Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes

Rafiei, Mohammad Hossein; Adeli, Hojjat

doi:10.1061/(asce)co.1943-7862.0001570

Cited by 191 publications

(136 citation statements)

References 62 publications

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“…• A natural language processing system to extract precursors and outcomes from unstructured injury reports (Tixier et al, 2016), • A knowledge extraction and representation system for narrative analysis in the construction industry (Yeung et al, 2014), • An expert system for crack diagnosis in cast-in-place reinforced concrete structures (Chen et al, 1999), • A construction noise prediction model based on case-based reasoning in the preconstruction phase (Kwon et al, 2017), • A machine-learning model for estimating construction costs (Rafiei and Adeli, 2018),…”

Section: Tier Systemsmentioning

confidence: 99%

Grand Challenges in Construction Management

Chen

2019

Front. Built Environ.

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This article is written for the launch of the specialty section on construction management at the Frontiers in Built Environment. This new specialty section opens an on-line platform for academics and practitioners in the subject field to share valuable experiences and findings from innovative research and development as well as practices, which focus on the management of people/workforce, product/production, and processes across built environment project stages, including development and construction, operation and maintenance, as well as demolition and redevelopment, under considerations on the dependability and productivity of construction management services. In this article, the writer presents his humble opinions limited from experiences from academic research and professional services around the world in the past three decades. The topics derived on grand challenges in construction management are gigantic in terms of their coverages, but may have huge impacts on professional development in both short and longer term. Discussions presented here on grand challenges in construction management consist of four parts to respectively focus on an initial cognitive framework of the construction management body of knowledge (CMBOK), intelligence pervasive construction management systems, interdisciplinary digital innovation, and solutions for performance enhancement in megaproject delivery. The research methodology that support the writer to describe the four grand challenges includes literature review, comparison study, site observation, and system architecture analysis and development. It was the writer's initial abductive reasoning to identify the four grand challenges in construction management through an extensive literature review, which was based on national and regional strategies for the construction industry from the world top economies. Some key references relating to these four major topics are collected to support discussions in this article. It is expected that this article could contribute to the debate as well as research and development in construction management in terms of continuous technical enhancements on project-oriented performance and value regardless the scope, the location, and the time associated and concerned. As the study was conducted through abductive reasoning, this article should have its limitation on the coverage to all grand challenges in construction management, and this should leave many unanswered questions for further exploratory research.

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Section: Tier Systemsmentioning

confidence: 99%

Grand Challenges in Construction Management

Chen

2019

Front. Built Environ.

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“…Building cost estimates traditionally include expenditures for labor, material, equipment and construction method, but they are also impacted by many other factors such as industry factors, market conditions, and macro-economic environment. The fluctuation of variables, such as interest rates, the consumer price index, house prices and the building service index may influence the cost [10]. In order to accurately estimate the building cost, it is necessary to identify the main determinant factors.…”

Section: The Factors Impact Building Costsmentioning

confidence: 99%

Transfer Function Analysis: Modelling Residential Building Costs in New Zealand by Including the Influences of House Price and Work Volume

et al. 2019

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An accurate cost estimate not only plays a key role in project feasibility studies but also in achieving a final successful outcome. Conventionally, estimating cost typically relies on the experience of professionals and cost data from previous projects. However, this process is complex and time-consuming, and it is challenging to ensure the accuracy of the estimates. In this study, the bivariate and multivariate transfer function models were adopted to estimate and forecast the building costs of two types of residential buildings in New Zealand: Low-rise buildings and high-rise buildings. The transfer function method takes advantage of the merits of univariate time series analysis and the power of explanatory variables. In the dynamic project conduction environment, simply including building cost data in the cost forecasting models is not valid for making predictions, because the change in demand must be considered. Thus, the time series of house prices and work volume were used to explain exogenous effects in the transfer function model. To demonstrate the effectiveness of transfer function models, this study compared the results generated by the transfer function models with autoregressive integrated moving average models. According to the forecasting performance of the models, the proposed approach achieved better results than autoregressive integrated moving average models. The proposed method can provide accurate cost estimates that can help stakeholders in project budget planning and management strategy making at the early stage of a project.

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“…Deep learning has attracted much attention and is widely applied in computer vision, natural language processing. In civil engineering, deep learning has been used in construction management (Rafiei & Adeli, 2018a; Rafiei, Khushefati, Demirboga, & Adeli, 2017), damage detection for road (A. Zhang et al., 2017; K. Zhang, Cheng, & Zhang, 2018), structure (Hoskere, Narazaki, Hoang, & Spencer, 2018; Kang & Cha, 2018; Modarres, Astorga, Droguett, & Meruane, 2018; Rafiei & Adeli, 2017, 2018b). However, there are few research literatures on deep learning in steel structure damage detection.…”

Section: Introductionmentioning

confidence: 99%

Recognition of rust grade and rust ratio of steel structures based on ensembled convolutional neural network

Gui

Han

2020

Computer aided Civil Eng

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Ensembled convolutional neural network (ECNN) was utilized to recognize the rust grade and rust ratio of steel structure to partially replace traditional visual inspection. The performance of ECNN was demonstrated by theoretical analysis and experimental verification, and the application scenarios of ECNN in the task of rust grade recognition and rust ratio recognition were discussed. The accuracy of ECNN classifier reached 93%, which improves upon the highest accuracy of 90% achieved by using a single classifier. By visualizing the misclassified images, it was found that the rust grade of misclassified image is indistinguishable and the classifiers show strong fault tolerance. The ensembled model is more robust than the single model in the task of rust ratio recognition. Gaussian blur was applied to the test images to study the effect of image blur on model performance, and the results show that the rust segmentation model was not susceptible to image blur.

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Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes

Cited by 191 publications

References 62 publications

Grand Challenges in Construction Management

Grand Challenges in Construction Management

Transfer Function Analysis: Modelling Residential Building Costs in New Zealand by Including the Influences of House Price and Work Volume

Recognition of rust grade and rust ratio of steel structures based on ensembled convolutional neural network

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