Impact of the Application of Smart Sensor Networks for the Construction Management of Geotechnical Activities

Nimer Alselami; Khaled Aati; Mohammed Mutnbak; Khaled A. Alrasheed; Muhammad Basit Khan

doi:10.28991/CEJ-2025-011-01-020

Authors

Nimer Alselami Civil and Architectural Engineering Department, College of Engineering and Computer Sciences, Jazan University, Jazan 45142,, Saudi Arabia
Khaled Aati Civil and Architectural Engineering Department, College of Engineering and Computer Sciences, Jazan University, Jazan 45142,, Saudi Arabia
Mohammed Mutnbak Civil and Architectural Engineering Department, College of Engineering and Computer Sciences, Jazan University, Jazan 45142,, Saudi Arabia
Khaled A. Alrasheed Department of Civil Engineering, College of Engineering and Petroleum, Kuwait University, Kuwait City,, Kuwait
Muhammad Basit Khan
mbkhan@um.cityu.edu.hk
Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR,, China

Vol. 11 No. 1 (2025): January

Research Articles

Downloads

PDF

Abstract
How to Cite
Metrics
References
License

The primary objective of this study is to evaluate the impact of smart sensor networks on geotechnical data management, specifically enhancing accuracy, real-time monitoring, safety, and reliability. To achieve this, data was collected through a survey of 380 geotechnical professionals in Saudi Arabia, with 106 valid responses analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Principal Component Analysis (PCA) and Factor Analysis (FA) were employed to identify the key variables and underlying relationships among them. The findings demonstrate that smart sensor networks significantly improve the accuracy of geotechnical data (path coefficient = 0.662), real-time monitoring and early warning systems (path coefficient = 0.701), safety and risk management (path coefficient = 0.761), and data reliability (path coefficient = 0.410). This study introduces a novel framework integrating advanced statistical methods with smart sensor networks, offering a practical approach to optimizing geotechnical operations. The research highlights the importance of advanced data analytics in enhancing the full potential of smart sensors, presenting an innovative solution for improving decision-making and risk management in geotechnical engineering. These findings provide a significant contribution to sustainable and effective geotechnical practices.

Doi: 10.28991/CEJ-2025-011-01-020

Full Text: PDF

Alselami, N., Aati, K., Mutnbak, M., Alrasheed, K. A., & Basit Khan, M. (2025). Impact of the Application of Smart Sensor Networks for the Construction Management of Geotechnical Activities. Civil Engineering Journal, 11(1), 346–368. https://doi.org/10.28991/CEJ-2025-011-01-020

