Geo-Environmental 

Engineering Solutions

Geotechnical Engineering

Our team has 15 years experience in geotechnical site investigation, in-situ and laboratory testing on soils and bedrocks, interpretation of test results, geotechnical/pavement design and recommendation to provide innovative and cost-effective engineering solutions in Texas. 

We are also dedicated to developing Geotechnical/Pavement Data and Design Reports in helping Designers and Contractors in a Design/Build environment achieve design efficiencies without compromising the integrity of structure performance. Please Contact Us

Environmental Engineering

 GEES's environmental services consist of the following:

1. Full (ASTM) Environmental Site Assessment Studies, ASTM E 1527

2. Phase II ESA. ASTM E 1903

3. Phase III Remediation Action Plan (RAP)

GEES has registered Professional Engineers (PE) in geotechnical and environmental engineering in Texas to provide sustainable and cost-effective engineering solutions to our clients, and solve their environmental problems. Please Contact Us

Geothermal Energy and Applications

Texas geologies, including geothermal resources in the State’s sedimentary basins, along with the State’s status as the epicenter of the oil and gas industry, present a large and promising opportunity to develop geothermal resources in the State. 

Our team has over 10 years experience in assessing thermal properties of soils/bedrocks, and its engineering applications in Geothermal Energy Piles (GEPs), Ground Source Heat Pumps (GSHPs), and Geothermal Deicing Systems (GDSs).  Please Contact Us

Artificial Intelligence (AI) in Geotechnical Engineering

GEES provides sustainable/cost-effective engineering solutions for our clients using the AI techniques. Please Contact Us

1. Machine Learning in Geoengineering 

2. Bayesian Compressive Sampling (BCS) to 2D/3D Spatial Data

3. Statistical Interpretation of 2D/3D Sparsely Measured Geo-data

4. Random Field Simulation from 2D/3D Sparse Measurements

5. Geotechnical Uncertainty, Reliability and Risk

Fig. 1 Two-dimensional (2D) prediction of unit skin friction using AI technique for deep foundation design

Fig. 2 Three-dimensional (3D) prediction of unit skin friction using AI technique for deep foundation design