Our research group combines advanced computational tools with access to collaborative networks and experimental support facilities, enabling innovative research in Gene Expression Programming (GEP) and Artificial Intelligence (AI) for civil engineering applications. These resources empower the development and validation of robust, interpretable models tailored to real-world engineering challenges.
GeneXproTools: Our core platform for implementing GEP models, used in applications such as structural behavior prediction, transportation system analysis, and environmental modeling.
Python Frameworks (TensorFlow, scikit-learn, Keras, XGBoost): These are used to build machine learning and deep learning models, complementing GEP-based approaches and allowing the development of hybrid solutions.
MATLAB and Simulink: Applied for simulation, numerical analysis, and model development in engineering systems.
Finite Element Software (ANSYS, ABAQUS): Used for high-fidelity simulation and validation of structural models that can be integrated with AI techniques for improved accuracy and insight.
GIS Tools (ArcGIS, QGIS): Employed for spatial data analysis in transportation planning and environmental monitoring.
AutoCAD and Civil 3D: Supporting the design, drafting, and visualization of infrastructure projects, often used in tandem with optimization models.
SPSS and MS Excel (Engineering ToolPak): Utilized for statistical modeling, risk analysis, and initial data exploration.
High-Performance Computing (HPC): Access to university-provided HPC clusters supports large-scale data processing, model training, and parallel computation.
While our research is primarily computational, we collaborate with and access the following laboratory resources to enhance model development and empirical validation:
Materials and Structural Data Repository: Through collaborations, the group accesses curated experimental data from structural and materials testing, supporting model training and validation for real-world structural behavior prediction.
Remote Sensing and GIS Lab: Enables the integration of spatial and environmental data into machine learning pipelines, supporting projects related to transportation networks, urban development, and environmental risk modeling.
Transportation Systems Lab: Provides tools for traffic data analysis, signal optimization modeling, and smart mobility simulations, aiding the validation of AI-driven transportation models.
These facilities allow our researchers to integrate simulation results, experimental findings, and field data into AI and GEP models—ensuring their applicability and reliability in civil engineering contexts.
In addition to in-house capabilities, we maintain strong partnerships that enhance our access to domain-specific data, applied engineering projects, and field-scale validation opportunities:
Ministry of Public Works and Housing – Structural Section
Jordan Engineers Association
Leading construction and civil engineering companies
Municipal and Transportation Authorities
These collaborations ensure that our models are shaped by practical engineering problems and contribute directly to national infrastructure and planning efforts.
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