Automation of Pavement Material Testing Laboratory Process: Review

Pavement Materials Testing Laboratories (PMTLs) play a critical role in ensuring that pavement materials meet the required specifications to sustain traffic loads and achieve design standards. This study investigates the potential for transforming PMTL operations through the adoption of automation technologies, with a particular focus on developing-country contexts such as Papua New Guinea (PNG).  A systematic review of the literature published up to 2025 was conducted using targeted keyword-search strategies to identify relevant advances in laboratory automation across multiple sectors. The review highlights significant progress driven by the integration of artificial intelligence (AI), the Internet of Things (IoT), robotics, and Laboratory Information Management Systems (LIMS). However, findings indicate that PMTLs remain largely dependent on manual processes, limiting efficiency, traceability, and data reliability. To complement the literature review, a case study was undertaken using structured questionnaires distributed to laboratory personnel across six PMTLs and to representatives from seven major road construction companies in PNG. The responses indicate strong support for the adoption of automation to improve laboratory operations. The study identifies a critical research gap in the absence of an integrated, end-to-end automated system capable of linking test requests, sample management, personnel allocation, and equipment utilisation within a unified PMTL framework. While the benefits of automation are evident, challenges related to sustainability, including funding constraints and limited resource capacity, remain significant barriers to implementation. This research contributes to the development of a conceptual foundation for PMTL automation, highlighting the need for scalable, context-specific solutions to enhance quality assurance and operational performance.

Laboratory Automation, LIMS, PMT, Laboratory Quality Assurance, ISO/IEC 17025:2017

1. Alhammad L. A., Ainosah T. K., Ahmad A. M., Samarkandi M. S., Jawi N. H., Alharthi M. A., Alsharif A.M., Anazi E A.A., Aldugeshem S.A., Johali F.Y. “The Impact of Laboratory Automation on Efficiency and Accuracy in Healthcare Settings” International Journal of Community Medicine and Public Health, (2023)https://dx.doi.org/10.18203/2394-6040.ijcmph20233857 [Google Scholar] [Crossref]

2. Buyya R., Yeo C.S.; Venugopal S.; Broberg J. & Brandic I. ”Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th utility” Future Generation Computer Systems (2008). https://doi.org/10.1016/j.future.2008.12.001 [Google Scholar] [Crossref]

3. Calderón D., Folgado F.J., González I., Calderón A.J . (2024). “Implementation and experimental application of industrial IoT architecture using automation and IoT hardware/software”. Sensors, 24(24), 8074. (2024) https://doi.org/10.3390/s24248074 [Google Scholar] [Crossref]

4. Dastjerdi A.V., Buyya R. “Fog Computing: Helping the Internet of Things realize its potential ” Computer, (2016)https://doi.org/10.1109/MC.2016.245 [Google Scholar] [Crossref]

5. Guerrero-Osuna H.A.,.García-Vázquez F., Ibarra-Delgado S., Mata-Romero M.E., Nava-Pintor J.A., Ornelas-Vargas G., Castañeda-Miranda R., Rodríguez-Abdalá V.I. & Sánchez L.S O. “Developing a Cloud and IoT-Integrated Remote Laboratory to Enhance Education 4.0: An Approach for FPGA-Based Motor Control” Journal of Applied Sciences, (2024).https://doi.org/10.3390/app142210115 [Google Scholar] [Crossref]

6. Hoodbhoy Z., Hasan B., Siddiqui K. .“Does artificial intelligence have any role in healthcare in low resource settings?” Vol 2 (July 2019): Journal of Medical Artificial Intelligence, (2019). https://doi.org/10.21037/jmai.2019.06.01 [Google Scholar] [Crossref]

7. Hajek M., Bavio M.A, Benesch T.N, Suárez R.C., Hamel N., Pinak M. &Tucker D.M., Wilding A.. ,“Experiences in Transitioning a Testing Laboratory to the New ISO/IEC 17025:2017 Standard” Radiation Protection Dosimetry, Volume 199, Issue 15-16, Pages 1670–1673, (2023).https://doi.org/10.1093/rpd/ncac209 [Google Scholar] [Crossref]

8. Oakley T., Vaz J., da Silva F., Allan R., Almeida D., Champlin K., da Silva E.S., Tilman A. J., Marr 1, Smith-Vaughan H., Yan J., & Francis J.R. “Implementation of a Laboratory Information Management System (LIMS) for Microbiology in Timor-Leste: Challenges, Mitigation Strategies, and End-user Experiences” BMC Medical Informatics and Decision Making, (2025).https://doi.org/10.1186/s12911-024-02831-6 [Google Scholar] [Crossref]

9. Okezue M. A., Adeyeye M.C., Byrn S.J., Abiola V.O. & Clase K.L. “Impact of ISO/IEC 17025 Laboratory Accreditation in Sub-Saharan Africa: case study” BMC Health Services Research (2020). https://doi.org/10.1186/s12913-020-05934-8 [Google Scholar] [Crossref]

10. Ochiai K., Tahara-Arai Y., Kato A., Kaizu K., Kariyazaki H., Umeno M., . Takahashi K, Kanda G. N., OzakiH., “Automating Care by Self-Maintainability for full laboratory automation” Royal Society of Chemical, (2025). https://doi.org/10.1039/d5dd00151j [Google Scholar] [Crossref]

11. Plebani M. “Total Laboratory Automation: Fit for Its Intended Purposes?” The Journal of Clinical Chemistry and Laboratory Medicine, (2025).https://doi.org/10.1515/cclm-2025-0855 [Google Scholar] [Crossref]

12. Rupp N., Ries R.,Wienbruch R., Zuchner T. “Can I benefit from laboratory automation? A Decision Aid for the Successful Introduction of Laboratory Automation,” Analytical and Bioanalytical Chemistry, (2023) https://doi.org/10.1007/s00216-023-05038-2 [Google Scholar] [Crossref]

13. Saliba J., Al-Shaar W., .Delage M. “Comparison of Field and Laboratory Tests for Soil Suitability Assessment in Raw Earth Construction” Applied Science (MDPI), (2024). https://doi.org/10.3390/app15041932 [Google Scholar] [Crossref]

14. Syed M., Bandara S., French G., Stewart S. “A systematic literature review on Intelligent Automation: Aligning Concepts from Theory, Practice, and Future Perspectives” Advanced Engineering Informatics, (2021). https://doi.org/10.1016/j.aei.2021.101246 [Google Scholar] [Crossref]

15. Zhang L., Liu Z.H., Lv, S Y. J.. Fu Z.M. & Guo L.M.L.. “Comprehensive Improvements in the emergency laboratory test process based on Information Technology” BMC Medical Informatics and Decision Making, (2023). https://doi.org/10.1186/s12911-023-02387-x [Google Scholar] [Crossref]

16. International Standard Organisation. ISO/IEC 17025:2017 General Requirements for the Competence of Testing and Calibration Laboratories. ISO/IEC 17025:2017(E) ed 2017. [Google Scholar] [Crossref]

17. PNG Department of Works Roads & Bridges Specification, 2015 www.works.gov.pg [Google Scholar] [Crossref]

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