FIU Discovery

Using Spaceborne Sensors to Evaluate the Performance of Civil Infrastructure Systems.  JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES.  39.

Enhancing Predictive Accuracy in Shear Strength of RC Deep Beams: A Comprehensive Analysis Using Ensemble Machine Learning Models.  ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING. 

Innovative Repair of Transversely Cracked PPC Bridge Deck Girder using Precast Prestressing Plates with Shape Memory Alloys

Advanced machine learning techniques for predicting mechanical properties of eco-friendly self-compacting concrete

Prediction of Stress-Strain Behavior of PET FRP-Confined Concrete Using Machine Learning Models.  ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING.  50:7911-7931.

Advanced machine learning techniques for predicting compressive strength of ultra-high performance concrete

Flexural behavior of RC beams strengthened with BFRP bars and CFRP U-jackets: Experimental and numerical analysis

Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Using Supervised Machine Learning Methods.  ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING.  49:4889-4904.

Optimized artificial neural network model for accurate prediction of compressive strength of normal and high strength concrete

Flexural strengthening of RC beams using sisal fibre reinforced polymer (SFRP) composite with anchorage systems

Shear strengthening of RC deep beams using glass chopped strand mat (GCSM) composite

Equivalent boundary conditions to analyze the realistic fatigue behaviors of a bridge RC slab.  STRUCTURAL ENGINEERING AND MECHANICS.  82:369-383.

Approximate Analytical Conditions of a Panel RC Slab for Reproducing the Fatigue Behaviors of a Real Bridge RC Slab

Flexural strengthening of RC beams with sisal fiber composites and sisal fiber rods.  144-147.