Expressway Project Cost Estimation With a Convolutional Neural Network Model

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.

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Modeling the Cost of Political Risk in International Construction Projects

Project Management Journal ◽

10.1177/875697280003100302 ◽

2000 ◽

Vol 31 (3) ◽

pp. 4-13 ◽

Cited By ~ 9

Author(s):

Hashem Al-Tabtabai ◽

Alex P. Alex

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Cost Estimation ◽

Construction Projects ◽

Construction Project ◽

Estimation Method ◽

Political Risk ◽

Project Cost ◽

International Construction

This paper describes a neural network model that can provide assistance in predicting the additional increase in project cost, due to political risk source variables affecting a construction project. The risk factors that affect a construction project are classified as “political source variables” and “project consequence variables.” These source variables are identified and represented in a neural network model. The paper explains how the developed political risk control model can be incorporated directly into a project cost estimation process. The paper concludes with a discussion of the capabilities and limitations of the proposed political risk estimation method, and how it will assist project managers in computing a realistic cost estimate for typical international construction projects under different political conditions.

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Simulation of Mathematical Model to Estimate the Cost of Large-Scale Hydraulic Engineering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.3239 ◽

2014 ◽

Vol 602-605 ◽

pp. 3239-3242

Author(s):

Mao Liu

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Cost Estimation ◽

Large Scale ◽

Rapid Development ◽

Estimation Method ◽

Estimation Model ◽

Project Cost ◽

Water Project

With the rapid development of engineering construction and gradual introduction of the bidding system, project cost estimation model continues to deepen. How to estimate engineering cost fast and accurately become one of the hot topics currently. In this paper, the characteristics of large-scale water project investment risk is combined to establish a neural network model suited for large-scale water project cost, through quantitating the main features of each category of water conservancy and combining neural network model established to quickly estimate water project cost with the toolbox. After engineering examples show that it is a fast and reliable water project cost estimation method.

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KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Jurnal Ilmiah Informatika Komputer ◽

10.35760/ik.2019.v24i3.2364 ◽

2019 ◽

Vol 24 (3) ◽

pp. 220-228

Author(s):

Gusti Alfahmi Anwar ◽

Desti Riminarsih

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Input Layer ◽

Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

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