Towards the Development of a Budget Categorisation Machine Learning Tool: A Review

Sousa, Luís Jacques de; Martins, João Poças; Baptista, J. Santos; Sanhudo, Luís

doi:10.1007/978-3-031-20241-4_8

Cited by 4 publications

(24 citation statements)

References 27 publications

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“…Data access represents the greatest conceptual challenge to developing ML algorithms (Jacques de Sousa et al, 2023b) because it is essential in the learning process of ML models and, therefore, fundamental to the goal of budget compliance prediction (Elmousalami, 2020). Since implementing ML algorithms requires a large amount of data to support their training ECAM 31,13…”

Section: Data Sourcingmentioning

confidence: 99%

“…, 2023b). Researchers must process the data to create a meaningful and compatible sample with their models (Jacques de Sousa et al. , 2023b).…”

Section: Introductionmentioning

confidence: 99%

“…, 2023b). Previous ML applications for construction cost prediction have mainly used private construction execution phase data (Jacques de Sousa et al. , 2023b).…”

Section: Introductionmentioning

confidence: 99%

“…The emergence of Artificial Intelligence may change this paradigm; however, due to technical and cultural reasons, these technologies, which have already been implemented in similar industries, have only started to be adopted by the construction sector (Chen et al, 2019). In recent years, different Machine Learning (ML) algorithm architectures have been adopted in various stages of the construction process (Jacques de Sousa et al, 2023b). The cost prediction aspect of the procurement process has been an area of substantial interest as it would make construction more productive and efficient (aspects where the sector trails behind similar industries) by making more realistic cost assessments and preventing underbudgeting (Kerridge and Halaris, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Predicting construction project compliance with machine learning model: case study using Portuguese procurement data

Jacques de Sousa,

Poças Martins,

Sanhudo

2024

ECAM

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PurposeFactors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.Design/methodology/approachIn this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.FindingsThe findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.Practical implicationsThe model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.Social implicationsAlthough the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.Originality/valuePrevious research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

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Section: Data Sourcingmentioning

confidence: 99%

“…, 2023b). Researchers must process the data to create a meaningful and compatible sample with their models (Jacques de Sousa et al. , 2023b).…”

Section: Introductionmentioning

confidence: 99%

“…, 2023b). Previous ML applications for construction cost prediction have mainly used private construction execution phase data (Jacques de Sousa et al. , 2023b).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting construction project compliance with machine learning model: case study using Portuguese procurement data

Jacques de Sousa,

Poças Martins,

Sanhudo

2024

ECAM

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“…Os algoritmos de IA continuam a necessitar do envolvimento de projetistas, construtores, dono de obra e entidades públicas para avaliar das características da solução óptima do algoritmo [7]. Os algoritmos de IA podem tentar encontrar a solução que satisfaça as restrições e os critérios de optimização [4].…”

Section: Potencialidades De Ia Para O Seu Aproveitamento Em Modelos Bimunclassified

A IA e o BIM: "Magia" ou algoritmos?

Sousa,

Soeiro,

Martins

2024

5º Congresso Português De Building Information Modelling Volume 1: ptBIM

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ResumoO interesse no desenvolvimento e aplicação de modelos de Inteligência Artificial (IA), suportada por algoritmos (AL), na Construção tem aumentado nos últimos anos. Este campo de desenvolvimento posiciona-se como a próxima grande área de pesquisa complementando o Building Information Modelling (BIM). Os modelos BIM são repositórios de informação que podem ser estruturais para a implementação de IA no âmbito da Construção. A IA apresenta diversos tipos de algoritmos (simbolísticos, relativistas, evolutivos, baiesianos e analógicos), que tratam dados ou informação estruturada através de métodos ontológicos ou outros. Este artigo aborda o uso de IA conjuntamente com ferramentas BIM e analisa e descreve aplicações e desenvolvimentos recentes destas ferramentas na Faculdade de Engenharia da Universidade do Porto (FEUP). Neste artigo pretende-se usar ferramentas de IA em casos concretos de projetos.Este trabalho apresenta vários tipos de ferramentas de IA para ilustrar a existência de um ou mais tipos de IA que sejam adequados para uma sinergia com as ferramentas BIM. Desta forma pretende-se delinear previsões para desenvolvimentos futuros, a curto prazo destas tecnologias neste contexto na FEUP.

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Previsão da conformidade de projectos de construção pública em Portugal com recurso a aprendizagem automática

Sousa,

Martins,

Sanhudo

2024

5º Congresso Português De Building Information Modelling Volume 2

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Towards the Development of a Budget Categorisation Machine Learning Tool: A Review

Cited by 4 publications

References 27 publications

Predicting construction project compliance with machine learning model: case study using Portuguese procurement data

Predicting construction project compliance with machine learning model: case study using Portuguese procurement data

A IA e o BIM: "Magia" ou algoritmos?

Previsão da conformidade de projectos de construção pública em Portugal com recurso a aprendizagem automática

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