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Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance Applications
The activity of the object in question is alerted directly upon completion of an effective object tracking. Dependent on hardware support or not, a strong object tracking protocol is required for a precise object tracking application. According to these methods, tracking an object accurately within a predetermined processing time window required a significant amount of computer complexity. In contrast, a variety of quality-degrading elements, including occlusion, shifting lighting, shadows, and so on, have an adverse effect on tracking. All of these tracking shortcomings will be fixed by a revolutionary residual network based on loss operator and anchor creation. Detection of object has concerns that rely on the process of feature extraction to afford efficient quality. For this purpose a model called ResNet has been used that comprises thirty layers and hence named as Resnet-thirty. These networks are a type of Convolutional Neural Network (CNN) that contain residual connections among various layers. The various merits of these connections is the network has the capability to learn the features of global, local and intermediate in parallel. As such, the system is robust against changes in lighting. These variations in light were understood in terms of tracking objects within a changing background. The proposed work uses MOT datasets. This dataset comprises of MOT 15, MOT16, MOT17 and MOT20. The results have been found by using these datasets. Hence, it evidently outperforms in terms of precision, recall, MOTA, IDF, MOTP, SAIDF and F1 measure to track the objects. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
3TFL-XLnet-CP: A Novel Transformer-Based Crop Yield Prediction Framework with Weighted Loss Based 3-Tier Feature Learning Model
The advancement of crop yield prediction through artificial intelligence (AI) has gained significant attention. However, the existing AI-based approaches for maximizing agricultural productivity, specifically in crop yield prediction, have not consistently delivered satisfactory results. In response to this challenge, we propose a novel framework named as Three Tier Feature Learning with XLnet based Crop Prediction (3TFL-XLnet-CP) that enhances agricultural productivity by accurately predicting crop yield. The 3TFL-XLnet-CP framework employs a three-tier feature learning approach in combination with the powerful XLnet transformer-based crop prediction model. The three-tier feature learning involves the integration of Spiking Neural Network (SNN), Graphical Neural Network (GNN), and Convolutional Neural Network (CNN) to extract distinct feature vectors from the preprocessed data. These feature vectors are then concatenated using Jaccard Similarity to measure their similarity score. Additionally, a weighted Loss function is introduced to optimize feature learning, further enhanced by a novel self-adaptive Spider Monkey Optimization algorithm (SASMO). The concatenated features are subsequently fed into the classification layer for making precise crop yield predictions. The proposed model is implemented using the Python platform and evaluated against existing models such as ANN, RNN, DNN, and BiLSTM. The comparison demonstrates the superiority of our proposed 3TFL-XLnet-CP framework in accurately predicting crop yield. The Author(s) 2025. -
Dysgraphia Disorder Detection and Classification Using Deep Learning Technique
Dysgraphia, a neurological condition, impedes childrens acquisition of standard writing abilities, leading to subpar written expression. Inadequate or underdeveloped writing proficiency can adversely affect a childs educational progress and self-esteem. To address this issue, our study involved compiling a novel dataset of handwritten operations and extracting an array of features to encapsulate the various dimensions of handwriting characteristics. This research presents the Rotational Region Convolutional Neural Network (R2CNN) as a novel approach to tackle this issue. The R2CNN framework integrates a multitask refinement network for accurate tilted box detection and a text region proposal network (Text RPN) to identify potential text areas. To address the imbalance in the training categories and mitigate the overpopulation problem through feature elimination, a balance parameter is incorporated into the loss function. This research focused on identifying dysgraphia by analyzing these extracted features, which included both handwriting and geometric elements. The feature-learning stage of deep transfer learning effectively extracts and applies characteristics to identify dysgraphia. Research findings indicate that this study can use handwritten images to detect dysgraphia in children. The results of the data-gathering process show that this investigation can leverage samples of handwritten text to recognize dysgraphia among young individuals. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
Machine Learning and Deep Learning Approaches for Guava Disease Detection
