Blockchain Timestamping for Unalterable Concrete Test Logs
Abstract
This study explores the application of blockchain technology to enhance the integrity and reliability of concrete test logs in civil engineering projects. Traditional methods of recording and managing concrete test data are susceptible to tampering, errors, and loss, which can compromise structural safety and project outcomes. The proposed solution leverages cryptographic hashing and immutable distributed ledgers to securely timestamp each test entry, ensuring tamper-proof records with verifiable audit trails. The system integrates seamlessly with existing concrete testing workflows by capturing test data directly from devices, encrypting it, and submitting hashes to a blockchain network. Smart contracts automate verification processes, improving transparency and accountability. The study further evaluates the solution’s security performance, transaction efficiency, and usability through simulation and prototype testing. Results indicate significant improvements in data immutability, regulatory compliance, and long-term storage capabilities compared to traditional systems. However, challenges such as transaction latency, scalability, industry resistance, and data privacy require careful mitigation through hybrid blockchain models, targeted training, and regulatory engagement. Future directions include integration with Internet of Things (IoT) sensors for real-time monitoring, AI-driven predictive analytics, and interoperability with Building Information Modeling (BIM) systems. This blockchain-enabled approach promises to transform construction quality assurance by embedding security and transparency throughout the data lifecycle, fostering safer, more accountable, and digitally advanced civil engineering practices.
Keywords
Downloads
References
- Alawida, M., Samsudin, A., Alajarmeh, N., Teh, J. S., & Ahmad, M. (2021). A novel hash function based on a chaotic sponge and DNA sequence. IEEE Access, 9, 17882-17897. https://doi.org/10.1109/ACCESS.2021.3049881
- Azad, P., Akcora, C., & Khan, A. (2024). Machine learning for blockchain data analysis: Progress and opportunities. Distributed Ledger Technologies: Research and Practice. https://doi.org/10.1145/3728474
- Bhumichai, D., Smiliotopoulos, C., Benton, R., Kambourakis, G., & Damopoulos, D. (2024). The convergence of artificial intelligence and blockchain: The state of play and the road ahead. Information, 15(5), 268. https://doi.org/10.3390/info15050268
- Bodkhe, U., Tanwar, S., Parekh, K., Khanpara, P., Tyagi, S., Kumar, N., & Alazab, M. (2020). Blockchain for industry 4.0: A comprehensive review. Ieee Access, 8, 79764-79800. https://doi.org/10.1109/ACCESS.2020.2988579
- Cârstea, C. (2023). Methods of Identifying Vulnerabilities in the Information Security Incident Management Process. Romanian Military Thinking, (1), 128-144.
- Celik, B. G., Abraham, Y. S., & Attaran, M. (2024). Unlocking Blockchain in Construction: A Systematic Review of Applications and Barriers. Buildings, 14(6), 1600. https://doi.org/10.3390/buildings14061600
- Celik, Y., Petri, I., & Rezgui, Y. (2023). Integrating BIM and Blockchain across construction lifecycle and supply chains. Computers in Industry, 148, 103886. https://doi.org/10.1016/j.compind.2023.103886
- Chavan, A. (2022). Importance of identifying and establishing context boundaries while migrating from monolith to microservices. Journal of Engineering and Applied Sciences Technology, 4, E168. http://doi.org/10.47363/JEAST/2022(4)E168
- Chavan, A. (2024). Fault-tolerant event-driven systems: Techniques and best practices. Journal of Engineering and Applied Sciences Technology, 6, E167. http://doi.org/10.47363/JEAST/2024(6)E167
- Cohen, J. E. (2016). The regulatory state in the information age. Theoretical Inquiries in Law, 17(2), 369-414. https://doi.org/10.1515/til-2016-0015
- Das, M., Tao, X., Liu, Y., & Cheng, J. C. (2022). A blockchain-based integrated document management framework for construction applications. Automation in Construction, 133, 104001.