Download Citation

[1] Huang, H. W., Zhang, D. M., & Ayyub, B. M. (2017). An integrated risk sensing system for geo-structural safety. Journal of Rock Mechanics and Geotechnical Engineering, 9(2), 226–238. doi:10.1016/j.jrmge.2016.09.005.
[2] Carri, A., Valletta, A., Cavalca, E., Savi, R., & Segalini, A. (2021). Advantages of IoT-based geotechnical monitoring systems integrating automatic procedures for data acquisition and elaboration. Sensors, 21(6), 2249. doi:10.3390/s21062249.
[3] Adekunle, S. A., Aigbavboa, C. O., Ejohwomu, O., Adekunle, E. A., & Thwala, W. D. (2024). Digital transformation in the construction industry: a bibliometric review. Journal of Engineering, Design and Technology, 22(1), 130–158. doi:10.1108/JEDT-08-2021-0442.
[4] Mottee, L. K., Arts, J., Vanclay, F., Miller, F., & Howitt, R. (2020). Metro infrastructure planning in Amsterdam: how are social issues managed in the absence of environmental and social impact assessment? Impact Assessment and Project Appraisal, 38(4), 320–335. doi:10.1080/14615517.2020.1741918.
[5] Baraibar, J. M., De-Paz, J., & Rico, J. (2022). Challenges for the Implementation of BIM Methodology in the Execution of Underground Works. Buildings, 12(3), 309. doi:10.3390/buildings12030309.
[6] El-Sappagh, S., Ali, F., Hendawi, A., Jang, J. H., & Kwak, K. S. (2019). A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard. BMC Medical Informatics and Decision Making, 19(1), 1-36. doi:10.1186/s12911-019-0806-z.
[7] Wehbe, F., Hattab, M. Al, & Hamzeh, F. (2016). Exploring associations between resilience and construction safety performance in safety networks. Safety Science, 82, 338–351. doi:10.1016/j.ssci.2015.10.006.
[8] Chiarini, A. (2021). Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance? Business Strategy and the Environment, 30(7), 3194–3207. doi:10.1002/bse.2797.
[9] Chen, M., Herrera, F., & Hwang, K. (2018). Cognitive Computing: Architecture, Technologies and Intelligent Applications. IEEE Access, 6, 19774–19783. doi:10.1109/ACCESS.2018.2791469.
[10] Arslan, M., Riaz, Z., Kiani, A. K., & Azhar, S. (2014). Real-time environmental monitoring, visualization and notification system for construction H&S management. Journal of Information Technology in Construction, 19, 72–91.
[11] Iyamu, T., & Mlambo, N. (2022). Actor-Network Theory Perspective of Robotic Process Automation Implementation in the Banking Sector. International Journal of Information Technologies and Systems Approach, 15(1), 1–17. doi:10.4018/ijitsa.304811.
[12] Suau-Sanchez, P., & Burghouwt, G. (2011). The geography of the Spanish airport system: Spatial concentration and deconcentration patterns in seat capacity distribution, 2001-2008. Journal of Transport Geography, 19(2), 244–254. doi:10.1016/j.jtrangeo.2010.03.019.
[13] Enriquez, J. G., Jimenez-Ramirez, A., Dominguez-Mayo, F. J., & Garcia-Garcia, J. A. (2020). Robotic Process Automation: A Scientific and Industrial Systematic Mapping Study. IEEE Access, 8, 39113–39129. doi:10.1109/access.2020.2974934.
[14] Chen, D., Liu, Z., Wang, L., Dou, M., Chen, J., & Li, H. (2013). Natural disaster monitoring with wireless sensor networks: A case study of data-intensive applications upon low-cost scalable systems. Mobile Networks and Applications, 18(5), 651–663. doi:10.1007/s11036-013-0456-9.
[15] Lynch, J. P. (2006). A Summary Review of Wireless Sensors and Sensor Networks for Structural Health Monitoring. The Shock & Vibration Digest, 38(2), 91–128. doi:10.1177/0583102406061499.
[16] Behera, R., & Das, M.R. (2021). Role of Cloud Computing in Geotechnical Engineering Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies. Springer, Singapore. doi:10.1007/978-981-15-6202-0_19.
[17] Yu, X., Fu, Y., Li, J., Mao, J., Hoang, T., & Wang, H. (2024). Recent advances in wireless sensor networks for structural health monitoring of civil infrastructure. Journal of Infrastructure Intelligence and Resilience, 3(1), 100066. doi:10.1016/j.iintel.2023.100066.
[18] Huang, A.-B., Ho, Y.-T., Lee, J.-T., & Wang, C.-C. (2013). Fiber Optic Sensored Geotechnical Testing and Field Monitoring. Sound Geotechnical Research to Practice, 382–404. doi:10.1061/9780784412770.026.
[19] Saftner, D. A., Hryciw, R. D., Green, R. A., Lynch, J. P., & Michalowski, R. L. (2008). The use of wireless sensors in geotechnical field applications. Proceedings of the 15th annual Great Lakes geotechnical/geo environmental conference, 9 May, 2008, Indianapolis, Indiana.
[20] Teh Chang, D. T., Tsai, Y.-S., & Yang, K.-C. (2013). Study of Real-Time Slope Stability Monitoring System Using Wireless Sensor Network (WSN). TELKOMNIKA Indonesian Journal of Electrical Engineering, 11(3), 2230. doi:10.11591/telkomnika.v11i3.2230.
[21] Soga, K., Ewais, A., Fern, J., & Park, J. (2019). Advances in Geotechnical Sensors and Monitoring. Geotechnical Fundamentals for Addressing New World Challenges. Springer Series in Geomechanics and Geoengineering. Springer, Cham, Switzerland. doi:10.1007/978-3-030-06249-1_2.
[22] Abdoun, T., Danisch, L., & Ha, D. (2005). Advanced Sensing for Real-Time Monitoring of Geotechnical Systems. Site Characterization and Modeling, 1–10. doi:10.1061/40785(164)4.
[23] Dixit, A. K., & Nalebuff, B. J. (1993). Thinking strategically: The competitive edge in business, politics, and everyday life. WW Norton & Company, New York, United States.
[24] Dakhil, A., Underwood, J., & Al Shawi, M. (2019). Critical success competencies for the BIM implementation process: UK construction clients. Journal of Information Technology in Construction, 24, 80–94.
[25] Lee, Z. P., Rahman, R. A., & Doh, S. I. (2021). Critical success factors for implementing design-build: analysing Malaysian public projects. Journal of Engineering, Design and Technology, 20(5), 1041-1056. doi:10.1108/JEDT-08-2020-0321.
[26] Ganeshan, M., & Venkataraman, S. (2022). Interface shear strength evaluation of self-compacting geopolymer concrete using push-off test. Journal of King Saud University - Engineering Sciences, 34(2), 98–107. doi:10.1016/j.jksues.2020.08.005.