A larger proportion of crops face disease outbreaks, making agricultural output difficult. Detecting and predicting diseases at an early stage can enhance productivity. Guava, a tropical and subtropical fruit, is cultivated in various countries. In regions such as Bangladesh, Pakistan, India, and South America, guava cultivation faces significant challenges due to diseases like Canker, Dot, Mummification, Phytophthora, Scab, and Styler and Root. Traditional diagnosis methods based on visual observation are often unreliable and time-consuming. To address this, we developed an automated system leveraging deep learning techniques. Our study utilized a dataset comprising 4046 guava leaf images categorized into these seven disease classes. We compared the performance of traditional methods with deep learning approaches using vision transformers and transfer learning. The results demonstrate the superiority of deep learning methods over traditional approaches, where traditional machine learning model SVM gave accuracy near 78% and deep learning methods gave over 90%. The transfer learning method gave an accuracy of nearly 97% and on the other hand, the vision transformer gave accuracy of 98%. This offers a promising solution for early disease detection in guava crops. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
Efficient Scene Text Recognition in Noisy Environments Using Fusion-Based Adaptation and Triple-Level Confidence Modeling
Scene Text Recognition (STR) involves deciphering textual content embedded within complex, natural scene images, often following detection stages or integrated into end-to-end pipelines. Addressing the challenge of STR in noisy target domains, characterized by inter-domain and intra-domain noise, cluttered backgrounds, and irregular text shapes, this study proposes a robust and understandable framework titled Fusion-Based Adaptation for Scene Text Recognition (FASTR). The framework integrates a primary classifier with an epistemically aware auxiliary classifier to model uncertainty, supported by a novel Adaptive Scale Feature Module (ASFM) that enhances localisation through pixel-level mask prediction and multi-scale fusion. A Triple-Level Confidence (TLC) strategycategorized into high, medium, and low consistency thresholdsis introduced to enforce consistency loss and improve generalisation across domains. Additionally, a pseudo-labelling scheme refines the adaptation process through self-training under structured domain noise. FASTR is trained and evaluated on both synthetic (SynthText, MJSynth) and real-world (ICDAR 2013, SVT, and IIIT5K) datasets. It achieves a word recognition accuracy of 92.4% on IIIT5K, 89.7% on SVT, and 93.1% on ICDAR 2013, outperforming state-of-the-art baselines by an average margin of 2.8%. On cross-domain benchmarks with added noise, FASTR maintains high performance, achieving an average F1-score of 90.5%, with precision and recall values of 91.2% and 89.9%, respectively. Hyperparameters, training configurations, and evaluation metrics are transparently documented to ensure reproducibility. The findings demonstrate superior scale robustness, effective domain adaptation, and resilience to cluttered backgrounds, with explainability preserved through interpretable confidence maps and visual cues. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
Automated Classification of Medicinal Plants Using Lightweight Deep Learning and Transfer Learning
The identification of medicinal plants plays a pivotal role in traditional medicine, biodiversity conservation, and rural healthcare. Conventional manual identification methods are often time-consuming and error-prone, particularly when differentiating between morphologically similar species or plants at varying growth stages. Recent developments in deep learning, especially convolutional neural networks (CNNs) with transfer learning, have emerged as robust solutions for image-based classification tasks, offering efficiency and high accuracy with limited computational resources. The proposed framework employs a carefully structured deep learning pipeline integrating advanced preprocessing, lightweight architecture design, and domain-adaptive transfer learning. A large real-world dataset of 20,109 medicinal leaf images across 99 classes was standardized through resizing, normalization, and categorical encoding, followed by targeted data augmentation and class-weight balancing to address inter-class similarity and dataset imbalance. A key methodological novelty lies in the use of MobileNetV3 with an optimized transfer-learning strategy, leveraging its inverted residual blocks, Squeeze-and-Excite modules, and hard-swish activation to enhance texture-, venation-, and contour-based feature extraction in plant leaves. Unlike existing plant-recognition studies that rely on heavier CNNs, our approach introduces a computationally efficient, low-latency model specifically tailored for mobile and embedded deployment. Experimental results demonstrate that the proposed MobileNetV3-based model achieved a classification accuracy of 92.88%, with macro- and weighted-average F1-scores of 0.85 and 0.86, respectively. Precision and recall values across most classes ranged between 0.80 and 0.95, confirming the models reliability in differentiating species. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
k-Domination Vertex Connectivity in Internet of Things Networks
The Internet of Things refers to a collection of closely connected devices that form a network through wireless or wired communication technology that work together to achieve common goals for their users. The IoT devices that are distributed in nature may cause the system to suffer from server crashes, server omissions, incorrect responses, and arbitrary errors. In this paper, we present a method of fault tolerance in IoT networks using graph theory approach to ensure the robustness of the network in case of attacks or disconnections through the concept of domination vertex connectivity in graphs. We further study this parameter in case of the Tensor product and Lexicographic product of specific graph classes, which have major implications in IoT networks. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
