利用以往资料评价混凝土厂生产市政建筑用高质量混凝土的准备情况

IF 1 Q4 ENGINEERING, CIVIL
M. Mohammadian, M. S. Zadeh
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引用次数: 0

摘要

测试混凝土工厂生产高质量混凝土能力的唯一方法是测试其最终产品。此外,测试和控制混凝土质量的过程既耗时又昂贵。在这方面,有一种快速、廉价和有效的方法来预测混凝土工厂生产高质量混凝土的准备情况是非常有价值的。本文提出了一种概率多属性算法来解决这个问题。在该算法中,目标是根据混凝土抗压强度的误差率来评估混凝土工厂生产高质量混凝土的准备情况。该算法利用过去的信息和数据挖掘技术,通过混凝土工厂生产因素与过去信息的相似性来预测混凝土工厂的准备水平。使用基于生产因素(PF)的订单偏好数据挖掘技术,并通过评估每个PF与过去信息的相似性/差异,对工厂的准备就绪备选方案进行排名。通过对20个具体工厂的案例研究,说明了新算法的能力;结果表明,该算法生成的非支配解可以帮助工厂经理制定高效的生产计划,这项任务使用现有方法既困难又耗时。在案例研究中,实验室测试完全证实了算法的结果,因此它已经得到了成功的验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Concrete Plants Readiness to Produce High Quality Concrete for Municipal Constructions Using Past Information
The only way to test the ability of concrete plants to produce high quality concrete is to test their final products. Also, the process of testing and controlling concrete quality is time consuming and expensive. In this regard, having a quick, cheap and efficient way to predict the readiness of concrete plants to produce high quality concrete is very valuable. In this paper, a probabilistic multi-attribute algorithm has been developed to address this problem. In this algorithm, the goal is to evaluate readiness of concrete plants to produce high quality concrete based on the error rate of concrete compressive strength. Using past information and data mining techniques, this algorithm predicts the readiness level of concrete plants by similarity of their production factors to past information. Readiness alternatives for plants are ranked using data mining techniques for order preference based on their production factors (PF) and by evaluating the similarity/difference of each PF to past information. A case study of 20 concrete plants is used to illustrate the capability of the new algorithm; with results showing that the algorithm generated nondominated solutions can assist plant managers to set efficient production plan, a task both difficult, cost and time-consuming using current methods. In the case study, lab test totally confirm the algorithm outcomes thus it has been successfully verified.
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来源期刊
CiteScore
1.30
自引率
60.00%
发文量
0
审稿时长
47 weeks
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