类比估算法在复杂工程项目中的精度提升策略研究 -工程造价专业

摘要

复杂工程项目因其规模庞大、不确定性高和多变性显著等特点，对成本估算提出了严峻挑战。类比估算法作为一种基于历史数据的快速近似方法，在复杂工程项目中得到了广泛应用，但其精度受制于项目相似度评估和数据质量等因素。为提升类比估算法在复杂工程项目中的应用效果，本研究以提高估算精度为核心目标，系统探讨了影响类比估算法精度的关键因素，并提出了一种集成多维度相似性分析与机器学习优化的改进策略。具体而言，研究首先通过文献分析和案例调研明确了复杂工程项目的特点及其对类比估算法的具体需求；其次，构建了包含技术特征、资源条件和环境因素的多层次相似性评估模型，以更全面地衡量项目间的可比性；再次，引入机器学习算法对历史数据进行清洗、筛选和校正，从而减少数据噪声对估算结果的影响；最后，设计并验证了一种结合专家经验和数据驱动的混合估算法则。研究结果表明，所提出的改进策略能够显著提升类比估算法的精度，尤其在处理具有高度不确定性和异质性的复杂工程项目时表现出更强的适应性。本研究的主要贡献在于将传统类比估算法与现代数据分析技术相结合，为复杂工程项目的成本管理提供了更为科学和可靠的工具支持，同时为相关领域的理论发展和实践应用奠定了基础。

关键词：复杂工程项目；类比估算法；相似性分析；机器学习优化；成本估算精度

ABSTRACT

Complex engineering projects, characterized by their large scale, high uncertainty, and significant variability, pose severe challenges to cost estimation. Analogous estimation, as a rapid approximation method based on historical data, has been widely applied in complex engineering projects; however, its accuracy is constrained by factors such as project similarity assessment and data quality. To enhance the application effectiveness of analogous estimation in complex engineering projects, this study focuses on improving estimation accuracy and systematically investigates the key factors influencing the precision of analogous estimation, proposing an improved strategy that integrates multidimensional similarity analysis with machine learning optimization. Specifically, the research first clarifies the characteristics of complex engineering projects and their specific requirements for analogous estimation through literature analysis and case studies; secondly, it constructs a multi-level similarity evaluation model encompassing technical features, resource conditions, and environmental factors to more comprehensively measure comparability between projects; thirdly, it incorporates machine learning algorithms to clean, filter, and correct historical data, thereby reducing the impact of data noise on estimation results; finally, it designs and verifies a hybrid estimation rule combining expert experience with data-driven approaches. The results demonstrate that the proposed improvement strategy significantly enhances the accuracy of analogous estimation, particularly exhibiting stronger adaptability when dealing with complex engineering projects characterized by high uncertainty and heterogeneity. The primary contribution of this study lies in integrating traditional analogous estimation with modern data analysis techniques, providing a more scientific and reliable tool support for cost management in complex engineering projects while laying a foundation for theoretical development and practical applications in related fields.

Keywords: Complex Engineering Projects; Analogy Estimation Method; Similarity Analysis; Machine Learning Optimization; Cost Estimation Accuracy