Early Disaster Detection and Monitoring Using Text Analysis and Levy Flight-based Particle Swarm Optimization Algorithm
Disasters can strike unexpectedly and leave a trail of destruction, causing immense suffering and loss of life while disrupting entire communities. These events can be natural, such as floods, earthquakes, hurricanes, wildfires, or man-made, including industrial accidents and technological failures. This study investigates a hybrid approach that uses text analysis, natural language processing, and optimization techniques to identify and monitor disaster-related events. The methodology of this paper involves collecting and analyzing text, focusing on sentiment and keywords associated with disaster-related text. Various aspects of text patterns are examined to enhance the models performance. The proposed model uses a Levy flight-based Particle Swarm Optimization algorithm to select optimal features from a vector set. It uses Text Blob for sentiment analysis, cosine similarity to classify each tweet as a disaster, Count Vectorizer for feature extraction, and XGBoost machine learning algorithm for classification. The significance of this model is that it provides early warning and insight for any disaster based on text analysis and classification. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
Data-Driven Sustainability: Revolutionizing Hospital Supply Chains through Big Data Analytics
Purpose: Despite the growing interest in Big Data Analytics Capabilities (BDAC), its significant impact on hospital operations and supply chains in shaping hospital performance remains elusive. The study investigates the pivotal role of BDAC within the framework of hospital supply chains across India. Drawing upon the Resource-Based View, Dynamic Capability View, and Organisation Information Processing Theory, this research explores the intricate relationships among the organization's capability factors, BDAC, and hospital performance indicators. Design/Methodology/Approach: A conceptual model was developed and empirically tested using survey data collected from 446 hospital managers. The analysis was carried out by using partial least square-structural equation modeling (PLS-SEM). Findings: The results of this study support the significant mediating impact of BDAC on Operational Flexibility, Supply Chain Sustainability, and Organisation Revenue leading to the enhancement of organizational performance. The findings highlight the strategic importance of cultivating BDAC to improve operational efficiency and overall effectiveness in the context of Indian multispeciality hospitals. Originality/Value: This research contributes to the existing knowledge by highlighting the relationship between organization capability factors, BDAC, and performance indicators in the different settings of Indian multispeciality hospitals. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Facile fabrication of mesh-free, GO-reinforced ZrO2-based separators for advanced alkaline water electrolysis
Alkaline Water Electrolysis (AWE) is a promising method for sustainable hydrogen production due to its maturity and use of non-noble metal catalysts. A key challenge lies in developing cost-effective, durable, and scalable separators that ensure ionic conduction and separation between the electrodes. This study presents a mesh-free composite separator composed of zirconia nanoparticles (ZrO2 NPs), polysulfone (PSU), and graphene oxide (GO), eliminating the need for expensive polyphenylene sulphide (PPS) mesh and its hazardous hydrophilic surface treatments. GO was incorporated as a multifunctional additive to enhance mechanical strength, hydrophilicity, and dispersion of ZrO2 NPs. Separators were fabricated with varying compositions of ZrO2 NPs, PSU, and GO, and tested in a zero-gap titanium-based electrolyser using nickel foam electrodes and 30?wt% potassium hydroxide (KOH) electrolyte. Amongst them, the Sep72/25/3 separator (72?wt% ZrO2, 25?wt% PSU, 3?wt% GO) showed a low area-specific resistance (ASR) of 298?m? cm2 at room temperature (RT). It also exhibited excellent wettability with a reduced contact angle of 23 after 24?h conditioning in 30?wt% KOH, along with a notable improvement in tensile strength, from 1.75?MPa (without GO) to 3.26?MPa, validating the reinforcing role of GO. The results demonstrate a simple and scalable route for fabricating mesh-free separators that strike an optimal balance between ionic resistance, mechanical strength, and wettability, thereby offering a cost-effective alternative for next-generation advanced alkaline water electrolysis (AAWE) systems. The Korean Ceramic Society 2025. -
Resilient strategies for sustainable tourism development: a land use analysis of the Kannur-Iritty corridor in Kerala, India
