Dashboard Real-Time Monitoring of Construction Projects

The complexity of managing large-scale construction projects necessitates efficient real-time monitoring systems to ensure timely completion, cost control, and streamlined resource management. Traditional monitoring methods often rely on manual data collection, periodic updates, and fragmented communication, leading to inefficiencies and delays. This research presents a software-based real-time dashboard that enables automated tracking, visualization, and management of construction projects using modern web technologies and data analytics. The proposed system is developed using Next.js for the frontend, Node.js/Python for backend processing, and PostgreSǪL/MongoDB for database management, ensuring a scalable and high- performance application. The dashboard provides key features such as project progress tracking, workforce management, safety reporting, financial analysis, and schedule adherence monitoring. Data is collected from multiple sources, including project management tools, manual entries from site supervisors, and integrated third-party APIs, ensuring seamless updates and comprehensive project insights. To enable real-time updates, the system incorporates WebSocket communication, allowing instant synchronization of project status, task completion, and financial data. Additionally, role- based access control (RBAC) ensures secure and efficient collaboration between project managers, engineers, supervisors, and clients. The software integrates interactive data visualization tools to provide an intuitive user experience, enabling stakeholders to analyze trends, identify risks, and make informed decisions. The effectiveness of this dashboard has been evaluated based on parameters such as system response time, data accuracy, user experience, and integration efficiency. Results indicate that the system significantly improves decision-making speed, reduces manual reporting errors, and enhances overall project transparency. By automating report generation, schedule management, and financial tracking, this software reduces administrative overhead and optimizes resource utilization. This research contributes to the development of smart construction management solutions by leveraging modern web technologies, cloud integration, and real-time data processing. Future work will focus on AI-driven predictive analytics, machine learning for risk assessment, and further automation to enhance efficiency and scalability.