scholarly journals ANN Based Approach for Estimation of Construction Costs of Sports Fields

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Michał Juszczyk ◽  
Agnieszka Leśniak ◽  
Krzysztof Zima
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Construction Projects ◽  
Construction Costs ◽  
General Applicability ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks ◽  
Cost Of Construction ◽  
Sports Fields

Cost estimates are essential for the success of construction projects. Neural networks, as the tools of artificial intelligence, offer a significant potential in this field. Applying neural networks, however, requires respective studies due to the specifics of different kinds of facilities. This paper presents the proposal of an approach to the estimation of construction costs of sports fields which is based on neural networks. The general applicability of artificial neural networks in the formulated problem with cost estimation is investigated. An applicability of multilayer perceptron networks is confirmed by the results of the initial training of a set of various artificial neural networks. Moreover, one network was tailored for mapping a relationship between the total cost of construction works and the selected cost predictors which are characteristic of sports fields. Its prediction quality and accuracy were assessed positively. The research results legitimatize the proposed approach.

scholarly journals Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 854
Author(s):  
Nevena Rankovic ◽  
Dragica Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Time Estimation ◽  
Effort Estimation ◽  
Activation Functions ◽  
Estimation Model ◽  
Wide Range ◽  
Software Product ◽  
Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

Artificial Neural Networks

2010 ◽  
pp. 122-128 ◽  
Author(s):  
Xiaojun Yang
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Urban Growth ◽  
Hydrological Modeling ◽  
Basic Structure ◽  
Nonlinear Phenomena ◽  
Model Complex ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks

Artificial neural networks are increasingly being used to model complex, nonlinear phenomena. The purpose of this chapter is to review the fundamentals of artificial neural networks and their major applications in geoinformatics. It begins with a discussion on the basic structure of artificial neural networks with the focus on the multilayer perceptron networks given their robustness and popularity. This is followed by a review on the major applications of artificial neural networks in geoinformatics, including pattern recognition and image classification, hydrological modeling, and urban growth prediction. Finally, several areas are identified for further research in order to improve the success of artificial neural networks for problem solving in geoinformatics.

scholarly journals FORECASTING OF SPORTS FIELDS CONSTRUCTION COSTS AIDED BY ENSEMBLES OF NEURAL NETWORKS

2019 ◽  
Vol 25 (7) ◽  
pp. 715-729 ◽  
Author(s):  
Michał Juszczyk ◽  
Krzysztof Zima ◽  
Wojciech Lelek
Keyword(s):  
Neural Networks ◽  
Cost Analysis ◽  
Construction Projects ◽  
Practical Implementation ◽  
Ensemble Averaging ◽  
Construction Costs ◽  
Different Types ◽  
Selection Of ◽  
Sports Fields

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.

scholarly journals Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle) costing of construction projects

2013 ◽  
Vol 13 (3) ◽  
pp. 51-64 ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Construction Projects ◽  
Back Propagation ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Excel Solver

Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective (LCCA) comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method was adopted (using MATLAB SOFTWARE); and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver). The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.

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