The study explores the resilience of the KannurIritty corridor in northern Kerala, where rapid infrastructural growth following the opening of Kannur International Airport in 2018 has reshaped mobility, land use, and tourism potential. The primary objective is to identify specific areas where challenges exist and provide policy recommendations to promote resilient and sustainable tourism development along the corridor. Integrating spatial, environmental, and perceptual data, the analysis develops a composite framework to assess environmental, infrastructural, socio-economic, and governance resilience. Results reveal strong infrastructural connectivity but moderate ecological and community adaptability. Water quality deterioration and unplanned land conversion reduce ecological resilience, while limited local awareness constrains adaptive tourism diversification. Conversely, peri-urban zones with mixed land use demonstrate higher potential for low-impact tourism such as farm and eco-tourism. Strengthening corridor governance through integrated land-use control, water-quality restoration, and community participation is essential to sustain tourism resilience. The study recommends targeted policy interventions, prioritizing sustainable infrastructure, decentralized waste management, and participatory tourism planning, to align regional development with Keralas responsible tourism agenda and provide a replicable model for other emerging tourism corridors in India. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Influence of industrialization on economic growth in the asian tigers and lessons for India
Economic growth over the past two centuries has been driven mainly by the process of industrialization. Mechanized manufacturing, factories, and technological advancements have contributed largely to economic development. A prime example of such countries is the Asian Tigers- Hong Kong, Singapore, Taiwan, and South Korea, and the Tiger Cubs- Indonesia, Thailand, Malaysia, Philippines, and Vietnam that have achieved rapid industrialization through export-led strategies, technological innovation, and strong policies fostering economic development. India gained its independence around the same time as the Tiger, though the pursuit of industrialization hasnt been as pronounced in India as it has been in the Tigers. This study examines the impact of industrialization, proxied with industrial efficiency, on the GDP per capita of the tiger economies and India. Along with other control variables like FDI inflows, inflation, market capitalization, manufacturing exports, ICT imports, and CO2 emissions. Using data from 1991 to 2022, Using data from 1991 to 2022, a 2SLS model is applied to the Tiger economies using the instrument, control of corruption. A time series Autoregressive Distributed Lag model is used for India. The findings of this paper confirm that industrialization was the primary driver of the economic success of the Asian Tigers, while showing weaker progress in India. Building efficient infrastructure facilities, strengthening human capital formation and export-led manufacturing could allow India to emulate the strategy of the Asian Tigers. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. -
Enablers of Circular Practices in Fast Fashion Supply Chains: a Study Towards Sustainable Fashion Development
The fast fashion industry confronts substantial sustainability issues because of its high resource consumption and waste output. The literature reveals that past studies have less focused on circular practices in fast fashion supply chain. This study aims to identify and analyse the potential enablers of circularity within fast fashion supply chains, promoting sustainable production and consumption practices. Through a comprehensive literature review and expert consultations with 12 domain experts from academia, apparel manufacturing, and sustainability practices, sixteen enablers of circularity were identified. To understand the interrelationships and hierarchical structure among the identified enablers, Grey DEMATEL method was employed. The results from the study reveal that sound purchasing policies, reverse logistics and adoption of eco-friendly and recyclable packaging act as the most influential causal enablers, while selection of fibres and consumer awareness and acceptance of recycled, refurbished clothing emerge as key effect enablers. By mapping these interrelationships, the study offers actionable insights for fashion retailers, policymakers, and sustainability practitioners to strengthen circular strategies. The findings contribute to advancing circular economy theory in the fashion sector and provide a practical framework for accelerating the transition towards sustainable and circular business models in fast fashion. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. -
Analyzing SDGs in high-and-low-emission industries: a comparative study of sustainability reports
This study assesses different Sustainable Development Goals (SDGs) in high- and low-polluting industries through a comparative analysis of sustainability reports. The objective is to evaluate SDG-related terms in reports from 16 companies across four sectorsCement, Automobile, Electric Equipment, and ITover five years. Using Python for data extraction and the text2sdg package in R programming for SDG term detection, the study identifies both prioritized and overlooked SDGs. Results indicate that high and low-polluting industries share similar SDG focus areas. SDGs 6 (Clean Water and Sanitation), 12 (Responsible Consumption and Production), and 13 (Climate Action) received the most attention. In contrast, SDGs 1 (No Poverty), 2 (Zero Hunger), 5 (Gender Equality), 10 (Reduced Inequalities), and 14 (Life Below Water) are consistently underrepresented. The findings suggest that both categories of industries acknowledge the importance of sustainability, yet significant gaps remain in addressing social and environmental challenges. This research contributes to the broader discourse on corporate sustainability and its role in achieving the 2030 Agenda, offering actionable insights for industries to increase their focus on less-considered SDGs. By identifying areas of improvement, the study supports efforts to foster more inclusive and environmentally responsible business practices. The Author(s) 2025. -
