ANN Based Approach for Estimation of Construction Costs of Sports Fields

Juszczyk, Michał; Leśniak, Agnieszka; Zima, Krzysztof

doi:10.1155/2018/7952434

Cited by 61 publications

(55 citation statements)

References 17 publications

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“…A complete assessment of these feelings can only be obtained on the basis of surveys carried out among people residing in the area. They can be the basis for determining a subjective nuisance of the received noise [73,74]. The direction of the future research should be the search for an effective policy of monitoring noise as an air pollution.…”

Section: Resultsmentioning

confidence: 99%

The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy

et al. 2019

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This paper addresses the problem of noise in spa protection areas. Its aim is to determine the delimitation of the areas that exceed a permissible noise level around the sanatorium on the example of a health resort in Inowrocław. The determination of the exceedance of permissible noise levels allows us to develop directly effective local policy tools to be included in planning documents. In order to reduce noise infiltration, it is important to define environmental priorities. Taking into account their impact on the health of users in the protection area, environmental priorities enable us to introduce additional elements to street architecture. In order to properly manage space, in accordance with the idea of sustainable development, zones of environmental sensitivity-and their socio-environmental vulnerability-have been designated for assessing damage (exceeding permissible noise in health facilities) and defining methods of building resilience (proper management). This has provided the basis for a natural balance optimized for the people living in these areas. To achieve the goal above, non-linear support vector machine (SVM) networks were used. This technique allows us to classify the linearly inseparable data and to determine the optimal separation margin. The boundaries of the areas which exceeded permissible noise levels (separation margin) were estimated on the basis of noise pollution maps, created by means of the SVM technique. Thus, the study results in establishing buffer zones where it is possible to use varied land utilization in terms of form and function, as described in the planning documents. Such an activity would limit the spread of noise.Sustainability 2019, 11, 4199 2 of 17 it is important to properly manage and recognize social, environmental and health vulnerabilities. "Noise sensitivity" is defined as an individual and increased probability of perceiving sounds as unpleasant [8][9][10][11]. Moreover, getting used to excessive noise levels causes indifference, which is caused by exposure to a hazardous situation [12][13][14][15][16].Noise can be characterized by its source (e.g., anthropogenic or natural causes) or by specific physical values, such as power, intensity, frequency or duration. It should be noted that its relevant parameters refer not only to strictly objective values considered to be unpleasant or harmful but also, to the same extent, to the places where common sounds will cause unwanted or disturbing effects [17][18][19]. Organized urban spaces are treated like resources directly used by local residents. The conditions of these spaces decide also on the possibilities for creating a proper base for planners who, to a greater or lesser degree, introduce quantitative and qualitative changes in the area [20,21].It is important then, to analyze the acoustic situation. The determination of physical parameters such as density, open spaces, their shape, the greenery and the location of the buildings has a significant effect on the noise spread in the environment [18,22]. Maps are a...

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Section: Resultsmentioning

confidence: 99%

The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy

et al. 2019

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“…This traditional approach is accurate but time consuming, and therefore new methods are still being sought by means of new mathematical tools that can support the effectiveness of the calculation. Studies that are worth considering include ones that are employing artificial neural networks [20][21][22], linear regression [23], fuzzy sets [19], and support vector machines [24]. Researchers [25] proposed a hybrid model where multivariate regression method and the artificial neural network (ANN) method have been combined to provide a cost estimate model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the basis of literature studies [22,[41][42][43] and the analysis of announcements in public procurement, 16 variables explaining sports fields were distinguished: Qualitative variables (type of information-DoC) type of the material for sports surface (six types of materials) type of the material for access routes (five types of materials) type of the fence (five types of fences) type of sports equipment-handball (yes or no) type of sports equipment-volleyball (yes or no) type of sports equipment-basketball (yes or no) type of sports equipment-football (yes or no) type of sports equipment-tennis (yes or no) impact of the construction on the environment (rating 1-5) impact on the surroundings (rating 1-5)…”

Section: An Example Of Supporting Cost Calculation With the Use Of Thmentioning

confidence: 99%

Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method

Leśniak

Zima

2018

Sustainability

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Abstract:The idea of sustainable development and the resulting environmentally friendly attitudes are increasingly used in construction projects. Designing in accordance with the principles of sustainable development has an impact on the costs of construction works. The authors of this paper proposed an approach to estimate the costs of sports field construction using the Case Based Reasoning method. In their analysis, they distinguished 16 factors that affect the cost of a construction project and are possible to already be described at an early stage of its preparation. The original elements of the work include: consideration of such environmental factors as the environmental impact of the building, materials used, the impact of the facility on the surroundings affecting the amount of implementation costs and development of own database containing 143 construction projects that are related to sports fields. In order to calculate the similarity of cases, different calculation formulas were applied depending on the type of data (quantitative, qualitative, uncertain, no data). The obtained results confirmed that the CBR method based on historical data and using criteria related to sustainable development may be useful in cost estimation in the initial phase of a construction project. Its application to the calculation of the costs that are related to the implementation of sports fields generates an error of 14%, which is a very good result for initial calculations. In the short run, such factors as the impact of the object and the type of materials that are used from the perspective of their influence on the environment may be decisive as far as the costs determined in the life cycle of the building are concerned, as well as the lowest costs of the building construction ensuring the appropriate quality and respect for the environment.

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“…The research presented in this paper is part of a broad project which aims to develop cost estimation tools for the construction industry. Some previous works on the subject have dealt with the use of self-organising feature maps for clustering sports fields as specific construction objects , estimation of construction works costs for sports fields supported by single neural networks (Juszczyk, Leśniak, & Zima, 2018), or the use of a case-based reasoning approach for construction costs estimates (Zima, 2015;. The paper content includes: a synthetic state-of-the-art review, main assumptions for the problem being solved along with concise presentation of the theoretical background for the mathematical tools applied, a presentation of the research results and a short case study.…”

Section: Introductionmentioning

confidence: 99%

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

Juszczyk

Zima

Lelek

2019

Journal of Civil Engineering and Management

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The paper presents an original approach to construction cost analysis and development of predictive models based on ensembles of artificial neural networks. The research was focused on the application of two alternative approaches of ensemble averaging that allow for combining a number of multilayer perceptron neural networks and developing effective models for cost predictions. The models have been developed for the purpose of forecasting construction costs of sports fields as a specific type of construction objects. The research included simulation and selection of numerous neural networks that became the members of the ensembles. The ensembles included either the networks of different types in terms of their structure and activation functions or the networks of the same type. The research also included practical implementation of the developed models for cost analysis based on a sports field BIM model. This case study examined and confirmed all of the four models’ predictive capabilities and superiority over models based on single networks for the particular problem. Verification including testing and the case study enabled selection of the best ensemble-based model that combined ten networks of different types. The proposed approach is prospective for fast cost analyses and conceptual estimates in construction projects.

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ANN Based Approach for Estimation of Construction Costs of Sports Fields

Cited by 61 publications

References 17 publications

The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy

The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy

Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method

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

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