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

Peško, Igor; Mučenski, Vladimir; Šešlija, Miloš; Radović, Nebojša; Vujkov, Aleksandra; Bibić, Dragana; Krklješ, Milena

doi:10.1155/2017/2450370

Cited by 50 publications

(49 citation statements)

References 51 publications

(54 reference statements)

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“…Third, based on the case studies applied to local office buildings, the accuracy and validity of the developed model was verified by comparison analysis between the actual construction duration and the predicted construction duration calculated from an approximate construction duration prediction model. Generally, the construction duration for office buildings are determined in two ways: the owner defines the project duration directly (Jin et al 2016;Bayram 2017;Peško et al 2017) or the contractor suggests construction duration to the owner. On the other hand, the construction duration computation model utilized by contractors during the project planning stage (Table 1) is not a specialized standard for general office buildings Koo et al 2010;Jin et al 2016;Kim et al 2016;Thomas, N., Thomas, A. V. 2016).…”

Section: Scope and Methods Of The Studymentioning

confidence: 99%

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Yeom

Seo

Kim

et al. 2018

Journal of Civil Engineering and Management

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Abstract. Accurate prediction of the construction duration is imperative to the reliable cash flow analysis during the project planning phase when feasibility analysis is carried out. However, lack of information and frequent changes that occur as a result of a negotiation process between the owner and the designer in defining the project scope make it difficult to compute real-time construction duration. Domestic and foreign models for calculating the construction durations cannot be readily applied to computation of construction duration for general office buildings in Korea specifically during the project planning phase as there is a limit in its applicability due to numerous restrictions. Moreover, there are no preceding studies suggesting different computational approaches to predict the entire construction duration for office buildings with the approximate construction duration concept during planning phase. Therefore, based on the collected performance data, this study proposes a multiple linear regression model that facilitates reliable prediction of approximate construction duration for office buildings in the project planning phase. The model will allow the owner and other stakeholders to predict the real-time construction duration using the basic information on office buildings and to assess the construction durations incorporating frequent changes during the project planning phase.

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Section: Scope and Methods Of The Studymentioning

confidence: 99%

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Yeom

Seo

Kim

et al. 2018

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

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“…Despite the milestone achieved in this study, the regression model does not consider the mean square error associated with forecasting, which is solved in our proposed model. Peško et al proposed an artificial intelligence system for the approximation of duration and cost of a construction project [4]. The balance between the planned costs, overheads and direct cost considerably affect the prospected profits there; it is essential to prepare a consice offer for the available resources and the required time [4].…”

Section: Related Workmentioning

confidence: 99%

“…Studies have shown a profound discrepancy between the prospected duration and cost of a project and the real cost and duration, i.e., defines the actual project completion parameters [4]. The figures in these researches have demonstrated that it is vital for construction companies to create internal databases to be used as benchmarks for future projects.…”

Section: Introductionmentioning

confidence: 99%

Guess the time of implementation of residential construction projects using neural networks ANN

AL-Zubaidi

Yas

Abbas³

2019

PEN

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The construction duration of residential projects, especially in building processes, significantly impact the business of a construction company. The balance between the planned cost, direct cost, and overheads directly depend on the precision of the implementation phase of the project. The application of the artificial neural network (ANN) to predict the duration of implementation of a residential construction project from the pre-design stage to completion is comprehensively discussed in this research. The study applies the backpropagation (BP) network made of nodes for error evaluation of the training states. Further, the proposed system illustrated that the artificial neural network (ANN) satisfy the three crucial criteria (cost, quality, and time) used for the evaluation of projects. The ANN provided accurate data for the training and estimation of, the duration of a residential construction project with adequate resources of implementation. The performance of the results for the ANN at 105 iteration shows that the prediction was 99.841% accurate for the overall system. The best fit occurred at the 99th epoch with an MSE of 0.10286.

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“…In medicine, there is a study where ANNs where used to identify patients with hard risk for dying after suffering an acute myocardial infarction [35]. Furthermore, Pesko et al present a comparative study between the use of ANNs and support vector machines, which are used to estimate costs and duration time of the construction of urban roads [36].…”

Section: Complexitymentioning

confidence: 99%

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

et al. 2018

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The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers' artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit ( = 1.0000) against the response surface methodology ( = 0.9996). Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

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

Cited by 50 publications

References 51 publications

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Guess the time of implementation of residential construction projects using neural networks ANN

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

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