Environmental sustainability and management (ES & EM) practices among Service Sector Institutions in Kathmandu, Nepal
Environmental sustainability (ES) emerged in response to the felt negative consequences of overexploitation of the environment and natural resources. ES has gained momentum in recent decades in areas of social policy, means of production, development, economy and everyday individual behaviours. The drive towards ES has been firmly based in scientific research which has been dominated by Western developed countries. For a tiny developing country like Nepal, its overall contribution to global environmental pollution and degradation is minimal; however, it has been disproportionately negatively affected by global warming, pollution, etc. There is sparse research on the various measures or state of environmental sustainability standards, policies or behaviours in Nepal. In this quantitative cross-sectional study, five types of Service Sector Institutions (SSI) from Kathmandu, Nepal were studied for their environmental sustainability (ES) and environment management (EM) measures in place at their facilities. SSIs were chosen because they have the distinct characteristic of being directly involved with large sections of populations, and hence hold the potential to pioneer innovative and effective solutions towards fostering environmental sustainability. ES was defined in terms of three measures related to sustainable freshwater use, energy use and waste management. The measures for EM included organizational capacity building and attitudes towards ES. Data was collected directly from representatives of the SSIs through self-report interviews or forms. The 104 SSIs included 25 schools, 26 restaurants, 16 hotels/lodges, 18 banks and 17 health care organizations. Based on frequency distributions and ANOVA tests, it was found that the overall extent of ES and EM practices among the 102 SSIs was dismally low in Kathmandu, Nepal. As given in the figure, educational institutions performed significantly better across all five ES and EM measures indicating highest prevalence of sustainability measures and practices. Banks performed significantly worst across all categories compared to the four other SSIs, indicating least amount of efforts in ES and EM. All five measures of environmental sustainability (ES) and environmental management (EM) were also strongly positively correlated amongst each other. A huge amount of effort is still required to revamp the existing ES and EM policies and organizational norms in Nepal. Moreover, it remaining challenging to change peoples attitudes and behaviours in order to effect lasting positive changes in the future and conserve the local environment better. The Author(s) 2025. -
Bridging tradition and sustainability through a behavioural model for the adoption of green wedding practices
The study aims to develop a behavioural model which indicates the intentions of unmarried people for adoption of green marriages. The study aims to explore the factors which affects the green marriage intentions, and also developing a green marriage intention matrix for categorizing the green marriages. The study is based on the primary data collected from a sample of 480 unmarried people restricting from age groups of 2040 only. Researchers have used Exploratory factor analysis to explore the factors, multiple regression analysis to develop the green marriage intentions model, and correlation analysis. ANOVA method was used to measure the impact of age, gender, and education on environmental attitude and green marriage intentions. It was found from the study that environmental attitude, social influence, perceived barriers, and perceived benefits are the four major factors which affects the green marriage intentions of the people. Further, the green marriage intentions matrix showed four categories of the people based on the environmental attitude and social influence namely; Influential green marriage, Casual green marriage, fashionably Green Marriage, and Eco-conscious green marriage. The study also included a detailed strategic plan with proposed actions to handle the barriers and promoting the green marriage practices along with environmental stewardship. The Author(s) 2025. -
Intellectual capital independent directors and leverage as determinants of sustainable growth in Indian pharmaceutical companies listed in the NSE NIFTY pharma index
This research investigates how Intellectual Capital (IC) influences the Sustainable Growth Rate (SGR) of Indian pharmaceutical firms that are part of the NSE NIFTY Pharma index. This study delves deeper into the moderating influence of Independent Directors and examines the control effect of Leverage (Debt-Equity Ratio) on this relationship. A descriptive research design was utilized, employing panel data from FY 2015 to FY 2024. The dataset was obtained from the Prowess database (CMIE), and the Two-Step System GMM method was utilized with STATA 18 to guarantee a thorough econometric analysis. The findings indicate that Intellectual Capital (IC) plays a crucial role in enhancing SGR, thereby reinforcing the Resource-Based View (RBV). Independent Directors effectively moderate this relationship, strengthening Agency Theory. Nonetheless, leverage has a detrimental effect on SGR, consistent with Pecking Order Theory. Pharmaceutical companies ought to allocate resources towards Intellectual Capital, enhance corporate governance, and uphold appropriate debt levels to ensure sustained long-term growth. This study effectively combines IC, corporate governance, and financial leverage in the Indian pharmaceutical sector, providing valuable concrete insights for policymakers, academics, and industry experts. The Author(s) 2026. -
