Think about an engineer who has a 100Gb hard drive fully loaded with sensor data and wants to assure the boss that the monitored bridge/civil infrastructure system will not collapse in the next decade. In these settings, we need a methodological framework to help the engineer reply to the boss. However, the engineer would need a collective set of self-compatible techniques to accomplish the task.

Development and Application of Techniques for Population-based Back Analysis of Geotechnical Excavations

National Research Found of Singapore & Singapore-ETH Center

2016-2020

In my doctoral research, I developed and implemented a  range of self-compatible computational techniques to streamline practical sensor data interpretation. I first attempted to strike a balance between model simplification and model accuracy (Wang et al., 2019). This work provided a viable numerical modelling technique to simplify an almost infeasible modelling task into one that is well within the capability of design engineers without losing much in the model accuracy. Built upon this work, I proposed an intelligent technique to select sensor data (Wang et al., 2021). I showed that not all data can provide useful information pertaining to the monitored geosystem, and my technique can precisely identify good sensor data that maximizes the knowledge of the geosystem under study. In another paper (Costa et al., 2022), I offered an advanced machine-learning-based optimization algorithm. Engineers often have multiple hypotheses on the possible performance of the geosystem under study. I demonstrated that the proposed optimization algorithm can identify not only the best hypothesis, but also a pool of good solutions. Last, I wrapped up my dissertation by compiling the techniques and comparing the implementation with the current state-of-the-art methodology (Wang et al., 2020). Not only did I demonstrate the coherence in my techniques, but I also provided guidelines for implementing my deliverables to solve real-world problems. I have developed an open-source software, MeDIUM (https://github.com/MeDIUM-FCL/MeDIUM.git), to support geosystem risk management. 

Peer-reviewed Journal Articles

• dBertola, N. J.*, Wang, Z. Z., Cao, W. J., & Smith, I.F.C. (2023). Methodology for selecting measurement points that optimize information gain for model updating. Journal of Civil Structural Health Monitoring. https://doi.org/10.1007/s13349-023-00711-7.[PDF]

• Costa, A.*, Wang, Z. Z., Goh, S. H., & Smith, I. F. C. (2022), “A smart sensor-data-driven optimization framework for improving the safety of excavation operations”. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2021.116413.[PDF]

• Wang, Z. Z.*, Bertola, N. J., Goh, S. H., & Smith, I. F. C. (2021). Systematic selection of field response measurements for excavation back analysis. Advanced Engineering Informatics, 48, 101296. https://doi.org/10.1016/j.aei.2021.101296. [PDF]

• Wang, Z. Z.*, Goh, S. H., Koh, C. G., & Smith, I. F. (2020). Comparative study of the effects of three data‐interpretation methodologies on the performance of geotechnical back analysis. International Journal for Numerical and Analytical Methods in Geomechanics, 44(15), 2093-2113.  https://doi.org/10.1002/nag.3120 [PDF]

• Wang, Z. Z.*, Goh, S. H., Koh, C. G., & Smith, I. F. (2019). An efficient inverse analysis procedure for braced excavations considering three-dimensional effects. Computers and Geotechnics, 107, 150-162. https://doi.org/10.1016/j.compgeo.2018.12.004 [PDF]

Peer-reviewed Conference Papers

• Wang, Z. Z.*, Hu, Y., & Guo, X. F. (2023). “Effects of spatial variability on Bayesian model updating using measured excavation responses”. In 10th European Conference on Numerical Methods in Geotechnical Engineering (NUMGE 2023). https://doi.org/10.53243/NUMGE2023-275. [PDF]

• Wang, Z. Z.*, Goh, S. H., Koh, C. G., & Smith, I. F. (2019). Comparison of data interpretation methodologies for geotechnical back analysis. In 9th Asia Young Geotechnical Engineers Conference (9AYGEC). [PDF]

• Wang, Z. Z.*, Goh, S. H., Koh, C. G., & Smith, I. F. (2018). Soil parameter identification for excavations: A falsification approach. Numerical Methods in Geotechnical Engineering (NUMGE IX), 1, 1181-1188. https://doi.org/10.1201/9781351003629-149 [PDF]

Software

• Wang, Z. Z.*, Pai, S. G. S., & Smith, I. F. C. (2021). MeDIUM: Measurement Data Interpretation using Uncertain Model [Computer software]. https://github.com/MeDIUM-FCL/MeDIUM