Announcing the Fall 2020 CaSE Seminar Series Speakers

Below is a listing for the speakers being showcased during the Fall 2020 Civil and Systems Engineering Graduate Seminar Series. All civil engineering graduate seminars are FREE and open to the public. Attendance is required for all enrolled Civil and Systems Engineering graduate students. For information on individual seminars, please refer to the Events Calendar.

All seminars will be held Thursdays from 12-1:25 PM EDT and hosted online until further notice. Please contact Carla Diaz for connection details.

October 1 – Marcelo Giguale, Director World Bank

October 8 – Jean -Francois Molinari, Director, Computational Solid Mechanics Laboratory, EPFL

October 15 – Robert Bachus, Senior Principal, Geosyntec Consultants

October 29 – Steve WaiChing Sun, Associate Professor, Dept. of Civil Engineering and Engineering Mechanics, Columbia University

November 5 – Amy M. Cohn, Arthur F. Thurnau Professor, Dept. of Industrial & Operations Engineering, University of Michigan

November 12 – Lesley Sneed, Professor and Stirrat Faculty Scholar, Dept. of Civil, Architectural and Environmental Engineering, Missouri S&T University

November 19 – Karen Smilowitz, James N. and Margie M. Krebs Professor in Industrial Engineering and Management Sciences, Northwestern University

December 3 – Luke Bisby, Chair of Fire and Structures, Dept. of Civil and Environmental Engineering, University of Edinburgh

Thesis Defense: Aakash Bangalore Satish, “Multimodel Bayesian estimation of total uncertainty and its application to modeling the yield strength of structural aluminum at elevated temperature”





Doctoral Candidate


Monday, August 24, 2020

1 PM

Contact Elena Shichkova for access to this presentation.

“Multimodel Bayesian estimation of total uncertainty and its application to modeling the yield strength of structural aluminum at elevated temperature”

In the multimodel approach, inference is based on an ensemble of model classes.  Uncertainties in the model  probabilities and parameter values are estimated from data using Bayesian inference. While the epistemic uncertainty in the estimates of parameter values and model form are accounted for in literature on Bayesian multimodel inference, the epistemic uncertainty in the estimates of model probabilities is often ignored. When working with small data sets, however, there might be large epistemic uncertainty in the model probabilities.

This thesis presents a Bayesian multimodel approach to quantify the total uncertainty in random variables quantified from limited data, which often serve as inputs to computational models of engineering systems. The novelty of this approach is that it builds on existing Bayesian multimodel methods by integrating into them the treatment of epistemic uncertainty in model probabilities. In this approach, model probabilities are treated as random variables and their posterior distribution is obtained by Bayesian inference. The mode of this posterior distribution quantifies the epistemic uncertainty in the model form, and the marginals quantify the epistemic uncertainty in the model probabilities.

The quantified uncertainty is propagated through computational models of engineering systems using samples drawn from the posterior predictive distribution, which incorporates the aleatory uncertainty as well as the epistemic uncertainty in the model probabilities, model form, and parameters of the set of input model classes. Then, importance sampling is employed to explicitly quantify the uncertainty in the outputs due to each kind of input uncertainty, by post-processing the outputs.

The proposed approach was applied to the estimation of total uncertainty in the yield strength of aluminum 6061-T6 at elevated temperatures, using data derived from an extensive testing program. A total of 100 steady-state uniaxial tension tests were conducted at six temperatures using material sourced off-the-shelf from nine different batches to study the variability in the stress-strain response of aluminum 6061-T6. Multimodel inference is employed using this data to estimate the design value and reduction factor for tensile yield strength incorporating epistemic uncertainty in the model probability, model form, and parameters used to model the variability in the yield strength.

Thesis Defense: Sirui Bi, “On the Processing, Microstructure and Strength of Brittle Foams”





Doctoral Candidate


Tuesday, August 25, 2020

3 PM

Contact Elena Shichkova for access to this presentation.

“On the Processing, Microstructure and Strength of Brittle Foams”

This dissertation focuses on the nonlinear mechanics of open-cell foams and in particular the connection between their cellular microstructure and their macroscopic strength. We first examine the quasi-brittle failure of Reticulated Vitreous Carbon (RVC) foams under compression. The carbon foam microstructure is analyzed using microcomputed tomography. It is shown to follow closely the polyhedral structure of the precursor polymer foam, that is pyrolyzed to produce its carbon counterpart. Scanning single foam-ligaments reveals that their cross-sectional area is a hypocycloid with a non-uniform distribution across the ligament length. X-ray tomography also shows several processing-induced defects in the form of anisotropy and remaining closed-cell faces. Specimens of different geometries and dimensions are crushed between rigid surfaces in order to examine the effect of load distribution, specimen size, relative density and cell-size on the resulting response and the associated crushing strength of the foam. In-situ testing and image analysis is utilized to observe the failure mechanism and associate it with the recorded force-displacement response.

In the second part of this thesis, additive manufacturing is employed to examine the strength of brittle foams with controlled microstructural characteristics. Tessellation-based topologies are used to generate realistic microstructures of open-cell foams that are subsequently 3D-printed by stereolithography. The stress-strain curve and fracture strength of the base photopolymer are measured using tensile tests on small dog-bone specimens with the dimensions of foam ligaments. Synthesized foams are scanned by microcomputed tomography and manufacturing-induced variations are quantified through image analysis. Characterization shows that there is a small amount of volume shrinkage of the material caused by the additive manufacturing process, but all other microstructural features are accurately reproduced. We then perform a series of experiments to measure the compressive response and strength of the 3D-printed foams and connect it to load-transferring conditions, the strength of the base solid material and the foam relative density.