Effectiveness of integrated waste minimisation strategies in high-rise residential construction projects
Construction waste has become a significant sustainability concern in fast-growing Indian cities, especially in high-rise residential projects characterised by intensive material flows. This study conducted a comparative analysis of material waste across the various stages of eight high-rise residential projects in Bengaluru, India. Four of the projects followed the conventional method, while the remaining four used an efficient method to reduce material waste. The material usage and generation were recorded for seven phases, each lasting two months, both quantitatively and qualitatively, using data and observations. Additionally, Relative Reduction (RR) values were calculated to assess the effectiveness of the implemented interventions by comparing the projected values for the baseline scenarios of uncontrolled and controlled projects. Uncontrolled projects exhibited an average wastage growth of 23% and negative RR values (? 4.48% to ? 9.15%), indicating a deterioration in waste management performance. At the same time, the sites implementing waste control measures demonstrated waste stability or reduction, with RR values of 713%, due to improvements in site supervision, material storage, batch extraction accuracy, and control of material issues. Material-wise analysis further supported the reduction in waste under controlled conditions. The benchmarking system developed in this research will provide practical support for waste tracking and remedial actions. The study demonstrates, using data, that low-cost, straightforward process interventions can substantially increase the effectiveness of resource use in achieving SDG 11.6 and SDG 12.5. The Author(s) 2026. -
Assessment of mechanical and micro structural analysis of iron ore tailings and red mud sustainable bricks using multiple linear regression
The synergistic utilization of mining and industrial wastes in the construction industry represents a major step forward in the environmental and sustainable constructions. This research has presented an exploration of the feasibility of combined use of iron ore tailings (IOT) and red mud (RM) in sustainable brick manufacture. The IOT and RM have been blended with ground granulated blast furnace slag (GGBS) and lime solution for the production of bricks without high-temperature kiln-firing. Eight different combinations of (IOT + GGBS) and (RM + GGBS) with varying ratios of the principal components have been used in the sustainable bricks. Multiple regression analysis has been employed to estimate the strength of a different compositions of bricks. The bricks with 70% RM and 30% GGBS have attained the highest strength of 9.68 MPa and the bricks with 70% IOT and 30% GGBS attained the highest strength of 6.25 MPa among various combinations. The water absorption results of 18.7% and 19.02% have fulfilled the IS standards too. The research has revealed that the bonding between bricks and mortar has been influenced by the Si-Al matrix at low calcium content. Additionally, the formation of the delicate Ca-Al-Si phase capable of permeating the brick, has contributed to the constructive brick structure. The study also reinforced the view that the combined use of mining and industrial waste in production of environmentally friendly bricks is viable. The Author(s) 2025. -
Development of flexible FRP butt joints between stiff FRP panels using hybrid resin and kevlar reinforcement for advanced structural applications
The present work focuses on developing a flexible fiber-reinforced polymer (FRP) butt joint between stiff FRP panels (adherends). The goal is to ensure the joint is flexible, moisture-resistant, and abrasion-resistant, while maintaining the original FRP strength and facilitating the casting of complex and modular shapes. To achieve this, an elastomeric resin system comprising polyurethane and polyurea in an optimized ratio of 10:6 (by weight) was formulated for the flexible joint region, whereas isophthalic polyester resin was used in the stiff FRP panels. The joint was reinforced using a hybrid layup of three layers of plain-weave Kevlar fabric, with glass fiber chopped strand mat (CSM) interleaved between the Kevlar layers, over an overlap length of 50mm on both panel edges. Mechanical characterization revealed that the hybrid resin alone exhibited an average tensile strength of 16.3MPa; however, no slip was observed for the 50mm overlap of the reinforced joint, and failure occurred in the adherend. Furthermore, the joint exhibited favorable performance under abrasion, water immersion, low-temperature fatigue, and drop-weight impact testing. These results confirm that the proposed hybrid Resin-Kevlar reinforced joining approach offers a reliable pathway for fabricating flexible, durable, and high-strength FRP joints suitable for advanced structural applications. The Author(s) 2026.