Cost assessment of construction projects through neural networks

GulcicekUmit,; OzkanOmer,; Gunduz, Murat; Hakki, DemirIsmail

doi:10.1139/cjce-2012-0442

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…Each neuron is connected by a linear combination and a nonlinear function. Since the linear combination has the same structure as the adaptive filter, the least squares algorithm is applicable to update the weight vector [18]. e specific process is as follows.…”

Section: Improved Neural Network Algorithmmentioning

confidence: 99%

Research on Economic Mathematical Analysis and Construction Model of Prefabricated Building Structure Based on Improved Neural Network Algorithm

Lin

2021

Mathematical Problems in Engineering

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In this article, a mathematical analysis model of economics of prefabricated building structure based on improved neural network algorithm is proposed in order to solve the low analysis accuracy in traditional methods. Firstly, by means of analyzing the costs of materials, labor, and equipment, the economic characteristics of the cost of fabricated building structures are determined. Secondly, the single neuron is analyzed and the weight coefficient is adjusted in accordance with the multilayer neural network structure, so as to minimize the construction error of the economic analysis model of the assembled building structure. Meanwhile, the weight vector is obtained, error-weighted square sum is calculated through choosing an adaptive filter and obtained, and the weight vector is updated by the least squares algorithm. Thirdly, the neural network algorithm training and learning process is designed and improved, the dependent variable is selected, the number of input points is determined, and then, the training and learning process of the improved neural network algorithm is completed. Finally, a fitness function is set to measure the authenticity of dataset, which is further defined as a combination of different weights to construct an economic mathematical analysis model. The experimental results indicate that the analysis results of this method can reach an accuracy up to 96%, so it has a broader application prospect in low-rise buildings.

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Section: Improved Neural Network Algorithmmentioning

confidence: 99%

Research on Economic Mathematical Analysis and Construction Model of Prefabricated Building Structure Based on Improved Neural Network Algorithm

Lin

2021

Mathematical Problems in Engineering

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“…In addition, mathematical operations were used on the matrices to produce a final estimation for energy and CO 2 emissions per hour of material hauled by an excavator. In order to produce an accurate estimation formula based on the ANN model, the data used in the model should first be preprocessed (i.e., through normalizing and scaling) to modify the training environment of the neural network [60,[64][65][66]. The input and output data were scaled within the range 0.1 to 0.9 in order to avoid the problem of a slow learning rate at the edges of the data boundaries, and to ensure precision of the output range based on the quality of the sigmoid function in the backward propagation learning algorithms in relation to the default scaled data between 0.0 to 1.0 [60].…”

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Jassim

Olofsson

2017

Sustainability

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Excavators are one of the most energy-intensive elements of earthwork operations. Predicting the energy consumption and CO 2 emissions of excavators is therefore critical in order to mitigate the environmental impact of earthwork operations. However, there is a lack of method for estimating such energy consumption and CO 2 emissions, especially during the early planning stages of these activities. This research proposes a model using an artificial neural network (ANN) to predict an excavator's hourly energy consumption and CO 2 emissions under different site conditions. The proposed ANN model includes five input parameters: digging depth, cycle time, bucket payload, engine horsepower, and load factor. The Caterpillar handbook's data, that included operational characteristics of twenty-five models of excavators, were used to develop the training and testing sets for the ANN model. The proposed ANN models were also designed to identify which factors from all the input parameters have the greatest impact on energy and emissions, based on partitioning weight analysis. The results showed that the proposed ANN models can provide an accurate estimating tool for the early planning stage to predict the energy consumption and CO 2 emissions of excavators. Analyses have revealed that, within all the input parameters, cycle time has the greatest impact on energy consumption and CO 2 emissions. The findings from the research enable the control of crucial factors which significantly impact on energy consumption and CO 2 emissions.

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“…With the development of artificial intelligence, machine intelligent algorithms, such as neural network model, support vector machine model, and gray prediction model, have gradually been used in project cost prediction. Gulcicek et al predicted the construction cost of reinforced concrete housing estate buildings by using artificial neural network and multiple regression model and investigated the relationship between cost and influencing factors like earthquake region, soil type, floor area, and the number of stories [6]. Cheng et al proposed an artificial intelligence approach, the evolutionary fuzzy hybrid neural network (EFHNN), to forecast the estimates of overall and category costs for actual construction projects [7].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Prediction of the Construction Cost of Substation Projects Using Support Vector Machine Optimized by Particle Swarm Optimization

Lin

Tao

Zhang

et al. 2019

Mathematical Problems in Engineering

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To establish and consummate the electric power network, the construction and investment scale of power substation projects is expanding every year. As a capital-technology-intensive project, it has high requirements for power substation project management. Accurate cost forecasting can help to reduce the project cost, improve the investment efficiency, and optimize project management. However, affected by many factors, the construction cost of a power substation project usually presents strong nonlinearity and uncertainty, which make it difficult to accurately forecast the cost. This paper presents a new hybrid substation project cost forecasting method called PCA-PSO-SVM model, which is a support vector machine (SVM) model optimized by a particle swarm optimization (PSO) algorithm with principal component analysis (PCA). In this intelligent prediction model, the PCA method is introduced to reduce the data dimension. Furthermore, the PSO algorithm is used to optimize the model parameters. In the example, 65 sets of substation construction data are input into PCA-PSO-SVM model for construction cost prediction, and the prediction results are compared with other prediction methods to verify the forecasting accuracy. The results show that the MAPE and RMSE of the PCA-PSO-SVM model is 6.21% and 3.62, respectively. And, the prediction accuracy of this model is better than that of other models, which can provide a reliable basis for investment decision-making of substation projects.

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Cost assessment of construction projects through neural networks

Cited by 9 publications

References 21 publications

Research on Economic Mathematical Analysis and Construction Model of Prefabricated Building Structure Based on Improved Neural Network Algorithm

Research on Economic Mathematical Analysis and Construction Model of Prefabricated Building Structure Based on Improved Neural Network Algorithm

Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Intelligent Prediction of the Construction Cost of Substation Projects Using Support Vector Machine Optimized by Particle Swarm Optimization

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