- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- Edited by Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
An Automated Framework for Ensuring Information Consistency in Price List Tendering Document
- Chiara Gatto
- Maryam Gholamzadehmir
- Marta Zampogna
- Claudio Mirarchi
- Alberto Pavan
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.83
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,
Politecnico di Milano, Italy
Politecnico di Milano, Italy - ORCID: 0000-0002-1780-5820
Politecnico di Milano, Italy
Politecnico di Milano, Italy - ORCID: 0000-0002-9288-8662
Politecnico di Milano, Italy - ORCID: 0000-0003-0884-4075
- 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
- 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
- Cunningham, T. (2015). Tender Documentation for Construction Projects - An Overview.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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
Metadata License
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
Metadata License
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