a novel water quality index was developed employing Shannon Entropy, which encompasses all essential variables for a comprehensive understanding of the wetland's water quality. We assessed sediment contamination from heavy metals—including chromium (Cr), cadmium (Cd), iron (Fe), manganese (Mn), copper (Cu), lead (Pb), and mercury (Hg)—and conducted fractionation studies, revealing important insights into how these metals interact within the ecosystem. Fish samples from three indigenous species that are locally consumed were collected, and we analyzed the bioaccumulation of heavy metals in various tissues and organs. Our findings indicated significant amounts of heavy metals in the fish organs, making their consumption potentially carcinogenic for humans. Finally, a eutrophication-based ecological model was developed to understand the nutrient dynamics within the wetland. The model was calibrated, and sensitivity analyses were performed and validated using the dataset generated through the laboratory analyses. The model was then simulated for two scenarios: 1) harvesting of aquatic weeds reflecting the current practices, and 2) establishing a treatment unit handling the nitrogen and phosphorus loadings. The results demonstrated that treating the inflow is a more sustainable approach to reducing eutrophication, and this strategy should be implemented promptly. Given the gravity of the situation for Deepor Beel, the findings of this study are significant and call for immediate attention and action

This study presents an analytical model to estimate the vertical distribution of streamwise velocity in double-layered flexible vegetation. The model divides the flow into distinct zones based on force balance conditions. Validation was conducted using experimental data from previous studies, demonstrating strong agreement between model predictions and observed velocity profiles. The influence of vegetation bending angles on velocity distribution was examined, revealing minimal impact in the lower vegetation zone for a fixed vegetation density. The model also incorporates wave effects using Keulegan–Carpenter (KC) numbers and evaluates both linear and nonlinear Stokes wave theories. The findings highlight the role of vegetation flexibility in modifying flow resistance, contributing to improved predictions of flood mitigation, erosion control, and wetland hydrodynamics

This study explores the potential of polyculture microalgae for removing emerging contaminants – paracetamol (PCT) and caffeine (CAF) – along with organics and nutrients from greywater, while assessing their impact on microalgal growth and overall treatment efficiency. Several reactors containing varying concentrations of PCT and CAF (3 – 10 mg L?1) were monitored for total organic carbon (TOC), nitrate, phosphate removal, and microalgal growth. Polyculture microalgae achieved maximum PCT and CAF removal efficiencies of 94.3% and 43.3%, respectively, with the highest increase in microalgal dry weight (0.41 g L?1) in the run with high PCT and low CAF concentration. The polyculture microalgae achieved higher PCT removal efficiency (94.3%) compared to previous studies using monocultures, demonstrating the potential of polycultures for greywater treatment. The highest TOC (73.6%), nitrate (65.6%), and phosphate (41.4%) removal were observed in the run with the absence of contaminants. The addition of PCT and CAF led to a decrease in the removal efficiencies of TOC by 0.6 – 21.9%, nitrate by 8.7 – 63.0%, and phosphate by 18.5 – 33.1%, suggesting that these contaminants impacted the regular nutrient consumption of the microalgae. However, organic consumption increased as PCT and CAF were also used as carbon sources. Despite reduced nutrient consumption, microalgal growth increased by 24.2%.

Incineration of municipal solid waste (MSW) along with energy recovery has been proven to reduce the volume of waste destined for landfills by as much as 90%. According to Indian solid waste management regulations, all municipal solid waste must be treated in composting facilities, waste-to-energy facilities, or other processing plants before being disposed of in landfills. This requirement not only lessens the spatial footprint of landfills but also contributes positively toward environmental sustainability. However, one challenge that remains is how to effectively reuse or dispose of the residues left behind after the incineration process. Even with comprehensive resource recovery, current estimates indicate that 25–35% of total MSW generated remains as residue that accumulates in landfills if not further used. Therefore, this research focuses on exploring the potential application of incinerated MSW ash as landfill liners. The study undertakes a meticulous analysis of both bottom ash and fly ash through extensive geotechnical characterizations. In order to gauge their suitability as landfill liners, the study conducts detailed geotechnical analysis on various aspects such as hydraulic conductivity and compressibility. Ultimately, this research promotes the massive utility of incinerated MSW ash, thereby encouraging a sustainable approach toward landfill management.