- Dhanagari, M. R. (2024). MongoDB and data consistency: Bridging the gap between performance and reliability. Journal of Computer Science and Technology Studies, 6(2), 183-198. https://doi.org/10.32996/jcsts.2024.6.2.21
- Dhanagari, M. R. (2024). Scaling with MongoDB: Solutions for handling big data in real-time. Journal of Computer Science and Technology Studies, 6(5), 246-264. https://doi.org/10.32996/jcsts.2024.6.5.20
- Goel, G., & Bhramhabhatt, R. (2024). Dual sourcing strategies. International Journal of Science and Research Archive, 13(2), 2155. https://doi.org/10.30574/ijsra.2024.13.2.2155
- Hasan, H., AlHadhrami, E., AlDhaheri, A., Salah, K., & Jayaraman, R. (2019). Smart contract-based approach for efficient shipment management. Computers & industrial engineering, 136, 149-159. https://doi.org/10.1016/j.cie.2019.07.022
- Ismail, A. S., Ali, K. N., Iahad, N. A., Kassem, M. A., & Al-Ashwal, N. T. (2023). BIM-Based Automated Code Compliance Checking System in Malaysian Fire Safety Regulations: A User-Friendly Approach. Buildings, 13(6), 1404. https://doi.org/10.3390/buildings13061404
- Karwa, K. (2023). AI-powered career coaching: Evaluating feedback tools for design students. Indian Journal of Economics & Business. https://www.ashwinanokha.com/ijeb-v22-4-2023.php
- Komalavalli, C., Saxena, D., & Laroiya, C. (2020). Overview of blockchain technology concepts. In Handbook of research on blockchain technology (pp. 349-371). Academic Press.
- Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient
- Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf
- Lakshmi, R. (2015). Quality control and quality assurance in building construction. In National Conference on Research Advances in Communication, Computation, Electrical Science and Structures (NCRACCESS-2015).
- Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and communication. International Journal of Science and Research (IJSR), 7(10), 1804-1810. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203184230
- OKIYE, S. E. (2021). Model for Advancing Quality Control Practices in Concrete and Soil Testing for Infrastructure Projects: Ensuring Structural Integrity.
- Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal of Science and Research (IJSR), 6(2). https://www.ijsr.net/archive/v6i2/SR24926091431.pdf
- Rejeb, A., Keogh, J. G., & Treiblmaier, H. (2019). Leveraging the internet of things and blockchain technology in supply chain management. Future Internet, 11(7), 161.
- Sardana, J. (2022). Scalable systems for healthcare communication: A design perspective. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2022.7.2.0253
- Sardana, J. (2022). The role of notification scheduling in improving patient outcomes. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient
- Singh, V. (2022). Visual question answering using transformer architectures: Applying transformer models to improve performance in VQA tasks. Journal of Artificial Intelligence and Cognitive Computing, 1(E228). https://doi.org/10.47363/JAICC/2022(1)E228
- Singh, V., Unadkat, V., & Kanani, P. (2019). Intelligent traffic management system. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 7592-7597. https://www.researchgate.net/profile/Pratik-Kanani/publication/341323324_Intelligent_Traffic_Management_System/links/5ebac410299bf1c09ab59e87/Intelligent-Traffic-Management-System.pdf
- Sukhadiya, J., Pandya, H., & Singh, V. (2018). Comparison of Image Captioning Methods. INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH, 6(4), 43-48. https://rjwave.org/ijedr/papers/IJEDR1804011.pdf
- Valenta, M., & Sandner, P. (2017). Comparison of ethereum, hyperledger fabric and corda. Frankfurt School Blockchain Center, 8, 1-8.
- Van Balen, T., Tarakci, M., & Sood, A. (2019). Do disruptive visions pay off? The impact of disruptive entrepreneurial visions on venture funding. Journal of Management Studies, 56(2), 303-342. https://doi.org/10.1111/joms.12390
- van Groesen, W., & Pauwels, P. (2022). Tracking prefabricated assets and compliance using quick response (QR) codes, blockchain and smart contract technology. Automation in Construction, 141, 104420. https://doi.org/10.1016/j.autcon.2022.104420
- Westgard, J. O., & Westgard, S. A. (2016). Quality control review: implementing a scientifically based quality control system. Annals of clinical biochemistry, 53(1), 32-50. https://doi.org/10.1177/0004563215597248
- Zhu, G., Chou, M. C., & Tsai, C. W. (2020). Lessons learned from the COVID-19 pandemic exposing the shortcomings of current supply chain operations: A long-term prescriptive offering. Sustainability, 12(14), 5858.
- Natti, M. (2023). Reducing Oracle RAC Wait Events by Using Instance-Specific Block Allocation for Production Applications. The Eastasouth Journal of Information System and Computer Science, 1(01), 65–68. https://doi.org/10.58812/esiscs.v1i01.447