Portfolio Daniel O. X. Medina Quispe (DOX) Civil Engineering & AI

Research Projects

Current main project

DeepIsolationNet

AI system for efficient inspection and monitoring of seismic isolators for resilient civil infrastructure.

This project aims to develop and validate an automated system for the inspection and monitoring of seismic isolators. A comprehensive database of ambient vibration signals and seismic monitoring records from operational isolators is curated, complemented by traditional inspection methods. This information will be integrated into the TremorBank database, which supports both local and global monitoring processes.

At the local level, the project will introduce a Deep Learning model designed to determine the degree of deterioration in individual isolators under normal operating conditions. On a global scale, a hybrid Deep Learning model will be developed and tested to monitor isolators subjected to seismic events. These models will be incorporated into a single module for post-earthquake analysis and assessment of the isolation system condition after a seismic event.

PI: Luis Bedriñana
Partners: Universidad de los Andes, Chile; Universidad Nacional de San Antonio Abad del Cusco, UNSAAC (Peru)
Funding: ProCiencia, Grant for Applied Research Projects 2025-02

Deep Learning, seismic monitoring, seismic isolation, Artificial Intelligence, neural networks, resilient infrastructure.

Bridge analysis image placeholder for SIBridge

Bridge inspection

Smart Inspection for Bridges (SIBridge)

Scalable Automatic Damage Inspection and Assessment of Reinforced Concrete Bridges via Unmanned Aerial Vehicles.

This project proposes an integration of drone path automation with state-of-the-art AI algorithms for the automatic evaluation and report of surface damages on RC bridges. The main objective is to propose a scalable, low-cost system for inspection and assessment of superficial damage in RC bridges using UAVs.

The project focuses on two main technical challenges: automatic damage segmentation and properties retrieval of concrete structures using 2D images, and flight path optimization of UAVs for data collection of full-size bridges. The resulting system can automate the bridge inspection process, reducing time, effort, and costs.

PI: Luis Bedriñana
Partners: University of Alberta
Funding: University of Alberta - UTEC Faculty Grant, 2024

Concrete structures; bridges; unmanned aerial vehicle; damage detection; machine learning; convolutional neural networks; deep learning; semantic segmentation; path automation.

Research Group

UTEC

RESUSCON

I am a member of the Research Group on Resilient and Sustainable Concrete Infrastructure at UTEC. The group works on scientific and applied research for durable, sustainable, and resilient concrete infrastructure affected by earthquakes.

Group site