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Book Chapter

An Automated Framework for Ensuring Information Consistency in Price List Tendering Document

  • Chiara Gatto
  • Maryam Gholamzadehmir
  • Marta Zampogna
  • Claudio Mirarchi
  • Alberto Pavan

Effective cost estimation for tendering plays a critical role in the building construction process, enabling efficient investment management and ensuring successful execution of the construction phase. Traditional cost estimation procedure involves manual information processing to extract and match technical data from textual description construction resources. This activity requires practitioner deep experience and manual effort, often resulting in errors and, in the worst scenario, judicial disputes. In response to the increasing demand for structured information and automated processes, this study addresses the need for Public Administrations to achieve better control over the data contained in public tendering documents provided to practitioners. To fulfill this objective, a framework is proposed to automatically retrieve information from these documents, serving as a support tool to map items within the documents, highlight missing data, and critical semantic ambiguity. The designed framework aims to develop a tool for automatically identifying similarities between work items and their corresponding elementary resource items in Price List tendering documents. By leveraging the information retrieval NLP technique of cosine similarity through TF-IDF, a methodology was developed to support and facilitate practitioners' activities. Finally, the framework was tested on four case studies extracted from Lombardy Regional Italian price list documents showing that the resulting support tool is able to automate the analysis process and efficiently reveal inconsistency. The model successfully extracted and correctly matched the elementary resource to the corresponding work query in 75% of the cases where the elementary resource was present in the list. Additionally, the model proved to be a valuable tool in helping practitioners identify missing resources

  • Keywords:
  • Automated cost estimation,
  • Information retrieval,
  • Text similarity,
  • NLP,
  • Tendering document,
  • Public Administrations,
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Chiara Gatto

