From Field Operations to Economics: Breaking the Barriers. Next Level of Integration (Russian)

Mukanov, Adil; Saginbayev, Almat; Salikhov, R. M.

doi:10.2118/202540-ru

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Indeed, the growing adoption of ML tools for addressing construction challenges has not yet significantly impacted the budgeting process. Budgeting in the construction sector continues to be a manual process, even though NLP and artificial neural network (ANN) have demonstrated the potential for automating some of construction’s budgeting procedures (Mukanov et al , 2020).…”

Section: Introductionmentioning

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

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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: Introductionmentioning

confidence: 99%