The utilization of river sand in the construction industry increases the demand and forms a several environmental impacts by degradation of river beds. It is very crucial to find the alternative materials for the sustainable development. Granite dust, M-sand, and palm oil fuel ash (POFA) are three of the most abundantly produced industrial by-products, yet their potential use as precursors and alternative fine aggregates has not been fully explored. This study comprehensively investigates the effects of incorporating varying proportions (0–50%) of granite dust into M-sand within both heat and ambient-cured alkali-activated binders (AABs) based on sodium (NaOH+Na?SiO?) and potassium (KOH+K?SiO?). POFA is utilized as a source material, replacing slag at levels of 10%, 20%, and 30% for the optimized granite dust composition. Extensive laboratory tests were performed to evaluate the physio-mechanical and durability performance of the resulting AABs, including compressive strength, water absorption, sorptivity, acid resistance, and microstructural characterization of the POFA-based alkali-activated samples using advanced analytical techniques. The results indicate that, irrespective of curing temperature, M-sand-based AABs exhibited enhanced setting behavior, compressive strength, water absorption, and porosity properties with up to 30% granite dust substitution. This improvement is attributed to the development of C-A-S-H, N-A-S-H, and K-A-S-H gel phases. In POFA-based AABs, the dense packing of POFA resulted in reduced water absorption and sorptivity compared to control specimens. Overall, the findings suggest that POFA, as a pozzolanic material, significantly improved the properties of granite dust-based AABs, offering a sustainable solution for mitigating the environmental impact and disposal challenges of industrial waste

The environmental impact of Portland cement production has intensified the search for alternative low-carbon cement. Reactive magnesium oxide cement has emerged as a promising option. The current study investigates the hydration behavior, strength development, and phase evolution of MgO and MgO-RHA blends cured under sealed and carbonation conditions. Two RHA sources with differing amorphous content and particle size were used. A detailed investigation was conducted using various techniques, including calorimetry, TGA, FTIR, XRD, Raman spectroscopy, and SEM. Results showed that higher glassy content and finer particles in RHA enhanced cumulative heat release, hydration product formation, and compressive strength. Carbonation curing further improved strength consistently by promoting the formation of nesquehonite and magnesium silicate hydrate. Quantitative XRD revealed that M-S-H formation was influenced by the consumption of periclase and unreacted glassy phase. Raman and FTIR analyses confirmed significant chemical and structural transformations, including the formation of brucite, nesquehonite, and carbonate phases. The D and G-band features in MgO-RHA samples suggested variations in carbonated products, influenced by processing conditions. Finally, SEM analysis revealed various carbonated products, M-S-H, and a dense microstructure. Overall, the study emphasizes the critical role of RHA properties and curing strategies in optimizing the performance of MgO-RHA systems for sustainable binder applications.

Incineration of municipal solid waste is increasingly being adopted in developing countries from the past couple of decades as a waste management strategy. This is driven by rapid urbanization, population growth and scarcity of land for landfills. MSW incineration reduces the volume of waste by 70–90% but still leaves behind substantial quantities of residual ash for disposal. Managing and disposing ash safely adds significant costs over just burning the waste, undermining the economics of incineration. The present study explores the potential applications of municipal solid waste incinerated (MSWI) ash as a landfill liner. This study provides a comprehensive characterization of both fresh and aged incinerated MSW ash (fly ash and bottom ash) collected from waste to energy plant (WTE). Analyzing the physicochemical properties of incinerated ash incorporating its mineralogy, morphology, and chemical composition is essential for its effective application in geotechnical engineering. This approach offers a sustainable alternative to traditional liner materials