Politecnico di Milano, Italy

Maryam Gholamzadehmir

Politecnico di Milano, Italy - ORCID: 0000-0002-1780-5820

Marta Zampogna

Politecnico di Milano, Italy

Claudio Mirarchi

Politecnico di Milano, Italy - ORCID: 0000-0002-9288-8662

Alberto Pavan

Politecnico di Milano, Italy - ORCID: 0000-0003-0884-4075

  1. Akanbi, T., & Zhang, J. (2021). Design information extraction from construction specifications to support cost estimation. Automation in Construction, 131(April 2020). DOI: 10.1016/j.autcon.2021.103835
  2. Cha, H. S., & Lee, D. G. (2018). Framework Based on Building Information Modelling for Information Management by Linking Construction Documents to Design Objects. Https://Doi.Org/10.3130/Jaabe.17.329, 17(2), 329–336. DOI: 10.3130/JAABE.17.329
  3. Cunningham, T. (2015). Tender Documentation for Construction Projects - An Overview.
  4. Ding, Y., Ma, J., & Luo, X. (2022). Applications of natural language processing in construction. Automation in Construction, 136, 104169. DOI: 10.1016/J.AUTCON.2022.104169
  5. Gatto, C., Farina, A., Mirarchi, C., & Pavan, A. (2023). Development of a framework for processing unstructured text dataset through NLP in cost estimation AEC sector. 4, 0–0. DOI: 10.35490/EC3.2023.232
  6. Hassan, F. ul, & Le, T. (2020). Automated Requirements Identification from Construction Contract Documents Using Natural Language Processing. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 12(2), 04520009. DOI: 10.1061/(ASCE)LA.1943-4170.0000379
  7. Jafari, P., Al Hattab, M., Mohamed, E., & Abourizk, S. (2021a). Automated extraction and time-cost prediction of contractual reporting requirements in construction using natural language processing and simulation. Applied Sciences (Switzerland), 11(13). DOI: 10.3390/app11136188
  8. Jafari, P., Al Hattab, M., Mohamed, E., & Abourizk, S. (2021b). Automated Extraction and Time-Cost Prediction of Contractual Reporting Requirements in Construction Using Natural Language Processing and Simulation. Applied Sciences 2021, Vol. 11, Page 6188, 11(13), 6188. DOI: 10.3390/APP11136188
  9. Lee, J. H., & Yi, J. S. (2017). Predicting Project’s Uncertainty Risk in the Bidding Process by Integrating Unstructured Text Data and Structured Numerical Data Using Text Mining. Applied Sciences 2017, Vol. 7, Page 1141, 7(11), 1141. DOI: 10.3390/APP7111141
  10. Leśniak, A., & Janowiec, F. (2020). Analysis of tender procedure phases parameters for railroad construction works. Open Engineering, 10(1), 846–853. DOI: 10.1515/eng-2020-0095
  11. Locatelli, M., Pattini, G., Seghezzi, E., Tagliabue, L. C., & Di Giuda, G. M. (2022). NLP-based system for automatic processing of quality demands in italian public procedure: a system engineering formalization. Proceedings of the 2022 European Conference on Computing in Construction, 3. DOI: 10.35490/ec3.2022.176
  12. Locatelli, M., Seghezzi, E., Pellegrini, L., Tagliabue, L. C., & Di Giuda, G. M. (2021). Exploring Natural Language Processing in Construction and Integration with Building Information Modeling: A Scientometric Analysis. Buildings 2021, Vol. 11, Page 583, 11(12), 583. DOI: 10.3390/BUILDINGS11120583
  13. M.E.Sepasgozar, S., Costin, A. M., Reyhaneh, K., Sara, S., & Abbasian, Ezatollah Li, J. (2021). BIM and Digital Tools for State-of-the-Art Construction Cost Management. Buildings. DOI: 10.1142/9789814447935_0007
  14. Moon, S., Lee, G., & Chi, S. (2021). Semantic text-pairing for relevant provision identification in construction specification reviews. Automation in Construction, 128, 103780. DOI: 10.1016/J.AUTCON.2021.103780
  15. Munková, D., Munk, M., & Vozár, M. (2013). Data pre-processing evaluation for text mining: Transaction/sequence model. Procedia Computer Science, 18, 1198–1207. DOI: 10.1016/j.procs.2013.05.286
  16. Naji, K. K., Gunduz, M., & Falamarzi, M. H. (2022). Assessment of Construction Project Contractor Selection Success Factors considering Their Interconnections. KSCE Journal of Civil Engineering, 26(9), 3677–3690. DOI: 10.1007/s12205-022-1377-6
  17. Opitz, F., Windisch, R., & Scherer, R. J. (2014). Integration of document- and model-based building information for project management support. Procedia Engineering, 85, 403–411. DOI: 10.1016/j.proeng.2014.10.566
  18. Sdino, L., & Rosasco, P. (2021). The Regional Price Lists for Estimating the Costs of Construction. GREEN ENERGY AND TECHNOLOGY, 213–229. DOI: 10.1007/978-3-030-49579-4
  19. Sitikhu, P., Pahi, K., Thapa, P., & Shakya, S. (2019). A Comparison of Semantic Similarity Methods for Maximum Human Interpretability. International Conference on Artificial Intelligence for Transforming Business and Society, AITB 2019. DOI: 10.1109/AITB48515.2019.8947433
  20. Tang, S., Liu, H., Almatared, M., Abudayyeh, O., Lei, Z., & Fong, A. (2022a). Towards Automated Construction Quantity Take-Off: An Integrated Approach to Information Extraction from Work Descriptions. Buildings, 12(3). DOI: 10.3390/buildings12030354
  21. Tang, S., Liu, H., Almatared, M., Abudayyeh, O., Lei, Z., & Fong, A. (2022b). Towards Automated Construction Quantity Take-Off: An Integrated Approach to Information Extraction from Work Descriptions. Buildings 2022, Vol. 12, Page 354, 12(3), 354. DOI: 10.3390/BUILDINGS12030354
  22. Xie, X., Chang, J., Kassem, M., & Parlikad, A. (2023). Resolving inconsistency in building information using uncertain knowledge graphs: a case of building space management. 4, 0–0. DOI: 10.35490/EC3.2023.267
  23. Zabin, A., González, V. A., Zou, Y., & Amor, R. (2022). Applications of machine learning to BIM: A systematic literature review. Advanced Engineering Informatics, 51(April 2021). DOI: 10.1016/j.aei.2021.101474
  24. Zhang, J., & El-Gohary, N. M. (2016). Semantic NLP-Based Information Extraction from Construction Regulatory Documents for Automated Compliance Checking. Journal of Computing in Civil Engineering, 30(2). DOI: 10.1061/(ASCE)CP.1943-5487.0000346
  25. Zhang, J., Zi, L., Hou, Y., Deng, D., Jiang, W., & Wang, M. (2020). A C-BiLSTM Approach to Classify Construction Accident Reports. Applied Sciences 2020, Vol. 10, Page 5754, 10(17), 5754. DOI: 10.3390/APP10175754
  26. Zhang, Z., & Ma, L. (2023). Using machine learning for automated detection of ambiguity in building requirements. 4, 0–0. DOI: 10.35490/EC3.2023.211
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  • Publication Year: 2023
  • Pages: 837-847

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  • Publication Year: 2023

Chapter Information

Chapter Title

An Automated Framework for Ensuring Information Consistency in Price List Tendering Document

Authors

Chiara Gatto, Maryam Gholamzadehmir, Marta Zampogna, Claudio Mirarchi, Alberto Pavan

DOI

10.36253/979-12-215-0289-3.83

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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