کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
246596 502381 2014 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box–Jenkins time series models
ترجمه فارسی عنوان
پیش بینی هزینه تعمیر و نگهداری تجهیزات ساخت و ساز: مقایسه شبکه عصبی رگرسیون عمومی و مدل سریهای زمانی جعکن جنکینز
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی عمران و سازه
چکیده انگلیسی


• Study on the time series forecasting of construction equipment maintenance cost
• Compares Box–Jenkins forecasting models and GRNN models
• Compares forecasting performance on equipment groups and equipment fleets
• Incorporation of fuel consumption in the maintenance forecasting model

This paper presents a comparative study on the applications of general regression neural network (GRNN) models and conventional Box–Jenkins time series models to predict the maintenance cost of construction equipment. The comparison is based on the generic time series analysis assumption that time-sequenced observations have serial correlations within the time series and cross correlations with the explanatory time series. Both GRNN and Box–Jenkins time series models can describe the behavior and predict the maintenance costs of different equipment categories and fleets with an acceptable level of accuracy. Forecasting with multivariate GRNN models was improved significantly after incorporating parallel fuel consumption data as an explanatory time series. An accurate forecasting of equipment maintenance cost into the future can facilitate decision support tasks such as equipment budget and resource planning, equipment replacement, and determining the internal rate of charge on equipment use.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automation in Construction - Volume 38, March 2014, Pages 30–38
نویسندگان
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