Geotechnical & Foundations
Subsurface characterization, shallow and deep foundations, settlement evaluation, difficult ground conditions, and foundation design support.
Rakam Lama Tamang is a Senior Geotechnical Project Manager with over 14 years of experience in civil and geotechnical engineering and construction. His work spans geotechnical investigation, ground improvement, foundation design, and geohazard assessment across complex geological settings and international projects. He has led and supported projects ranging from small assignments to large design-build infrastructure, including mentoring junior engineers.
Expertise includes shallow and deep foundation analysis, geo-structural design, slope stability, excavation support, grouting design and construction oversight, dredging impacts on shoreline structures, sinkhole mitigation, and complex ground conditions involving soft, karstic, expansive, corrosive, and collapsible soils.
Ph.D. research on machine learning, deep learning, physics-informed neural networks, and uncertainty quantification. Applied approaches include RAG, LLM-based retrieval, anomaly detection, clustering, PCA, reinforcement learning, and automated construction-quality analysis.
From subsurface investigations to intelligent engineering systems, the work combines practical field understanding with advanced analysis and digital problem-solving.
Subsurface characterization, shallow and deep foundations, settlement evaluation, difficult ground conditions, and foundation design support.
Excavation support, retaining systems, slope stability, grouting, and numerical analyses using 2D and 3D tools.
Machine learning, uncertainty quantification, anomaly detection, Python automation, and intelligent workflows for engineering data.
Transportation, bridges, dams, levees, transmission structures, pipelines, sediment remediation, and renewable energy projects.
Project management, design review, technical oversight, multidisciplinary coordination, and mentoring junior engineers.
PLAXIS 2D/3D, MATLAB, Python, ArcGIS Pro, SLOPE/W, DeepEX, OpenGround, and analytical dashboards.
Assessing large language model responses for accuracy, relevance, factual grounding, robustness, and consistency in technical knowledge workflows.
Retrieval-driven AI workflows connecting technical documents, structured engineering knowledge, and language model responses.
Exploring tool-using AI agents, multi-step task orchestration, and human-in-the-loop automation for engineering processes.
Representative work spanning transportation, foundations, marine and shoreline systems, geo-structural design, and digital engineering.
A high-complexity geotechnical engagement involving very soft ground conditions, settlement assessment, deep foundation design, advanced PLAXIS analyses, and construction-stage decision support.
Some project descriptions are intentionally concise or generalized to protect confidential details while still communicating the scope and type of engineering work.
Research and development work focused on AI applications in geotechnical engineering, predictive modeling, knowledge workflows, and engineering analytics.
Development of data-driven and physics-guided models for complex geo-engineering phenomena, including turbulent jet grout flow and uncertainty-aware prediction.
Methods for outlier detection, quality control, installation tracking, and faster analysis of field data from geotechnical construction activities.
Retrieval-augmented generation (RAG), LLM evaluation, agentic AI workflows, clustering, PCA, and scalable Python pipelines for engineering knowledge systems.
A combination of scripting, dashboards, and analytical workflows to review installation data, compare field parameters, visualize trends, and accelerate quality checks.
Browse selected journal articles and conference papers related to AI-enabled geotechnical engineering, jet grouting, risk assessment, and computational analysis.
2026 · Acta Geotechnica
View publication ↗Applying analytical rigor and innovation to geotechnical challenges across diverse terrains, sectors, and delivery models.
Temple University · December 2025
Southern Illinois University · 2014
Tribhuvan University · 2010
Licensed in:
ASCE Grouting Committee · Peer-reviewed journal and conference publications · Technical mentorship and project leadership.
A growing collection of tools and experiments. Only apps explicitly published from the private workspace appear here.
Open to thoughtful conversations around geotechnical engineering, digital engineering workflows, infrastructure delivery, and applied AI.