Owing to the urgent and escalating environmental crisis of water pollution through anthropogenic wastewater generated from various sources, the development of novel and innovative bioremediation strategies that are equally sustainable is highly necessitated. The present study embarks on an integrated omics-based exploration, complemented by a thorough literature synthesis, to critically evaluate and enhance hybrid algal-bacterial systems for effective wastewater treatment. Drawing on case studies and research from diverse geographic regions, we explore how these technologies inform the design and optimization of both engineered and natural treatment systems. The review emphasizes the integration of multi-omics data to support sustainable, targeted bioremediation strategies and underscores the cross-disciplinary convergence of environmental engineering, molecular biology, and systems ecology. This global and holistic perspective positions omics as a cornerstone for advancing the next generation of wastewater treatment solutions. Comprehensive analyses of the efficacies of different treatment methods used to remediate organic pollutants, heavy metals, nutrients, and contaminants of emerging concern (CECs), including antibiotic resistance genes (ARGs), were carried out, thus underscoring the pivotal role of microbial diversity and metabolic activity in the complex process of contaminant elimination. While prior research has predominantly focused on isolated components, the current study presents a holistic approach, merging state-of-the-art high-throughput metagenomics and transcriptomics techniques. This innovative combination illuminates the functional dynamics of microbial communities operating within the hybrid system under a range of operational conditions. The primary critical findings reveal significant shifts in microbial community structure and gene expression patterns, which are intricately linked to enhanced efficiencies in nutrient uptake and contaminant removal. In addition, the study also situates these findings within the expansive framework of omics-based bioremediation research, providing a clear and structured pathway for identifying prevailing knowledge gaps and directing future optimization efforts. Collectively, these contributions not only deepen our understanding of microbial community functions but also pave the way for designing next-generation bio-based wastewater treatment systems driven by the intricate interplay of microbial dynamics.

Understanding the global water flux is vital to comprehend different hydrological components of the planet. The precise quantification of the global water budget is important to understand the global water cycle. While the first attempts in closing the global water budget date back to the early 1900s, we still have not been able to comprehensively understand the global water cycle. The past few decades have witnessed significant interests among the researchers worldwide in understanding the global water budget considering various ecological, hydrological, and climatic parameters and using data from various sources, such as ground observations or satellite information. However, a certain degree of uncertainty still prevails in the global or regional models developed till date. Here, we discuss some recent advances in this context, taking into account continent-wise analyses of the various significant attempts made by different researchers in varying geographical conditions, and using different tools. These findings will eventually lead to deeper understanding and advancements in the existing approaches aiding in the closure of the global water budget.

Deep marine sediments are rich in microbial diversity, which holds metabolic repertoire to modulate biogeochemical cycles on a global scale. We undertook the environmental microbiome inhabiting the Gulf of Kathiawar Peninsula as a model system to understand the potential involvement of the deep marine sediment microbial community and as a cohort in the carbon, nitrogen, and sulfur biogeochemical cycles. These gulfs are characterized by dynamic tidal variations, diverse sediment textures, and nutrient-rich waters, driven by coastal processes and the interaction between natural coastal dynamics and anthropogenic inputs that shape its microbial community diversity. Our findings suggest that carbon fixation was carried out by Gamma-proteobacteria with CBB cycle-related genes or by microbial participants with Wood-Ljungdahl pathway-related genes. Microbial communities involved in nitrogen metabolism were observed to be rich and diverse, and most microbial communities potentially contribute to the nitrogen cycle via processing nitrogen oxides. Bacteria belonging to the KSB1 phylum were also found to fix nitrogen. The sulfur cycle was spread throughout, with Verrucomicrobiota phylum being a major contributor. The varying napAB genes, significantly lower in the Gulf of Kutch compared to the Gulf of Cambay and the Arabian Sea, mediated nitrate reduction. Dynamics between these pathways were mutually exclusive, and organic carbon oxidation was widespread across the microbial community. Finally, the proteobacteria phylum was highly versatile and conceivably contributed to biogeochemical flux with exceptionally high abundance and the ability to form metabolic networks to survive. The work highlights the importance of critical zones and microbial diversity therein, which needs further exploration

Pesticide poisoning through contaminated water, soil, or food is often linked to the widespread use of chemical pesticides in Indian agriculture. While many studies have reported the association between pesticide exposure and human health impacts, it has been challenging to disseminate this information to a broader population at state and national levels. Consequently, no state-level database exists correlating pesticide use with cancer rates in India. Here, we provide a comprehensive outlook focusing on the challenges of correlating these factors to develop a comprehensive geospatial database at the national level. A data-mining approach can help identify cancer hotspots, supporting informed policymaking

