Cost Estimation Model (Cem) for Residential Building using Artificial Neural Network

Yadav, Richa; Vyas, Monika; Vyas, None Vivekanand; Agrawal, Sanket

doi:10.17577/ijertv5is010431

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…An inadequate amount of data will affect the accuracy of cost estimates by ANN. For example, Richa et al (2016) uses cost data from the schedule of rates for the past 23 years to estimate the cost of residential buildings using ANN and get result accuracy above 90%. Meanwhile, Chandanshive and Kambekar (2019) in their study find out that the accuracy of the ANN model increases with the data size.…”

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confidence: 99%

Data Quality Issues That Hinder the Implementation of Artificial Neural Network (Ann) for Cost Estimation of Construction Projects in Malaysia

Zammari,

Ayob

2023

JAPCM

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The Artificial Neural Network (ANN) which is one of the Artificial Intelligence (AI) tools, has been identified as a great technique to be used for construction cost estimation in the project. With the optimum quality of data input into the ANN model, it could produce an optimum and reliable cost estimation output. Nonetheless, the construction industry is lack of breadth and depth of data that is required as input into ANN. Though many online databases have been made available for data consumers, data quality problems remain unresolved. Thus, this study aims to identify data quality issues that can hinder the implementation of ANN for cost estimation of a construction project. A literature review and semi-structured interview were employed for the data collection of this research. The content analysis method was used to analyse the information obtained through the literature review. Meanwhile, the data collected from the semi-structured interview with nine (9) respondents were analysed using both content analysis and descriptive statistics analysis methods. The findings revealed six data quality issues that can hinder the ANN implementation for cost estimation of construction projects in Malaysia which are inaccurate data, outdated data, data access barriers, insufficient data, noise in training data, and data input degree of influence. Academically, this study contributes to the body of knowledge about the implementation of ANN for cost estimation of construction projects in Malaysia. Keywords: Artificial Neural Network, Artificial Intelligence, Cost Estimation, Data Quality.

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confidence: 99%

Data Quality Issues That Hinder the Implementation of Artificial Neural Network (Ann) for Cost Estimation of Construction Projects in Malaysia

Zammari,

Ayob

2023

JAPCM

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Artificial Intelligence for Developing Accurate Preliminary Cost Estimates for Composite Flooring Systems of Multi-Storey Buildings

Elhegazy

Chakraborty

Elzarka

et al. 2021

Journal of Asian Architecture and Building Engineering

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In a decision making study, design alternatives are compared with respect to cost, performance, and reliability, and the best is selected. Often, due to the time constraints imposed by the design schedule and budget restrictions, the number of design alternatives considered is limited. This research focuses on composite flooring systems for multi-story buildings and the application of value techniques to the construction industry. The study uses data that obtained from the RSMeans Assemblies Books for the period 1997 to 2019. The data obtained from RSMeans consists of assemblies cost ($/sf of floor) as the dependent variable; and the structural span (ft.), superimposed load (p.s.f), unit cost of sheet metal ($/LF of sheet), unit cost of concrete ($/CY), unit cost of steel structural ($/ton), and total load (p.s.f) as the independent variables. A simple computer model is designed for recommending the optimal composite flooring system of a multi-story building, during the preliminary design stage. The value engineering (VE) team to achieve the VE goals mentioned above can use the model.

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Cost estimation of civil construction projects using machine learning paradigm

Arage¹,

Dharwadkar²

2017

2017 International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Cost Estimation Model (Cem) for Residential Building using Artificial Neural Network

Cited by 3 publications

References 5 publications

Data Quality Issues That Hinder the Implementation of Artificial Neural Network (Ann) for Cost Estimation of Construction Projects in Malaysia

Data Quality Issues That Hinder the Implementation of Artificial Neural Network (Ann) for Cost Estimation of Construction Projects in Malaysia

Artificial Intelligence for Developing Accurate Preliminary Cost Estimates for Composite Flooring Systems of Multi-Storey Buildings

Cost estimation of civil construction projects using machine learning paradigm

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