IIRJET

Big Data–Driven Structural Health Monitoring of High-Rise Buildings Using IoT Sensor Networks

2026-02-11T09:12:15+00:00

Large amounts of diverse sensor data are frequently difficult for conventional structural health monitoring (SHM) techniques to handle in real time. Because of their increasing complexity of structures and susceptibility to dynamic loads including wind, seismic activity, & occupancy-induced vibrations, high-rise buildings' long-term performance and safety are major challenges in contemporary urban environments. This paper suggests an IoT sensor network-based Big Data-Driven Structural Health Monitoring architecture for high-rise structures in order to overcome these constraints. In order to effectively store, process, and analyse high-velocity structural response data, the suggested system combines distributed Internet of Things (IoT) sensors for continuous data collecting with big data analytics platforms. To identify irregularities, evaluate the state of the structure, and anticipate possible damage patterns, advanced data machine learning and analytics approaches are used. The framework improves structural safety and lowers lifetime maintenance costs by enabling continuous tracking, early damage detection, and predictive maintenance. When compared to traditional SHM techniques, the suggested big data-driven strategy greatly increases monitoring accuracy, adaptability, and decision-making efficiency, according to experimental evaluation utilising simulated & real-time sensor datasets. The findings demonstrate how data analysis and IoT technology can be combined to create intelligent, robust, and smart infrastructures for high-rise buildings.

Design and Analysis of Electric Vehicle Charging Infrastructure with Renewable Sources

2026-02-11T10:06:17+00:00

In order to support the quick widespread use of this mode of transportation, a dependable infrastructure for charging electric vehicles is becoming more and more necessary. The demand for dependable and sustainable infrastructure for charging has surged due to the quick expansion of electric vehicles (EVs), creating difficulties for current power systems. A viable way to lower greenhouse gases, peak load stress, and operating expenses is to incorporate renewable energy sources (RES) like wind and solar power into EV charging stations. The planning and evaluation of a renewable energy-powered charging system for electric cars with potential grid support are presented in this paper. To guarantee effective and continuous charging under changing load and generation conditions, the suggested system combines photovoltaic generating, energy storage, power electronics converters, and a smart energy management strategy. System performance is assessed using simulation-based analysis in terms of cost-effectiveness, grid dependency reduction, renewable energy utilisation, and charging efficiency. The findings show that while preserving power quality and dependability, environmentally friendly EV charging infrastructure greatly increases energy efficiency and sustainability. The viability of implementing cost-effective and environmentally friendly EV charging options for next smart transportation systems is demonstrated by this study.

Design and Implementation of Intelligent Electronic Systems Using Embedded AI and Quantum Computing

2026-02-11T10:12:22+00:00

Computational methods that surpass the capabilities of traditional embedded intelligence are required due to the quick development of intelligent electronic devices. In order to improve system performance and decision-making efficiency, this study describes the development and execution of a smart electronic system that combines quantum computing and embedded artificial intelligence. The suggested framework uses quantum computer techniques for optimization and difficult problem solving, while integrating resource-constrained embedded devices with predictive models deployed at the edge. A hybrid traditional and quantum architecture is shown, in which quantum-assisted computation facilitates data processing and model optimisation while embedded AI conducts real-time inference. An embedded processor platform is used to implement the system, and representative smart electronics applications are used to assess it. When compared to conventional embedded AI techniques, experimental results show increased scalability, decreased computational delay, and higher accuracy. The suggested concept offers a scalable route to next-generation intelligent electronics and demonstrates the viability of combining quantum-assisted cognition with embedded electronic systems. For upcoming embedded systems that need high-performance intelligence with limited resources, this study provides a useful paradigm.

Design and Thermal Analysis of a High-Efficiency Heat Exchanger for Industrial Applications

2026-02-11T11:05:26+00:00

The growing demand for energy-efficient thermal systems in industrial processes has increased the importance of high-performance heat exchangers. This study presents the design and thermal analysis of a high-efficiency heat exchanger intended for industrial applications. The heat exchanger is designed by considering key thermal and hydraulic parameters such as heat transfer rate, overall heat transfer coefficient, pressure drop, and effectiveness. Standard design methodologies are employed to determine the geometric configuration and material selection based on operating conditions. Thermal performance analysis is carried out using analytical calculations and computational fluid dynamics (CFD) simulations to evaluate temperature distribution, heat transfer characteristics, and flow behavior. The simulation results are validated against theoretical calculations to ensure accuracy. The performance of the proposed design is compared with that of a conventional heat exchanger under similar operating conditions. Results indicate a significant improvement in heat transfer efficiency with an acceptable pressure drop, demonstrating the suitability of the proposed heat exchanger for industrial use. The findings of this study highlight the potential of optimized heat exchanger design to enhance thermal performance, reduce energy consumption, and improve overall system efficiency in industrial applications.

Artificial Intelligence–Based Predictive Maintenance Framework for Marine Propulsion Systems

2026-02-11T12:07:10+00:00

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Marine propulsion systems are critical to the safe and efficient operation of ships, and unexpected failures can lead to significant operational downtime, increased maintenance costs, and safety risks. Conventional maintenance strategies are largely time-based or reactive, limiting their effectiveness under varying operating conditions. This paper proposes an artificial intelligence–based predictive maintenance framework for marine propulsion systems using multi-sensor operational data. The proposed framework integrates data acquisition from key propulsion components, signal preprocessing, and intelligent condition assessment using machine learning techniques. An AI model is developed to automatically learn fault-related patterns and degradation trends, enabling early fault detection and accurate health state prediction. Experimental results demonstrate that the proposed approach improves fault diagnosis accuracy and supports condition-based maintenance decisions when compared with traditional maintenance strategies. The findings highlight the potential of artificial intelligence to enhance reliability, reduce unplanned downtime, and optimize maintenance planning in marine propulsion systems.