Estimation of construction project building cost by back-propagation neural network

Jiang, Qinghua

doi:10.1108/jedt-08-2019-0195

Cited by 26 publications

(15 citation statements)

References 19 publications

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“…However, this paper tries to examine the model with a wider range of parameters and also apply it to green buildings. On the other hand, [28] have studied the application of ANN in cost estimation of building projects, and it compared the results with RBFNN paper methods, and showed the ANN outperforms. Then, the study followed by optimizing the model accuracy, and applying it to other types of projects, and using other methods for cost factors' screening.…”

Section: Methodsmentioning

confidence: 99%

Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

2020

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Construction cost predictions to reduce time risk assessment are indispensable steps for process of decision-making of managers. Machine learning techniques need adequate dataset size to model and forecast the cost of projects. Therefore, this paper presents analysis and studied manuscripts that proposed for cost estimation with machine learning techniques for the last 30 years. The impact of this manuscript is deep studied of machine learning techniques and applied an analysis methodology in cost estimation based on direct cost and indirect cost of construction projects, which consists of two parts. In the first part, for study the proposals, we focus on collecting related studied from Google Scholar and Science Direct journals. The interested application areas for project cost estimation are building, highway, public, roadway, waterrelated constructions, road tunnel, railway, hydropower, power plant and power projects. The second part is regarded to the analysis of the proposals. For cost analysis, there are possibilities to consider two approaches as qualitative and quantitative. However, reflect to the machine learning techniques the quantitative approach is studied. In quantitative approach, we categorized the models in three parts, as statistical, analogues and analytical model and analyse them based on their features. Correspondingly, papers have been thoroughly investigated based on the application area, method applied, techniques implemented, journals, which have been published in, and the year of publication. The most important outcome of this study is to find out the different analytics methods and machine learning algorithms to predict the cost estimation of construction and related projects and aid to find out the suitable applied methods.

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Section: Methodsmentioning

confidence: 99%

Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

2020

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“…Support Vector Machine (SVM) and BPNN, as the representatives of machine learning, are widely used in various fields and have achieved good results. Lu et al (2019) applied BPNN to prediction of three-dimensional coordinates of space points with simple structure and high precision, and Jiang (2019) used BPNN to estimate the building cost, which had smaller error and converged at about 85 times. Li and Jing (2015) established support vector regression (SVR) model, which was trained between 2D feature size and the corresponding circumference size, to provide the accurate data to dress industry, and Cheng et al (2017) used the audio signals based on the support vector machine (SVM) algorithm to solve the problem of activity analysis of construction equipment.…”

Section: Literature Reviewmentioning

confidence: 99%

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Tian

Song

et al. 2021

ECAM

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PurposeThis paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.Design/methodology/approachThe paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.FindingsThe paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R2) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.Originality/valueMachine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R2) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

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“…In addition to the above references, which are more focused on technical aspects, machine learning has also been used for the study and prediction of construction phase costs of civil engineering projects [18]. Examples include the development of a cost estimation model for residential building [18,19] and the prediction of costs associated with construction projects through back-propagation neural networks [18,20].…”

Section: Examples Of the Application Of Machine Learning In Civil Engineeringmentioning

confidence: 99%

Methodology for the Characterisation of Linear Rail Transport Infrastructures with the Machine Learning Technique and Their Application in a Hyperloop Network

Gago

Pérez-Seoane

2021

Urban Rail Transit

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The layout solution for linear rail transport infrastructure will always alternate ‘surface’ sections with ‘tunnel’ and ‘viaduct’ sections. The capital expenditure (CapEx) linked at the planning stage to this type of public asset is strongly connected to the quantity of tunnels and viaducts planned. In this context, for similar lengths, a railway line using 15% tunnels and 7% viaducts to link two cities should not have the same financial viability as one using 8% tunnels and 3% viaducts to link the same cities. The process of planning, design and construction of linear works is heavily scrutinised by public administrations in all countries, and in many cases similar standards of work are shared. Firstly, this research paper highlights the existence of hidden geometric patterns in all linear transport infrastructures worldwide. Secondly, it proposes to exploit the existence of such patterns for the benefit of planners through the computational power available today in machine learning-as-a-service (MLaaS) platforms. This article demonstrates how geometric features extracted from any succession of rectangular trapeziums in linear infrastructures can predict the quantity of kilometres in ‘surface’, ‘tunnel’ and ‘viaduct’ sections in future linear rail transport infrastructures that have not yet been built. The practical application of the proposed working methodology has made it possible to intuit the characteristics of a future Hyperloop transport network in Europe of more than 12,000 km in length.

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Estimation of construction project building cost by back-propagation neural network

Cited by 26 publications

References 19 publications

Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data

Methodology for the Characterisation of Linear Rail Transport Infrastructures with the Machine Learning Technique and Their Application in a Hyperloop Network

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