Av Engineering (Czechia)

Abstract: Beams commonly fail in shear due to diagonal tension, shear compression, or web shear, typically caused by insufficient reinforcement, overload, or material weaknesses. One solution is the use of fiber-reinforced polymer (FRP) wrapping, which enhances durability and strength. FRP, a composite of fibers and resin, exhibits superior mechanical properties such as impact resistance, stiffness, and corrosion resistance. This study investigates the effectiveness of Polypropylene Fiber Reinforced Polymer (PFRP) in strengthening reinforced concrete (RC) beams. Mechanical behaviour was assessed through compression, split tensile, and flexural tests. RC beams wrapped with PFRP were also analyzed in terms of shear strength using experimental and simulation methods. The results indicate higher strength in PFRP-wrapped beams compared to unwrapped ones.

Abstract Objectives To improve ductility and impact performance of AA2029–SS316L friction stir welded joints by optimizing key FSW process parameters using numerical simulation and statistical analysis. Methods Numerical simulations using ANSYS and Response Surface Methodology were employed, considering beam radius, clearance, force, and HAZ temperature, with ANOVA, fit statistics, and perturbation analysis validating structural responses. Results Optimized parameters – BWC 2.0067 mm, HAZT 1,600.41 °C, and force 5,482.06 kN – significantly enhanced ductility, reduced stress concentrations, and improved joint integrity under impact loading conditions. Conclusions The optimized FSW parameters exceed industry standards, demonstrating improved mechanical performance and structural reliability of AA2029–SS316L joints, confirming the effectiveness of simulation-driven optimization for advanced welding applications.

The Automatic Dusk Flipper Machine (ADFM) is a mechatronics-based solution designed to reduce poultry mortality and labor through automation. Unlike existing litterturning systems, the ADFM employs a dual-motor DC architecture and simplified mechanical drive to achieve consistent flipping efficiency, operating at 230 RPM via a rear-mounted plough. This helps manage heat in the bedding, promoting flock health. The system emphasizes energy efficient motor control, allowing for operation on DC mains or battery power, making it suitable for rural poultry farms. Speed and power consumption analysis supports future integration of intelligent control and environmental sensing.

O-rings, vital in aerospace, automotive, and manufacturing, require precise defect detection, as even minor imperfections can cause failure. Traditional methods like manual checks often fall short. This project aims to create an automated, real-time O-ring defect detection system. The system integrates mechanical automation, computer vision, and embedded electronics. A Waveshare OV5693 5MP camera, mounted around a rotary fixture, provides 360° inspection. CAD models were created in SolidWorks/Fusion 360, with the setup built on a 3Dprinted <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex>-slot structure. A machine learning pipeline, combining ResNet-18 and Autoencoder models, was trained on custom and Roboflow datasets. Image processing used OpenCV, TensorFlow, and PyTorch, with Streamlit for real-time defect visualization. The ResNet-18 model achieved 97% accuracy, while the autoencoder showed low reconstruction loss. The combined ensemble model reached 93% accuracy, with the system achieving 92.3% accuracy and sub-second latency under real-time conditions. This solution provides a scalable, energy-efficient, and interpretable alternative to traditional inspection, advancing AI-driven quality assurance in industry.

With the rapid growth of electric vehicles (EVs), the need for advanced Battery Management Systems (BMS) that ensure efficiency, longevity, and operational safety has become increasingly critical. This project presents the design and implementation of a Smart Battery Management System (SBMS) that integrates hardware, software, and risk analysis into a unified platform. The system features a synthetic data generator that simulates realistic lithium-ion battery charge-discharge cycles, enabling robust testing of analytical models under diverse conditions. An in-house analytics engine processes this data to extract key performance metrics such as State of Health (SoH), Remaining Useful Life (RUL), degradation trend, and efficiency. A modular hardware prototype incorporates isolated voltage, current, and temperature sensing, along with passive balancing to maintain uniform cell behavior. The entire system is complemented by a React-based web dashboard that visualizes real-time battery status, historical trends, and predictive indicators. Additionally, a structured HAZOP (Hazard and Operability) analysis is conducted on critical hardware subsystems-including sensing, balancing, and communication-to identify potential failure modes and implement appropriate mitigation strategies. The combined approach not only enables proactive maintenance and early fault detection but also enhances safety, usability, and lifecycle optimization compared to conventional BMS architectures. This SBMS serves as a scalable, interdisciplinary framework for next-generation EV battery monitoring.

