scholarly journals Development of Cost Estimation Models Based on ANN Ensembles and the SVM Method

2020 ◽  
Vol 30 (3) ◽  
pp. 48-67
Author(s):  
Michał Juszczyk
Keyword(s):  
Support Vector Machines ◽  
Cost Estimation ◽  
Construction Projects ◽  
Predictive Performance ◽  
Design Stage ◽  
Support Vector ◽  
Early Design Stage ◽  
Vector Machines ◽  
Structural Frames ◽  
The Cost

Abstract Cost estimation, as one of the key processes in construction projects, provides the basis for a number of project-related decisions. This paper presents some results of studies on the application of artificial intelligence and machine learning in cost estimation. The research developed three original models based either on ensembles of neural networks or on support vector machines for the cost prediction of the floor structural frames of buildings. According to the criteria of general metrics (RMSE, MAPE), the three models demonstrate similar predictive performance. MAPE values computed for the training and testing of the three developed models range between 5% and 6%. The accuracy of cost predictions given by the three developed models is acceptable for the cost estimates of the floor structural frames of buildings in the early design stage of the construction project. Analysis of error distribution revealed a degree of superiority for the model based on support vector machines.

scholarly journals Implementation of MCA Method for Identification of Factors for Conceptual Cost Estimation of Residential Buildings

2013 ◽  
Vol 8 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Michał Juszczyk ◽  
Agnieszka Leśniak ◽  
Krzysztof Zima
Keyword(s):  
Comparative Analysis ◽  
Cost Estimation ◽  
Construction Projects ◽  
Residential Buildings ◽  
Residential Building ◽  
Design Stage ◽  
Problem Solution ◽  
Key Factors ◽  
Early Design Stage ◽  
Select Group

Abstract Conceptual cost estimation is important for construction projects. Either underestimation or overestimation of building raising cost may lead to failure of a project. In the paper authors present application of a multicriteria comparative analysis (MCA) in order to select factors influencing residential building raising cost. The aim of the analysis is to indicate key factors useful in conceptual cost estimation in the early design stage. Key factors are being investigated on basis of the elementary information about the function, form and structure of the building, and primary assumptions of technological and organizational solutions applied in construction process. The mentioned factors are considered as variables of the model which aim is to make possible conceptual cost estimation fast and with satisfying accuracy. The whole analysis included three steps: preliminary research, choice of a set of potential variables and reduction of this set to select the final set of variables. Multicriteria comparative analysis is applied in problem solution. Performed analysis allowed to select group of factors, defined well enough at the conceptual stage of the design process, to be used as a describing variables of the model.

scholarly journals Forecasting Electric Load by Support Vector Machines with Genetic Algorithms

2005 ◽  
Vol 9 (2) ◽  
pp. 134-141 ◽  
Author(s):  
Ping-Feng Pai ◽  
◽  
Wei-Chiang Hong ◽  
Chih-Shen Lin ◽  
◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Machines ◽  
Moving Average ◽  
Predictive Performance ◽  
Support Vector ◽  
Electric Load ◽  
Power Company ◽  
Svm Model ◽  
Vector Machines ◽  
General Regression Neural Networks

Support vector machines (SVMs) have been successfully used in solving nonlinear regression and time series problems. However, the application of SVMs to load forecasting is very rare. Therefore, the purpose of this paper is to examine the feasibility of SVMs in forecasting electric load. In addition, the genetic algorithms are applied in the parameter selection of SVM model. Forecasting results compared with other two models, namely autoregressive integrated moving average (ARIMA) and general regression neural networks (GRNN), are provided. The experimental data are borrowed from the Taiwan Power Company. The numerical results indicate that the SVM model with genetic algorithms (SVMG) results in better predictive performance than the other two approaches.

scholarly journals Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Igor Peško ◽  
Vladimir Mučenski ◽  
Miloš Šešlija ◽  
Nebojša Radović ◽  
Aleksandra Vujkov ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Construction Projects ◽  
Percentage Error ◽  
Support Vector ◽  
Building Process ◽  
Vector Machines ◽  
Specific Part

Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE) of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.

scholarly journals Early Cost Estimation in Customized Furniture Manufacturing Using Machine Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 28-33
Author(s):  
O. Kurasova ◽  
◽  
V. Marcinkevičius ◽  
V. Medvedev ◽  
B. Mikulskienė
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Design Stage ◽  
Early Design Stage ◽  
Furniture Manufacturing ◽  
Learning Techniques ◽  
Key Factor ◽  
The Cost

