Novel Ideas on Experimental, Numerical and Data-Driven Approaches in Sustainable Ground Improvement
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 3863
Special Issue Editors
Interests: geotechnical engineering; machine learning; artificial intelligence; data science; sustainable soil stabilization
Special Issues, Collections and Topics in MDPI journals
Interests: sustainable geotechnics; expansive soils; stabilisation of expansive clay; cementitious materials; geopolymer; ground improvement
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue seeks articles that address recent developments in experimental, numerical and data-driven techniques applicable to sustainable ground improvement.
Sustainable methods of ground improvement have gathered tremendous pace in the last three decades, thus rendering practices that rely on energy-intensive methods and environmentally non-friendly techniques very unattractive. Unique experimental research (laboratory trial batching, instrumentation and testing) validated by robust numerical analysis and vice versa, adopting soil-mixing stabilizing agents (polymers, metakaolin, agro-based materials, organics, industrial waste, silica, lignosulphates, etc), ground freezing, geosynthetics, grouting, electrical stabilisation, vibro stone and rigid columns etc., are currently being applied to address the stability of geo-infrastructural systems, including but not limited to:
- Static and dynamic response systems with 2D and 3D simulations;
- Numerical analysis of shallow and deep improved tunnel systems;
- Embankment and slope stability analysis (including probabilistic analysis with spatial variability);
- Reinforcement of constructed layers;
- Ground remediation;
Additionally, in recent times, enviable data-driven techniques have begun to emerge to prepare geotechnical engineering and ground improvement in particular for the digital transformation age. Data-centric geotechnics adopted in this way can aid better decision making throughout the lifecycle of a ground improvement project from design to construction. Hence, articles leveraging “big data”, machine learning, digital twin paradigms, etc., in the context of the sustainable reinforcement of weak ground are also welcomed.
Dr. Eyo Umo Eyo
Dr. Samuel Abbey
Guest Editors
Manuscript Submission Information
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