Abstract - Urban street development projects in India are increasingly challenged by complex planning deficiencies, fragmented execution mechanisms, and on-site management inefficiencies. This review paper synthesizes insights from twenty empirical and conceptual studies to identify, evaluate, and consolidate the major planning and execution challenges faced during urban street infrastructure development. The collective analysis reveals that inefficiencies often originate from overlapping factors such as inadequate project planning, poor coordination among stakeholders, delayed decision-making, financial constraints, and design-related ambiguities. Site-based challenges particularly concerning material management, equipment allocation, traffic diversion planning, and utility relocation—emerge as recurrent sources of delay and cost escalation. Studies on Indian contexts highlight that unclear project scopes, weak communication channels, insufficient risk assessment, and lack of integrated scheduling tools contribute significantly to time and cost overruns. Comparative findings from both Indian and international case studies underscore the universal importance of adopting structured project management frameworks, enhanced site supervision, and stakeholder collaboration mechanisms. Furthermore, the integration of digital technologies such as BIM, GIS, and remote monitoring tools has been identified as a transformative approach to improving planning accuracy, progress tracking, and coordination efficiency at the site level. The synthesis concludes that a shift from conventional top-down planning to adaptive, site-based management frameworks supported by real-time data systems and participatory decision-making can substantially improve execution reliability, sustainability, and public service continuity in urban street development projects. This review thus provides a consolidated foundation for policymakers, urban planners, and project managers to implement data-driven, site-responsive strategies that address the persistent planning and execution challenges in India’s evolving urban infrastructure landscape. Keywords: Adaptive Management, BIM Integration, Cost Overrun, Execution Challenges, GIS Application, Infrastructure Development, Planning Efficiency, Project Coordination, Site-Based Approach, Urban Street Development

Trajectory classification is a well-known class of problems in movement data analysis. The technical advancement in GPS enabled devices and autonomous systems has made it possible to collect movement data of objects and events in real time. This leads to massive and complex trajectory data that has associated movement patterns. Trajectory analysis becomes challenging due to its voluminous size and large dimensionality. Furthermore, traditional methods fail to handle this massive data for efficient processing. So, the focus is shifted to the automation of trajectory data analysis by researchers and practitioners. Deep Learning (DL) based techniques are efficient in the automatic handling of trajectory analysis. It is quite promising in solving many trajectory-related tasks, such as classification, prediction, segmentation, etc.The current paper delves into the trajectory classification activity using deep learning techniques. It specifically investigates Convolutional Neural Networks (CNNs) based methodologies to solve classification tasks. Due to the increased accuracy and reliability of CNN models, their applicability for trajectory classification is widely being investigated. Our work brought a deep study of trajectory classification and implemented a few notable CNN architectures, viz. ResNet152, NasNet, EfficientNet, DenseNet, and Xception. It presents a comparative analysis of these CNN methods to classify trajectory data. The methods are tested on the Automated Information System (AIS) dataset, which is part of the ship’s onboard Automated Information System (AIS). The AIS datasets are preprocessed and made ready for the implemented system.

This study examines the effectiveness of deep learning techniques through a comparison of two of the widely used Convolutional Neural Network (CNN) architectures, SqueezeNet and MobileNet, on the CIFAR-10 and MNIST datasets in poor conditions. Both architectures were compared after numerous techniques had been used to enhance their accuracy. Based on the MNIST dataset, MobileNet achieved a training accuracy of 98.34% in conjunction with a validation accuracy of 91.40%, thus demonstrating its capability to tackle complex image recognition tasks. SqueezeNet, on the other hand, was poor with a training accuracy of 9.85% and a validation accuracy of 9.95%. When the CIFAR-10 dataset was employed, MobileNet achieved a training accuracy of 39.43% and a validation accuracy of 9.95%, while SqueezeNet achieved a training accuracy of 10.10% and a validation accuracy of 9.70%. This study indicates the significance of accuracyenhancing techniques and highlights the performance of different CNN architectures in poor conditions.