Accurate cost estimation at the early stage of a construction project is a key factor in the success of most projects. Many difficulties arise when estimating the cost during the early design stage in customized furniture manufacturing. It is important to estimate the product cost in the earlier manufacturing phase. The cost estimation is related to the prediction of the cost, which commonly includes calculation of the materials, labor, sales, overhead, and other costs. Historical data of the previously manufactured products can be used in the cost estimation process of the new products. In this paper, we propose an early cost estimation approach, which is based on machine learning techniques. The experimental investigation based on the real customized furniture manufacturing data is performed, results are presented, and insights are given.

scholarly journals HEARTBEAT CLASSIFICATION USING SUPPORT VECTOR MACHINES (SVMs) WITH AN EMBEDDED REJECT OPTION

2012 ◽  
Vol 26 (01) ◽  
pp. 1250001 ◽  
Author(s):  
ZAHIA ZIDELMAL ◽  
AHMED AMIROU ◽  
ADEL BELOUCHRANI
Keyword(s):  
Support Vector Machines ◽  
Performance Enhancement ◽  
Support Vector ◽  
Heartbeat Classification ◽  
Medical Diagnoses ◽  
Hinge Loss ◽  
Beat Classification ◽  
Vector Machines ◽  
Significant Performance ◽  
The Cost

In this paper, we introduce a new system for ECG beat classification using support vector machines classifier with a double hinge loss. The proposed classifier rejects samples that cannot be classified with enough confidence. Specifically in medical diagnoses, the consequence of a wrong classification can be so harmful that it is convenient to reject such sample. After ECG preprocessing, feature selection and extraction, our decision rule uses dynamic reject thresholds according to the cost of rejecting or misclassifying a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrythmia database. The achieved results are represented by the error reject tradeoff. We obtained 98.2% of sensitivity with no rejection and more than 99% of sensitivity for the optimal classification cost being competitive to other published studies.

An integrated BIM-based approach for cost estimation in construction projects

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdulwahed Fazeli ◽  
Mohammad Saleh Dashti ◽  
Farzad Jalaei ◽  
Mostafa Khanzadi
Keyword(s):  
Cost Estimation ◽  
Construction Projects ◽  
Design Methodology ◽  
Automated System ◽  
Design Stage ◽  
Complex Structures ◽  
Human Errors ◽  
Content Type ◽  
The Cost ◽  
Practical Implications

PurposeAnalyzing different scenarios at the design stage of construction projects has always been a challenging task. One of the main parameters that helps owners in making better decisions in designing their buildings is to look after the cost perspective on different design scenarios. Thus, this study aims to propose a semi-automated BIM-based cost estimation approach that enables practitioners to estimate the cost of projects based on different design scenarios by an accurate and agile system.Design/methodology/approachThis study proposes an integrated framework, through which the cost estimation standard of Iran (FehrestBaha) is linked to the materials quantity take-offs (QTO) from BIM models. The performance of the system is based on connecting the classification standards of UniFormat and MasterFormat to the cost estimation standard of FehrestBaha. A BIM-based extension in the Revit environment is developed to automate the cost estimation process.FindingsTo evaluate the efficiency of the proposed approach in cost estimation, it is implemented to estimate the cost of the architectural discipline in a real construction project. The results indicate that the proposed BIM-based approach estimated the cost of the architectural discipline with an acceptable level of accuracy.Practical implicationsThe proposed approach could be used by practitioners to have an agile and accurate BIM-based cost estimation of different scenarios during design process. The semi-automated system considerably reduces the time of cost estimation in comparison to the traditional manual approaches, particularly in complex structures. Owners are able to easily trace changes in project cost according to any changes in components and materials of the BIM model. Furthermore, the proposed approach provides a practical roadmap for BIM-based cost estimation based on cost estimation standards in different countries.Originality/valueUnlike the traditional manual cost estimation approaches, the proposed BIM-based approach is not highly dependent on the knowledge of experienced estimators, which therefore facilitates its implementation. Furthermore, automating both QTO process and the required calculations in this approach increases the accuracy of cost estimation while decreasing the probability of human errors or omission occurrence.

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