Expressway Project Cost Estimation With a Convolutional Neural Network Model

Xue, Xiaojuan; Jia, Yang; Tang, Yuanjie

doi:10.1109/access.2020.3042329

Cited by 13 publications

(5 citation statements)

References 41 publications

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“…Regarding the type of project to which ML and NLP techniques have been most applied, “highway construction” leads the way with six publications (Cao and Ashuri, 2020; Gaussmann et al , 2020; Xue et al , 2020; Moon et al , 2021a; Moon et al , 2021b; Suneja et al , 2021), followed by “composite slabs” (Elhegazy et al , 2021; Juszczyk, 2018a, 2018b) and “sports buildings” with two each (Juszczyk et al , 2018; Juszczyk et al , 2019). The remaining types of projects include: “wood construction” (Akanbi and Zhang, 2020), “stairs” (Zhang et al , 2018), “building renovation” (Cho et al , 2019), “educational buildings” (Yaqubi and Salhotra, 2019), “bridges and piers” (Dimitriou et al , 2018), “electrical installations” (Ronghui and Liangrong, 2021) and “aggregate pavement and bases” (Jeon et al , 2021a).…”

Section: Resultsmentioning

confidence: 99%

“…In the same line, Pessoa et al obtained a MAPE error below 6% in the forecasts of their MLP algorithm. Also, using the MAPE indicator, Xue et al obtained a 17% prediction error with their CNN-based model (Xue et al , 2020). In another work, M. Juszczyk applied an SVM algorithm for cost prediction and calculated an average RSME error of 15 and MAPE of 5%.…”

Section: Resultsmentioning

confidence: 99%

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Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Jacques de Sousa,

Poças Martins,

Sanhudo

et al. 2024

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Purpose This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding. Design/methodology/approach This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction. Findings This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools. Originality/value Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Jacques de Sousa,

Poças Martins,

Sanhudo

et al. 2024

View full text Add to dashboard Cite

show abstract

“…Similarly, research by The Ho et al [52] proposes a methodology to detect ''chatter'' by using a multi-input convolutional neural network (CNN) via image and sound signals to classify data and to determine whether the mechanical machining is stable or vibrational. Research in corresponding fields is expanding as evident from diverse research including performance analysis of fuzzy c means clustering based ANFIS and Elman ANN in effort and cost estimation by Yang et al [53], the establishment of a link between manufacturing and economic variables by cost estimation in mechanical production by H'mida et al [54] and project cost estimation of 415 Chinese expressways using CNN algorithm by Xue et al [55] are to name a few. The field is expanding and is likely to earn dividends in software, manufacturing, and construction domains.…”

Section: Computational Intelligencementioning

confidence: 99%

“…These techniques not only address the challenges and risks associated with software cost estimation but their application impact project success in real-world scenarios, e.g. global software development [71], construction projects [55], defense industries [74], crosscompany database management [99], etc.…”

Section: A Rq-1: What Are the Most Common ML And Non-ml Techniques Fo...mentioning

confidence: 99%

Software Cost and Effort Estimation: Current Approaches and Future Trends

Rashid,

Shafi,

Ahmad

et al. 2023

IEEE Access

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Software cost and effort estimation is one of the most significant tasks in the area of software engineering. Research conducted in this field has been evolving with new techniques that necessitate periodic comparative analyses. Software project success largely depends on accurate software cost estimation as it gives an idea of the challenges and risks involved in the development. The great diversity of ML and Non-ML techniques has generated a comparison and progressed into the integration of these techniques. Based on varying advantages it has become imperative to work out preferred estimation techniques to improve the project development process. This study aims to present a systematic literature review (SLR) to investigate the trends of the articles published in the recent one and a half decades and to propose a way forward. This systematic literature review has proposed a three-stage approach to plan (Tollgate approach), conduct (Likert type scale), and report the results from five renowned digital libraries. For the selected 52 articles, artificial neural network model (ANN) and constructive cost model (COCOMO) based approaches have been the favored techniques. The mean magnitude of relative error (MMRE) has been the preferred accuracy metric, software engineering, and project management are the most relevant fields, and the promise repository has been identified as the widely accessed database. This review is likely to be of value for the development, cost, and effort estimations.

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“…Direct costs refer to various costs associated with the entire project and those that contribute to the formation of the project during project implementation, including material costs and construction costs (labor costs and machinery costs) [21].…”

Section: Direct Costsmentioning

confidence: 99%

Analysis and Case Study of National Economic Evaluation of Expressway Dynamic Wireless Charging

Duan

Xia

et al. 2022

Energies

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As the transportation industry develops new forms of energy and electrification, expressway dynamic wireless charging has become an attractive technology that has the potential to completely solve a range of anxieties associated with electric vehicles. The main objective of this paper was to analyze the economic feasibility of dynamic wireless charging projects on highways. First, the roadside cost of dynamic wireless charging was estimated in terms of equipment, construction, and maintenance costs. Then, various indicators of the national economy of expressway dynamic wireless charging were analyzed. Finally, using the GM (1,1) model, a prediction model for evaluating the associated economic benefits is proposed in this study. As a case study, a national economic evaluation of retrofitting a dynamic wireless charging infrastructure on the Guiyang to Xinzhai Expressway was calculated with the following results: the economic internal rate of return (EIRR) is 11.27% to 29.11%, the economic net present value (ENPV) is 321.59 million RMB to 733.51 million RMB, the economic benefit to cost ratio (EBCR) is 1.02 to 1.32, and the payback period is 3.15 years to 6.35 years. All indicators are higher than the benchmark value for the national economic evaluation, and the sensitivity analysis results are also higher than the benchmark. The results of this paper show that the project is economically feasible and has certain economic benefits. From the perspective of economic benefits, it is necessary to provide more effective information for investors and decision-makers to build dynamic wireless charging highway projects.

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Expressway Project Cost Estimation With a Convolutional Neural Network Model

Cited by 13 publications

References 41 publications

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Software Cost and Effort Estimation: Current Approaches and Future Trends

Analysis and Case Study of National Economic Evaluation of Expressway Dynamic Wireless Charging

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