Construction Engineering Cost Evaluation Model and Application Based on RS-IPSO-BP Neural Network

نویسندگان

  • Yuan Hong
  • Haibo Liao
  • Yazhi Jiang
چکیده

Aimed at coping with the complexity of construction engineering cost evaluation, the advantages of rough set theory, particle swarm algorithm and BP neural network are integrated to put forward a new model of construction engineering cost evaluation, namely, the model of construction engineering cost evaluation of optimized particle swarm and BP neural network on the basis of rough set theory. First, rough set theory was used to reduce the factors affecting construction engineering cost and optimize input variables of BP neural network. Then, the improved particle swarm algorithm with constriction factors is adopted to optimize the initial weights and thresholds. Through this method, BP neural network can be used in a better way to solve nonlinear problems and to improve the rate of convergence and the ability to search global optimum. An engineering project in a city of Hunan is selected to make empirical analysis. It shows that based on the features of engineering, this new model enjoys a high practical value as it can be applied to make scientific evaluation of costs of construction engineering.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Neural Network Model Based on Support Vector Machine for Conceptual Cost Estimation in Construction Projects

Estimation of the conceptual costs in construction projects can be regarded as an important issue in feasibility studies. This estimation has a major impact on the success of construction projects. Indeed, this estimation supports the required information that can be employed in cost management and budgeting of these projects. The purpose of this paper is to introduce an intelligent model to im...

متن کامل

Application of Neural Network in the Cost Estimation of Highway Engineering

Based on the BP neural network, this paper sets up the model of cost estimation of highway engineering. The BP neural network model is trained by a sample data obtained from some performed typical engineering to come true quick cost-estimating.It is sure that the method is practical and the estimating results are reliable according to lots of examples.It shows the promising perspective of BP Ne...

متن کامل

Correlation between IP and Rs and grade data in modeling and evaluation of a copper deposit, case study: the Sarbisheh copper deposit, Iran

This paper addresses the application of integrated chargeability and resistivity method and grade data in modeling and evaluation of copper deposits. We argue that the relationship between IP, Rs and grade data may be used for modeling and reserve estimation and tested this argument for Sarbisheh copper deposit that is located in eastern Iran. Geology and mineralization situation of Sarbisheh d...

متن کامل

Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven metho...

متن کامل

Cost Effective Heat Exchanger Network Design with Mixed Materials of Construction

This paper presents a simple methodology for cost estimation of a near optimal heat exchanger network, which comprises mixed materials of construction. Intraditional pinch technology and mathematical programming it is usually assumed that all heat exchangers in a network obey a single cost model. This implies that all heat exchangers  in a network are of the same type and use the same mate...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • JCP

دوره 9  شماره 

صفحات  -

تاریخ انتشار 2014