دانلود مقاله ISI انگلیسی شماره 69414
ترجمه فارسی عنوان مقاله

روش های جدید پیش بینی پیشرفت پویا برای پروژه های ساخت و ساز

عنوان انگلیسی
A novel dynamic progress forecasting approach for construction projects
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
69414 2012 9 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 39, Issue 3, 15 February 2012, Pages 2247–2255

ترجمه کلمات کلیدی
پروژه ساخت و ساز؛ پیش بینی؛ مدل پیش بینی پویا خاکستری - چند جمله ای
کلمات کلیدی انگلیسی
Construction project; Forecast; Grey dynamic prediction model; Modified residual model; Polynomial
پیش نمایش مقاله
پیش نمایش مقاله  روش های جدید پیش بینی پیشرفت پویا برای پروژه های ساخت و ساز

چکیده انگلیسی

In this paper, we propose a novel construction project progress forecasting approach which combines the grey dynamic prediction model and the residual modified model to forecast the current progress during the construction phase. Firstly, four typical S-curves simplified from various sigmoid curves are proposed and fitted to the grey dynamic prediction model. For higher prediction accuracy, three different residual modified models are taken to amend the initial prediction value which was derived from the above step. The mean absolute percentage error (MAPE) and standard deviation of the estimate of Y (SDY) are used to assess the accuracy of the composite results. The better residual modified prediction model is adopted to combine the grey dynamic prediction model to form the novel progress forecasting approach. Then, practical completed construction cases are provided for testing the prediction ability of the proposed progress forecasting approach. Results show that the forecasting approach proposed to forecast construction progress during construction phase is able to get better prediction accuracy almost within 10% whether typical S-curves or practical cases. The new approach relatively provides an accurate, simple and stable method for predicting construction progress in comparison with the previous traditional forecasting methods.