摘要

监理人员的绩效对于监控、控制和协调项目的施工过程至关重要，以是保障预期进度、成本、质量和其他目标如期完成的关键要素。但是，由于数据不足、主观错误等问题，往往难以有效评估监理人员绩效。因此，本文通过集成层次分析过程（AHP）和人员日常活动跟踪，提出一种数据驱动的监理人员绩效定量评估方法。该方法包含指标提取、权重分析、数据驱动的指标计算与案例验证等部分。首先，研通过文献综述及对施工监理信息系统（CSI系统）的调研与功能分析，抽取了评定人员绩效的关键指标。然后，研究引入AHP及问卷调研确定了各指标的权重及其相互关系。进而，研究结合CSI系统中监理人员日常工作流程和巡检活动记录信息，研发了绩效指标的自动计算方法和软件模块。最后，将所提出的方法在某真实工程案例中进行了应用验证。结果表明，所提方法能够全面、自动的量化施工监理人员的工作绩效，为基于人员日常工作记录来自动评价其绩效水平提供了思路。同时，数据驱动的流程也增强了我们对人员行为和评估指标的深度洞察，并有望提升组织中每个成员的绩效。

The performances of construction supervisors are essential for the monitoring, control, and coordination of the construction process of a project in order to adhere to a predefined schedule, cost, quality and other factors. However, it is challenging to evaluate their performance due to limitations such as data deficiency, human error, etc. Thus, this paper proposes an approach to data-driven quantitative performance evaluation of construction supervisors by integrating an analytic hierarchy process (AHP) and activity tracking. The proposed approach contains three parts, namely, index extraction, weighting, data-driven index calculation, and then validation by case study. Firstly, performance indexes were developed based on a literature review as well as surveys and function analysis of the information system for construction supervision (CSI system). Then, the weights of and relationships among of the indexes are determined by AHP. After that, with daily workflow and inspection activities tracked in the CSI system, a method and a software module for automatic calculation of indexes were developed. Lastly, the proposed approach was validated by a real-world case. The result showed that the proposed approach can quantify the performance of a construction supervisor systematically and automatically, which shed lights on how to evaluate the performance of a worker based on the tracking of daily activities. The data-driven process enhanced our strong interpretation of member actions and evaluation indexes, and can boost the performance of every member in an organization.

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This research was funded by the Science and Technology Plan Project of the Zhejiang Provincial Department of Transportation (grant number 2020061).

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