Keynote Lecture: Advances in Deep Foundation Testing, Instrumentation, and Analysis – A Game Changer for Future Design and QC/QA?-PPT Presentation

PPT Presentation

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The presentation examines how advances in full-scale testing, instrumentation, data analysis, and artificial intelligence (AI) could transform deep-foundation design and quality control/quality assurance.<br /><br />Buildwise’s research strategy combines detailed soil characterization, careful recording of construction methods and parameters, intensive monitoring, and—in some cases—excavation and inspection. Instrumentation has evolved from conventional strain gauges and extensometers to fiber-optic systems, including FBG, BOFDA, and Rayleigh sensing, enabling measurements at multiple points or continuously along structures.<br /><br />Recent full-scale applications include instrumented screw-injection and driven tubular piles, deep piles tested under compression, tension, and cyclic loading, and piles supporting a bridge over the River Scheldt. Fiber optics have also been used to monitor geogrids in a reinforced-soil embankment, alongside long-term displacement monitoring and finite-element back-analysis. These projects demonstrate the growing ability to collect detailed performance data under real conditions.<br /><br />Such data can calibrate and validate design methods, support site-specific design, and improve understanding through back-analysis. However, realizing these benefits requires data that are accessible, structured, standardized, and linked across soil investigation, construction, and performance monitoring. Shared databases, such as Flanders’ GIS-based DOV, illustrate the potential of organized, accessible information.<br /><br />AI research in geotechnics is exploring pile-capacity and driveability prediction, ground identification, foundation quality control, deformation forecasting, digital twins, and construction risk analysis. AI may improve prediction accuracy and reduce variability, but many applications remain at the research or pilot stage. Success depends on sufficient high-quality data and the integration of expert judgment.<br /><br />The presentation concludes that AI is likely to become a major force in geotechnical engineering. Progress will depend on effective data governance, collaboration—potentially through federated AI—and continued integration of engineering expertise.

Keywords

deep-foundation design

full-scale testing

instrumentation

fiber-optic sensing

quality control

quality assurance

geotechnical engineering

pile performance

data analysis

artificial intelligence