A Neuro-Adaptive Learning (NAL) Approach about Costs of Residential Buildings

Uğur, Latif Onur

doi:10.12693/aphyspola.132.585

Cited by 8 publications

(3 citation statements)

References 24 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study conducted a rigorous comparative analysis between predicted values and actual cost outcomes, meticulously scrutinizing prediction errors. This research facilitated an economic evaluation of installation projects and significantly mitigated the margin of error in project cost estimation.UGUR L.O [8] pursued the development of a predictive model for residential cost based on adaptive neural networks. The study involved an in-depth analysis of the disparities between predicted values and actual costs.…”

Section: Introductionmentioning

confidence: 99%

BP neural network construction cost prediction model based on different optimization algorithms

Liu,

Yang,

et al. 2024

Preprint

View full text Add to dashboard Cite

Aiming at the complexity of prefabricated construction project input cost estimation and the subjectivity and limitation of manual estimation, a method of cost prediction based on BP neural network is proposed. In this paper, 144 sets of assembled engineering data are selected and collected. By using the mapping relationship between construction cost and 11 cost impact indicators, the prediction results are compared and analyzed after training and prediction. The results show that BP neural network can effectively predict cost, but the convergence is slow and the fitting effect is not good. In this paper, three algorithms, Adam, SGD and Adadelta, are proposed to optimize the BP neural network respectively, and the prediction results are compared and analyzed. The maximum error of SGD-BP neural network prediction model is 6.56% and the minimum error is 0.91%, the maximum error of Adadelta-BP neural network prediction model is 17.10% and the minimum error is 0.82%, the Adam-BP neural network prediction model has a maximum error of 3.89% and a minimum error of 0.11%. From the error analysis, it can be seen that the prediction results of Adam-BP neural network prediction model are more accurate, with good generalization effect, providing more accurate assembly building construction cost reference data for decision makers to judge the feasibility of the project.

show abstract

Section: Introductionmentioning

confidence: 99%

BP neural network construction cost prediction model based on different optimization algorithms

Liu,

Yang,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This index is a North American cost indicator published monthly by Engineering News-Record (ENR). Ugur (2017) used neural models to predict costs of public undertakings and evaluated their training and test results using the R 2 metric, which, in the case of training, was 0,97 and in the test was 0,89.…”

Section: Introductionmentioning

confidence: 99%

Cost Forecasting of Public Construction Projects Using Multilayer Perceptron Artificial Neural Networks: A Case Study

Sousa

Maués

et al. 2021

Ing. Inv.

View full text Add to dashboard Cite

The execution of public sector construction projects often requires the use of financial resources not foreseen during the tendering phase, which causes management problems. This study aims to present a computational model based on artificial intelligence, specifically on artificial neural networks, capable of forecasting the execution cost of construction projects for Brazilian educational public buildings. The database used in the training and testing of the neural model was obtained from the online system of the Ministry of Education. The neural network used was a multilayer perceptron as a backpropagation algorithm optimized through the gradient descent method. To evaluate the obtained results, the mean absolute percentage errors and the Pearson correlation coefficients were calculated. Some hypothesis tests were also carried out in order to verify the existence of significant differences between real values and those obtained by the neural network. The average percentage errors between predicted and actual values varied between 5% and 9%, and the correlation values reached 0,99. The results demonstrated that it is possible to use artificial intelligence as an auxiliary mechanism to plan construction projects, especially in the public sector.

show abstract

“…ANFIS has been used in various fields of engineering. For instance, Ugur (2017) used ANFIS to estimate the costs of the residential building. While Fragiadakis et al (2014) used ANFIS to assess the occupational risk in the shipbuilding industry; Ebrat and Ghodsi (2014) applied ANFIS to evaluate the risk in construction projects; Li et al (2011) forecasted building energy consumption using hybrid ANFIS; GüNeri et al…”

Section: Introductionmentioning

confidence: 99%

Predicting the impact of size of uncertainty events on the construction cost of highway projects using ANFIS

Moghayedi¹,

Windapo²

2019

Proceedings of the 2019 European Conference on Computing in Construction

View full text Add to dashboard Cite

This study examines the use of Adaptive Neuro-Fuzzy Inference System (ANFIS) as a machine learning technique in the prediction of the impact size of uncertainty events on construction cost of highway projects and whether this technique is more accurate than the classical statistical methods. The rationale for the study stems from the availability of several techniques such as regression analysis and machine learning for developing predictive models of relationships of various variables in the construction industry. However, there has been limited research undertaken to compare the accuracy of the available techniques. The success or failure of prediction depends on the credibility of the prediction method. In this study, the predicted impact size of 76 uncertain events on the construction of highway projects using ANFIS as an intelligence machine learning method and Stepwise Regression Analysis (SRA) as a classical statistical method were compared to delineate the ability and accuracy of the ANFIS prediction technique. The comparison of calculated R-Value and two error tests for ANFIS and SRA show that the constructed ANFIS model has a higher performance than the SRA method in both fitness and reliability of the prediction model. Also, the performance comparison showed that ANFIS is a good tool for predicting the impact of uncertainty events on construction project cost. Based on these findings, the study concludes that the use of intelligent methods such as ANFIS will minimise the potential inconsistency of correlations in construction cost and time prediction. The model developed enables cost engineers to estimate the construction cost with a higher degree of accuracy.

show abstract

A Neuro-Adaptive Learning (NAL) Approach about Costs of Residential Buildings

Cited by 8 publications

References 24 publications

BP neural network construction cost prediction model based on different optimization algorithms

BP neural network construction cost prediction model based on different optimization algorithms

Cost Forecasting of Public Construction Projects Using Multilayer Perceptron Artificial Neural Networks: A Case Study

Predicting the impact of size of uncertainty events on the construction cost of highway projects using ANFIS

Contact Info

Product

Resources

About