The increasing importance of biomass-based energy production as a critical component of sustainable energy resources and effective waste management necessitates a comprehensive understanding of the fundamental properties of biomass feedstocks. This review critically evaluates the physicochemical and fuel characteristics of seven widely available biomass sources in India (sugarcane bagasse, sugarcane tops, rice husk, rice straw, maize stalks, maize cobs, and empty palm oil fruit bunches), with a particular focus on the impact of torrefaction. Despite the well-documented benefits of torrefaction in improving biomass properties, limited studies have compared the specific effects of this thermal pretreatment process across diverse biomass sources. This review addresses this gap by critically analyzing the impact of torrefaction on key biomass properties, including hemicellulose, cellulose, lignin, elemental composition (carbon, hydrogen, nitrogen, and sulphur), moisture content, volatile matter, and high heating value, providing a comparative analysis to determine the optimal biomass for energy applications. Moreover, the review critically analyzes the impact of torrefaction on key biomass properties, including hemicellulose, cellulose, lignin, elemental composition (carbon, hydrogen, nitrogen, and sulphur), moisture content, volatile matter, and high heating value. Furthermore, the review synthesizes recent findings to identify optimum torrefaction conditions that enhances the properties of each corresponding biomass. By providing a comprehensive analysis of the complex relationships between biomass characteristics and their practical applications, this review contributes to the advancement of sustainable energy production by optimising biomass-based energy systems and promoting waste-to-energy strategies

The study investigates the potential of unmanned aerial vehicles (UAVs) for acquiring phenotypic trait parameters of Victoria amazonica in an open pond environment. Sequential 2D images using UAVs were acquired from multiple views on a weekly basis. The structure from motion (SfM) technique was then used to build high-resolution orthomosaic and 3D models of the mapping area. Measurements corresponding to typical leaf, petiole, and flower growth were made using digital models. It was observed that the digital models could represent the actual ground truth values for all the traits with a ground sample distance ranging between 0.25 and 0.4 cm/pixel. A comparison of digital and manually measured phenotypic trait data revealed that the UAV-based measurements could predict on par with the conventional manual measurements. Additionally, linear regression fits generated for digital and manual trait data resulted in adjusted coefficients of determination (Adj. R2) of atleast 0.98 for all parameters. The trait data were also statistically analyzed to assess the growth rates of various parameters during the monitoring period. It is observed that the leaf rim height and petiole parameters are the highly sensitive and varying traits (COV range: 53–78%) for Victoria amazonica species. Besides addressing the problems with manual phenotyping, the proposed methodology provides an easy, flexible, frequent, accurate, contactless, non-destructive and cost-effective solution for aquatic plant research.

The rapid growth of urbanization and the construction industry has led to increased consumption of natural resources, resulting in significant environmental impacts. This study explores the use of three locally available waste materials to develop sodium- and potassium-based alkali-activated binders. Granite dust was employed as an alternative to river sand, with replacement levels ranging from 0 to 50%, and optimized for performance. Additionally, palm oil fuel ash (POFA) was utilized as a source material, replacing slag at levels of 10% to 30% in a control mix, activated using NaOH & Na?SiO? and KOH & K?SiO? under both heat curing at 65 °C and ambient curing conditions. The mechanical and durability properties like compressive strength, water absorption, sorptivity and resistance to acids with influence of the activator, and microstructural characteristics of the binders were thoroughly analyzed. The temperatures effects of specimens were clearly analyzed and the heat cured specimens gives the 25% of lesser strength than the ambient cured AAB irrespective of activator. In both sodium and potassium based alkali activated binders. K-Nearest Neighbors and artificial neural networks were used to forecast the alkali-activated mortar’s compressive strength. Metrics used for performance evaluation, such as the coefficient of determination R2 and RMSE, showed that the ANN model produced better predictions. For sodium-based activators, ANN produced an RMSE of 0.174 and an R2 value of 0.96 under ambient curing conditions, while KNN produced an RMSE of 0.154 and an R2 value of 0.158. The findings highlight the potential use of waste materials, such as POFA, granite dust and slag in the creation of eco-friendly and high-performance alkali-activated binders