[27] MijoviÄ‡, P., Giagloglou, E., TodoroviÄ‡, P., MaÄuŠ¾iÄ‡, I., JeremiÄ‡, B., & GligorijeviÄ‡, I. (2014). A tool for neuroergonomic study of repetitive operational tasks. ACM International Conference Proceeding Series, 2637280. doi:10.1145/2637248.2637280.
[28] Gajbhiye, S., Sharma, S. K., & Awasthi, M. K. (2015). Application of Principal Components Analysis for Interpretation and Grouping of Water Quality Parameters. International Journal of Hybrid Information Technology, 8(4), 89–96. doi:10.14257/ijhit.2015.8.4.11.
[29] Waqar, A., Andri, Qureshi, A. H., Almujibah, H. R., Tanjung, L. E., & Utami, C. (2023). Evaluation of success factors of utilizing AI in digital transformation of health and safety management systems in modern construction projects. Ain Shams Engineering Journal, 14(11), 102551. doi:10.1016/j.asej.2023.102551.
[30] Renzi, E., & Trifarò, C. A. (2022). Knowledge and Digitalization: a way to improve safety of Road and Highway Infrastructures. Procedia Structural Integrity, 44, 1228–1235. doi:10.1016/j.prostr.2023.01.158.
[31] Naghshbandi, S. N., Varga, L., & Hu, Y. (2021). Technology capabilities for an automated and connected earthwork roadmap. Construction Innovation, 22(4), 768–788. doi:10.1108/ci-02-2021-0022.
[32] Gunasekhar, T., & Teja, M. S. (2021). Cognitive Computing. Cognitive Engineering for Next Generation Computing, 189–217, John Wiley & Sons, Hoboken, United States. doi:10.1002/9781119711308.ch7.
[33] Costin, A., Pradhananga, N., & Teizer, J. (2012). Leveraging passive RFID technology for construction resource field mobility and status monitoring in a high-rise renovation project. Automation in Construction, 24, 1–15. doi:10.1016/j.autcon.2012.02.015.
[34] Pegin, P., Igolkin, G., & Rajczyk, M. (2018). A model for dynamic design of a superstructure for magnetic levitation vehicles. Transportation Research Procedia, 36, 567–576. doi:10.1016/j.trpro.2018.12.151.
[35] Ratia, M., Myllärniemi, J., & Helander, N. (2018). Robotic process automation - Creating value by digitalizing work in the private healthcare? ACM International Conference Proceeding Series, 222–227. doi:10.1145/3275116.3275129.
[36] Narayanan, A., Mathur, A., Chetty, M., & Kshirsagar, M. (2020). Dark Patterns: Past, Present, and Future. Queue, 18(2), 67–92. doi:10.1145/3400899.3400901.
[37] Micheev, P. S., Muraviev, K. A., Rezchikova, E. V., & Selivanov, K. V. (2023). Designing smart pulse flow meters using diversion analysis. International Journal of Electrical and Computer Engineering, 13(2), 1338–1345. doi:10.11591/ijece.v13i2.pp1338-1345.
[38] Jo, G., Jang, S.-H., & Jeong, J. (2019). Design and Implementation of CPPS and Edge Computing Architecture based on OPC UA Server. Procedia Computer Science, 155, 97–104. doi:10.1016/j.procs.2019.08.017.
[39] Waqar, A. (2024). Evaluation of factors causing lateral migration of light non-aqueous phase liquids (LNAPLs) in onshore oil spill accidents. Environmental Science and Pollution Research, 31(7), 10853–10873. doi:10.1007/s11356-024-31844-x.
[40] Jo, B. W., Lee, Y. S., Kim, J. H., Kim, D. K., & Choi, P. H. (2017). Proximity warning and excavator control system for prevention of collision accidents. Sustainability (Switzerland), 9(8), 1488. doi:10.3390/su9081488.
[41] Dobrica, L. (2022). Robotic process automation platform UiPath. Communications of the ACM, 65(4), 42–43. doi:10.1145/3511667.
[42] Fan, C., Wu, F., & Mostafavi, A. (2024). Discovering the influence of facility distribution on lifestyle patterns in urban populations. Developments in the Built Environment, 17, 100348. doi:10.1016/j.dibe.2024.100348.
[43] Wang, N., Guo, G., Wang, B., & Wang, C. (2020). Traffic Clustering Algorithm of Urban Data Brain Based on a Hybrid-Augmented Architecture of Quantum Annealing and Brain-Inspired Cognitive Computing. Tsinghua Science and Technology, 25(6), 813–825. doi:10.26599/TST.2020.9010007.
[44] Adams-Groom, B., Skjí¸th, C. A., Baker, M., & Welch, T. E. (2017). Modelled and observed surface soil pollen deposition distance curves for isolated trees of Carpinus betulus, Cedrus atlantica, Juglans nigra and Platanus acerifolia. Aerobiologia, 33(3), 407–416. doi:10.1007/s10453-017-9479-1.
[45] Garg, S., & Baliyan, N. (2019). Data on Vulnerability Detection in Android. Data in Brief, 22, 1081–1087. doi:10.1016/j.dib.2018.12.038.
[46] Watson, J., Thomas, S., & Goodfellow, T. (2022). Groundwater resource management during construction dewatering. Sustainable Water Resources Management, 8(4), 91. doi:10.1007/s40899-022-00678-1.
[47] Waqar, A., Othman, I., Shafiq, N., & Mansoor, M. S. (2024). Evaluating the critical safety factors causing accidents in downstream oil and gas construction projects in Malaysia. Ain Shams Engineering Journal, 15(1), 102300. doi:10.1016/j.asej.2023.102300.
[48] Waqar, A., Mateen Khan, A., & Othman, I. (2024). Blockchain empowerment in construction supply chains: Enhancing efficiency and sustainability for an infrastructure development. Journal of Infrastructure Intelligence and Resilience, 3(1), 100065. doi:10.1016/j.iintel.2023.100065.
[49] Rui, Y., Yaik-Wah, L., & Siang, T. C. (2021). Construction project management based on building information modeling (BIM). Civil Engineering and Architecture, 9(6), 2055–2061. doi:10.13189/cea.2021.090633.
[50] Mehdipoor, A., Kaloorazi, S. M., & Ghadim, H. B. (2022). a Study of Critical Success Factors for Bim-Fm Implementation Among Facility Managers in Malaysia. Malaysian Construction Research Journal, 15(Special issue 1), 248–257.
[51] Franco, J. de A. B., Domingues, A. M., Africano, N. de A., Deus, R. M., & Battistelle, R. A. G. (2022). Sustainability in the Civil Construction Sector Supported by Industry 4.0 Technologies: Challenges and Opportunities. Infrastructures, 7(3), 43. doi:10.3390/infrastructures7030043.
[52] Wiiliams, K., & Dair, C. (2007). A framework of sustainable behaviours that can be enabled through the design of neighbourhood-scale developments. Sustainable Development, 15(3), 160–173. doi:10.1002/sd.311.
[53] Waqar, A., Othman, I., Saad, N., Qureshi, A. H., Azab, M., & Khan, A. M. (2023). Complexities for adopting 3D laser scanners in the AEC industry: Structural equation modeling. Applications in Engineering Science, 16, 100160. doi:10.1016/j.apples.2023.100160.

