This study thoroughly investigates how civil engineering management can effectively control project costs. Through analyzing key cost control strategies within civil engineering management, the research reveals the significance of economic principles and rational resource utilization. Using a case study approach, diagnostic analysis of actual engineering projects was conducted, validating the effectiveness of measures such as detailed budgeting, strengthened cost accounting, and enhanced cost management. The findings suggest that implementing scientifically sound civil engineering management can significantly reduce project costs and enhance project efficiency. Additionally, the study recommends reforming traditional management approaches and integrating modern management concepts and methodologies, further optimizing cost control in civil engineering projects. The results offer essential reference value for improving cost management, increasing project benefits, and promoting long-term civil engineering development.

Show more

Kunshan, a typical river network city located in the Taihu Plain, has a dense network of rivers, with upstream receiving discharge from rivers and downstream supported by multiple tributaries entering the Yangtze River. Additionally, the city’s internal embanked areas are numerous. Furthermore, the existing information system was constructed prematurely and lacks professional forecasting and simulation technologies, making it difficult to fully grasp the current water security situation and rapidly display the effects of scheduling plans. This study analyzes the “Four-Preparation” integration and proposes improvements. Based on Kunshan’s current situation, the goal is to construct an Integrated Four-Preparation System, establishing a scientific coupling system for forecasting, early warning, rehearsal, and contingency planning. This will strengthen intelligent and smart development applications to achieve scientific disaster reduction.

Show more

To improve the accuracy of photovoltaic power prediction, a combined prediction model integrating multiple algorithms is proposed. First, key factors are selected, and the Gaussian Mixture Model (GMM) is used to cluster and generate historical days with high correlation to the forecast day. Then, a photovoltaic power prediction model based on Variational Mode Decomposition (VMD) and an improved Grey Wolf Optimizer Kernel Extreme Learning Machine (MSI-GWO-KELM) is constructed. Finally, actual data from a photovoltaic power station in Australia is used as a case study. The results show that the VMD-MSI-GWO-KELM model is suitable for different weather conditions and offers better prediction accuracy than other models.

Show more

With the acceleration of urbanization, flood control and drainage management in cities are facing increasing challenges. As a nationally recognized model for effective flood control, Suzhou Industrial Park has experienced frequent extreme weather events in recent years, highlighting the urgent need for informatization in flood control and drainage management. This paper provides a detailed introduction to the design scheme of the flood control and drainage decision-making command information system for Suzhou Industrial Park, covering key aspects such as system requirement analysis, overall architecture, core technologies, and risk management. By integrating multi-source data, including meteorology, hydrology, waterlogging points, and the sewage pipeline network, the system establishes a comprehensive flood control and drainage decision-making platform featuring situational awareness, monitoring and early warning, and command and dispatching functions. The system aims to enhance the flood control and drainage capabilities of the park, ensure urban safety, and provide a reference for the informatization of flood management in other cities.

Show more