event

PhD Defense by Fernando Patino-Ramirez

Primary tabs

Ph.D. Thesis Defense Announcement

Bio-inspired optimization of underground networks   by Fernando Patino-Ramirez   Advisor(s): Dr. Chloe Arson   Committee Members:

Dr. David Frost (CEE), Dr. Sheng Dai (CEE), Dr. Polo Chau (CC), Dr. Cino Viggiani (Univ. Grenoble), Dr. Bernardo Caicedo (Univ. Andes)

 

Date & Time: Oct 29th - 9 am

Location: https://bluejeans.com/498975972

 

 Complete announcement, with abstract, is attached

The exploration of the underground space is essential to the exploitation of energy resources, the foundation and design of structures and, more importantly, to the sustained development of cities both in terms of transportation and access to utilities. The urbanization and densification of the population around the globe has caused a reduction of the above-ground space, and an increased demand for urban transportation and utility systems. Such increased demand and low availability of above-ground space has highlighted the social and environmental benefits that underground construction offers, and the need for new and more efficient subsurface design methods. 
 
Nature has created and perfected exceptional, sustainable and cost-efficient strategies to build underground cavities (e.g., worms and ants), and transportation networks (e.g., roots, leaves and fungal networks). Underground networks are global, interconnected systems, subjected to complex environmental constrains that vary in space and time. Taking inspiration from natural systems, we analyze the mechanical performance of underground structures at the scale of the cavity, and we optimize cost and resiliency of the scale of the entire network.
 
In the first part of the thesis, we take inspiration from biology to optimize networks that connect  resources spread over a finite domain and we apply biological network models to the design of engineered systems. We first study the morphogenesis of slime mold, and we describe its behavior with transport indirect graphs. We apply slime mold underlying network deployment strategies to the optimization of fracture patterns in rock. Then, we use a leaf venation inspired algorithm to model roads and public transportation, benchmarking its performance against theoretical bounds and applying it to the design of transportation networks in the city of Atlanta.
 
In the second part of the thesis, we focus on the mechanical behavior of underground cavities embedded in granular soil at shallow depth, which is relevant to in-situ testing, tunneling, anchoring and arching. We focus on the mechanical behavior and failure patterns of such cavities, and we examine common assumptions used in practice to model cavity expansion. We first present the results of a comprehensive experimental study on pressurized vessels under geo-static stress. We analyze the influence of the length of the cavity, the density of the soil and the vertical surcharge load on failure mechanisms and strain patterns. We then use the finite element method (FEM) and machine learning (ML) algorithms to quantify the influence of various cavity and soil parameters and understand their influence on the behavior of the cavity. Lastly, we show an application to real design problems, using an ant colony optimization algorithm to automate and accelerate the design process of horizontal directional drills.
 

Status

  • Workflow Status:Published
  • Created By:Tatianna Richardson
  • Created:10/15/2020
  • Modified By:Tatianna Richardson
  • Modified:10/15/2020

Categories

Keywords