scholarly journals Application of Project Whole Process Cost Control in Construction Project Cost Audit

2021 ◽  
pp. 240-246
Author(s):  
Weiying Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Construction Project ◽  
Construction Cost ◽  
Grey Theory ◽  
Project Cost ◽  
Neural Network Method ◽  
Network Method ◽  
Construction Project Cost

This paper summarizes the theory of project cost, which paves the way for the basic theoretical system of construction project cost estimation. This paper expounds the function of project cost and the main factors affecting project cost. Secondly, the basic principles of BP neural network method and grey theory are described, which provides technical support for the establishment of construction cost estimation system model. In view of the shortcomings of BP neural network, such as slow convergence speed, easy to fall into local minimum and inaccurate prediction, this paper proposes an improved method to process the data of BP neural network input layer with grey one-time accumulation, and then use grey one-time subtraction to process the output layer. Finally, the optimization model based on grey BP neural network method is established to establish a more accurate knowledge framework system in order to solve the construction cost estimation.

scholarly journals An Algorithm for Construction Project Cost Forecast Based on Particle Swarm Optimization-Guided BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Dan Ye
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Construction Project ◽  
Particle Swarm ◽  
Construction Cost ◽  
Cost Prediction ◽  
Swarm Optimization ◽  
Project Cost ◽  
Construction Project Cost

Construction project cost prediction is an important function in construction-related fields; it can provide an important basis for project feasibility study and design scheme comparison and selection, and its accuracy will directly affect the investment decision of the project. The successful realization of construction cost prediction can bring great convenience to the control and management of construction cost. The purpose of this paper is to study a fast, accurate, convenient, deducible, and rational construction project cost prediction method, to provide a basis for the cost management of the whole life cycle of the project. Therefore, this paper uses particle swarm optimization algorithm to improve BP neural network and proposes a novel construction project cost prediction algorithm based on particle swarm optimization-guided BP neural network. Aiming at the defects of BP neural network updating weights and thresholds with the gradient descent method, this paper uses the advantages of particle swarm optimization in the field of parameter optimization to optimize BP neural network with PSO algorithm. The structure of BP neural network weights and the threshold of each neuron in the coding, through intelligent search for each particle, find the most suitable weights and thresholds, so that the BP neural network has faster convergence speed, better generalization ability, and higher prediction precision. Simulation results also show that the proposed algorithm is competitive enough.

scholarly journals Research on Construction Cost Estimation based on Artificial Intelligence Technology

2021 ◽  
Vol 22 (2) ◽  
pp. 93-104
Author(s):  
Bin Wang ◽  
Jianjun Yuan ◽  
Kayhan Zrar Ghafoor
Keyword(s):  
Neural Network ◽  
System Theory ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Estimation Model ◽  
Project Cost ◽  
Construction Project Cost ◽  
Bp Neural Network Model

For the prediction of economic expenses involved in construction industry, cost estimation has become an important aspect of construction management for the prediction of economic expenses and successful completion of the construction work. Cost analysis is crucial and require expertise for accurate and comprehensive estimation. In order to effectively improve the accuracy of construction project cost, this paper establishes an estimation model based on gray BP neural network. It combines the MATLAB toolbox for program design, and learns and tests the input and output of training samples. This article determines the application of grey system theory to optimize the estimation model of Back Propagation (BP) neural network. The viability of the method established in this article, is tested by collecting the engineering cost data in Zhengzhou city and comparing between the standard BP neural network and the gray BP neural network methods. The results show that the average error of the gray system theory optimized BP neural network model designed in this paper is 2.33%. The gray BP neural network model studied in this paper can not only quickly estimate the project cost, but also has high accuracy rate. The outcomes obtained establishes a model with scientific and reasonable construction project cost estimation.

Nursing Scheme Based on Back Propagation Neural Network and Probabilistic Neural Network in Chronic Kidney Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 416-421 ◽  
Author(s):  
Xuxia Ying ◽  
Bibo Tang ◽  
Canxin Zhou
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Bp Neural Network ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Model Errors ◽  
Neural Network Method ◽  
Network Method ◽  
Case Data

Objective: The purpose of graded care for chronic kidney disease is to share expert experience, so that doctors can more accurately diagnose chronic kidney disease, so that patients with chronic kidney disease can understand their condition in time and collect case data. The collected case data is established into a data warehouse, the data quality is evaluated, and the BP neural network method is used for data mining to analyze the data. Methods: The paper studied BP neural network and probabilistic neural network (PNN), and used 75% of the samples to compare the models. The model errors were analyzed including maximum, minimum, expectation, variance and running time to get Adaboost. The accuracy and robustness of the -PNN model and the IGABP model are good. Results: BP neural network model and probabilistic neural network method can achieve higher application of graded care for chronic kidney disease. The method is capable of quickly predicting disease grading and providing a standardized treatment care regimen. The method realizes the main functions of querying, managing, and collecting data of medical records. Conclusion: The external expansion function of BP neural network and probabilistic neural network can achieve accurate data analysis, which can effectively improve the diagnosis time and grade prediction accuracy of chronic kidney disease, and provide opinions for graded nursing.

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