Publication Start Year:	2015
JCR 2023 Impact Factor (IF):	4.3
JIF QUARTILE:	Q1
SJR 2023 Quartile:	Q1
Acceptance Rate:	27%
Review Speed (Average):	76 days
Issue Per Year:	12
Number of Volumes:	10
Number of Issues:	107
Number of Articles:	1552
Number of Reviewers:	3417
Number of Contributors:	3604
Contributing Countries:	86
No. of WoS Citations:	7986
WoS h-index:	34
Scopus CiteScore:	7.0
No. of Google Citations:	17324
Google h-index:	47
Google i10-index:	393
Abstract Views:	5,819,908
PDF Download:	3,790,637

Impact of the Application of Smart Sensor Networks for the Construction Management of Geotechnical Activities

Authors

Downloads

Login

Publisher and Affiliated Societies

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

Journal Imprint

Journal Imprint

Journal Metrics

IndexedBy

Indexed In

Follow us

Follow Us

Most Cited Articles

The ITB Unit Hydrograph Method: A Novel Approach to User-Defined Unit Hydrograph Development (Part II)

Sustainable Interlocking Blocks Containing Sugarcane Bagasse Ash: Structural Integrity, Cost Efficiency, and Environmental Benefits

Design a No-Fine Concrete Using Epoxy in Pavement

Energy Optimization in Residential Buildings: Evaluating PCM-CLT Wall Systems Across U.S. Climate Zones

Revolutionizing Recycled Aggregate Concrete: A Dual Approach Using HCl Treatment and Silica Fume

visitors

Analytics

Information

Address

Contact Info: