Forecasting of Sports Fields Construction Costs Aided by Ensembles of Neural Networks

Juszczyk, Michał; Zima, Krzysztof; Lelek, Wojciech

doi:10.3846/jcem.2019.10534

Cited by 32 publications

(20 citation statements)

References 37 publications

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“…For construction cost estimation, ANN is a representative method for early construction cost estimation by identifying cost influencing factors and establishing a prediction model based on historical data [47]. Juszczyk, Zima, and Lelek [12] presented an original approach of building construction cost predictive models based on ensembles of some MLPNNs. Rafiei and Adeli [66] used advanced machine learning concepts to create innovative construction cost estimation models, including an unsupervised deep Boltzmann machine learning approach and a soft-max layer three-layer BPNN.…”

Section: Application Fields and Hot Topics On Ann In CMmentioning

confidence: 99%

“…ANN can play roles in the prediction, optimization, classification, and decision-making in the practice of CM and has been used in CM since the early 1990s [11]. For instance, Juszczyk et al [12] proposed a predictive model for fast cost analyses and conceptual estimates in the planning stage. The crack detection method of wavelet neural network was proposed by Turkan et al [13] to minimize the possibility of facility failure.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Neural Networks in Construction Management: Current Status and Future Directions

et al. 2021

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Artificial neural networks (ANN) exhibit excellent performance in complex problems and have been increasingly applied in the research field of construction management (CM) over the last few decades. However, few papers draw up a systematic review to evaluate the state-of-the-art research on ANN in CM. In this paper, content analysis is performed to comprehensively analyze 112 related bibliographic records retrieved from seven selected top journals published between 2000 and 2020. The results indicate that the applications of ANN of interest in CM research have been significantly increasing since 2015. Back-propagation was the most widely used algorithm in training ANN. Integrated ANN with fuzzy logic/genetic algorithm was the most commonly employed way of addressing the CM problem. In addition, 11 application fields and 31 research topics were identified, with the primary research interests focusing on cost, performance, and safety. Lastly, challenges and future directions for ANN in CM were put forward from four main areas of input data, modeling, application fields, and emerging technologies. This paper provides a comprehensive understanding of the application of ANN in CM research and useful reference for the future.

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Section: Application Fields and Hot Topics On Ann In CMmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks in Construction Management: Current Status and Future Directions

et al. 2021

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“…In this study, a new prediction model of critical strain energy release rate was established according to Tables 10, 13, and 14, which is shown in Equation (8). Based on these real-time detection data, the construction quality for the lowtemperature performance can be evaluated by this prediction model [43].…”

Section: Prediction Model Of Critical Strain Energy Releasementioning

confidence: 99%

Determination and Monitoring of Key Construction Control Indices for Low‐Temperature Performance of Asphalt Mixtures Based on BIM Platform

Zhao

Gao

Zhang

et al. 2021

Advances in Civil Engineering

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The construction control indices were commonly used to ensure the quality of asphalt layers in the construction process. However, the relationship between the construction control indices and the low-temperature performance of asphalt layers is not clear. The purpose of this paper was to investigate the effect of the variations of the construction control indices on the low-temperature performance of asphalt mixtures to determine the key construction control indices, and propose the method for the monitoring of these indices using the Building Information Modeling (BIM) platform. The low-temperature performance of asphalt mixtures was evaluated by the semicircular bend (SCB) test. A new prediction model of critical strain energy release rate was established to evaluate the low-temperature performance of the asphalt layer. Five factors are considered for the low-temperature performance, which are the gradation and asphalt-aggregate ratio in the asphalt mixture plant, rolling temperature, gradation segregation, and temperature segregation. Orthogonal test (OT) results indicated that the order of importance of factors affecting the low-temperature performance is asphalt-stone ratio, gradation, and molding temperature. The influences of gradation segregation and temperature segregation on the low-temperature performance were quantified in this study. Furthermore, the construction control indices were monitored by the BIM platform developed in this research. In the construction process of the asphalt layer, the gradation variation caused by the segregation should be paid more attention to ensure the low-temperature performance of the pavement.

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“…Afterwards, in order to increase the accuracy of the final estimate of the number of used scaffoldings, the so-called network set, which consists of five previously developed and selected models, was used. According to [27,39], the use of such a set contributes to the reduction of the prediction error. Figure 6 presents a comparison of empirical values and the results obtained with the help of the developed set of networks.…”

Section: Networkmentioning

confidence: 99%

Calculation of the Population of Construction Scaffoldings Using Neural Networks

Nowobilski¹

2021

Applied Sciences

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Building scaffoldings are temporary structures that are commonly used in the construction industry. A precise determination of the number of building scaffoldings in use is a very complex task. The literature survey showed that there is a lack of scientific studies concerning the estimation of the scaffolding population in the construction industry. This observation gave rise to the need to undertake such research, the aim of which was to develop a model of a neural network set which would in turn enable the number of used building scaffoldings to be estimated. In order to carry out such a research task, an original research methodology was developed, which used the results of empirical research that involved the counting of construction scaffoldings used in selected representative areas of the studied regions of Poland (the research was carried out in the period from 2016 to 2018), and also data taken from a publication of the Central Statistical Office on socio-economic indicators that characterize the analyzed regions (data from 2010 to 2018). The main element of the developed methodology is a set of five MLP neural networks, which was used to predict the number of used construction scaffoldings. The analysis of the sensitivity of the quantitative and qualitative variables of the model showed that they have a significant impact on the final result generated by the networks. The obtained results of the research and analyses showed the size of the population of building scaffoldings used in individual regions of Poland, and also the seasonality of their occurrence. The knowledge obtained on this basis can be used, among others, in economic analyses related to the use of construction scaffolding, as well as in the process of managing occupational safety on scaffoldings. The most important scientific aspect of the article concerns the development of an original methodology for estimating the population of building scaffoldings.

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Forecasting of Sports Fields Construction Costs Aided by Ensembles of Neural Networks

Cited by 32 publications

References 37 publications

Application of Artificial Neural Networks in Construction Management: Current Status and Future Directions

Application of Artificial Neural Networks in Construction Management: Current Status and Future Directions

Determination and Monitoring of Key Construction Control Indices for Low‐Temperature Performance of Asphalt Mixtures Based on BIM Platform

Calculation of the Population of Construction Scaffoldings Using Neural Networks

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