Finally, we examine the compressive response of open-cell aluminum foams under high temperatures. Foam specimens with different cell-sizes are compressed within an environmental chamber under a range of temperatures from 20 °C to 300 °C. The localization and evolution of collapse are monitored and analyzed using Digital Image Correlation (DIC) and the overall force-displacement response is measured. The results indicate that high temperatures significantly affect all mechanical properties of aluminum foams. Both the limit and plateau stresses were found to decrease linearly with temperature. More importantly, their drop is not proportional to the corresponding one in the base material’s yielding stress. The densification strain, that is a measure of the plateau extension, also follows a linear trend albeit in an increasing manner. This increase is attributed to changes of the collapse mechanism, which at high temperatures involves localization in different zones within the foam, as well as increased compaction at the cell-level caused by the base material softening. Finally, we measure the reduction in the strain energy absorption capacity of the foam caused by high temperatures.

Thesis Defense: Marietta Squire, “Mathematical Modeling and the Prevention of Healthcare-Associated Infections”





Doctoral Candidate

Marietta Squire

Monday, July 27, 2020

11 AM

Contact Elena Shichkova for access to this presentation.

“Mathematical Modeling and the Prevention of Healthcare-Associated Infections”

Hospital construction, renewal, and sustainment involve complex processes as a result of rapid change in healthcare technology and information systems technology, changing practices of care, and the ongoing presence of varied bacterial and viral threats. Quality of care is in many ways predicated upon the quality and safety of the environment upon which the patient receives care. Hospitals operate twenty-four hours a day at staff levels that are often not adequate for the number of patients being seen. This further emphasizes the importance of ensuring that a hospital is designed and operated in the safest manner possible, for both patients and staff.

This thesis develops novel methods and toolsets that can be implemented during the hospital design phase as well as during hospital operations, to help ensure patient and staff safety are paramount. These methods quantify the clinical impacts of infection control as well as associated costs and savings, prior to intervention implementation. The Hospital Energy model addresses the management and sustainment of critical care when operating within a distressed power grid environment. These quantitative tools provide an objective assessment of how to best allocate resources and energy within fiscal constraints. Both the Infection De-escalation model and Hospital Energy model are then adapted and expanded to address SARS-CoV-2 transmission in hospitals. The excess energy and economic cost of implementing both ultraviolet light decontamination and negative pressure treatment rooms in hospitals are evaluated through the integration of these two models.

Thesis Defense: Xiaohui Tu, “Developing Image-based Crystal Plasticity Models for Deformation and Crack Propagation in Polycrystalline 7000-series Aluminum Alloys”





Doctoral Candidate

Xiaohui Tu

Thursday, July 23, 2020

11 AM

Contact Elena Shichkova for access to this presentation.

Developing Image-based Crystal Plasticity Models for Deformation and Crack Propagation in Polycrystalline 7000-series Aluminum Alloys

This dissertation develops various models for image-based crystal plasticity FEM modeling of deformation and fracture mechanisms of 7000 series Aluminum alloys. The work begins with the development of preprocessors for micromechanical analysis of polycrystalline-polyphase microstructures of Al alloys, such as Al7075-T651. Starting from input data in the form of electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM) maps of orthogonal surfaces of experimental specimens, a robust methodology is created for generating 3D statistically equivalent virtual microstructures (3D-SEVMs) by a 3D stereological projection of 2D statistical distribution and correlation functions using a genetic algorithm (GA)-based numerical algorithm. Validation of the SEVM reconstruction process is conducted by comparing the SEVM statistics with morphological and crystallographic distributions of grains and precipitates from the experiments. Microstructure-based statistically equivalent representative volume element (M-SERVE) that corresponds to the minimum sized SERVE for convergence of morphological or crystallographic distributions are established using the Kolmogorov–Smirnov (KS) tests. Property-based statistically equivalent RVE (P-SERVE), defined as the smallest SERVE for predicting response functions (both effective and local), is estimated by conducting crystal plasticity finite-element simulations. Convergence plots of material response functions are used to assess the P-SERVE. These convergence analyses reveal that the controlling factor for the SERVE size is local extreme values of stress and strain, as well as the two-point correlation function of precipitates and precipitate-grain correlations.

A coupled crystal plasticity-phase field (CP-PF) model is next used for analyzing crack nucleation and propagation in polycrystalline-polyphase microstructures of metallic alloys. The model explicitly represents elastic and plastic anisotropies, tension-compression asymmetry, and the crack surface topology in the material. The phase-field model incorporates a regularization length-scale 𝑙𝑙𝑐𝑐 that controls the sharpness of the phase-field approximation to the discrete crack. Recently, to incorporate fracture energy anisotropy, the scalar fracture toughness 𝐺𝐺𝑐𝑐 is extended to an orientation-dependent tensor form and is represented in terms of crystallographic planes and their corresponding fracture energies. The development enables favorable crack growth on intrinsically weak planes in crystals. The coupled crystal plasticity-crack phase-field variational formulation is solved by a novel, wavelet-enriched adaptive FE framework. It has the unique capability of optimally resolving high gradients in the phase-field order parameter near the crack surface, and creating adaptive, multi-resolution wavelet-based hierarchical enrichment of the FE model.

Coupled deformation and crack nucleation-propagation simulations in polyphase-polycrystalline microstructures of Aluminum 7000 alloys are performed under monotonic (mode I, II) and cyclic loading conditions. As shown in these micromechanical analyses, the crack evolution in Aluminum 7000 alloys occurs in three stages, viz. crack initiation and propagation inside precipitates, the coalescence of precipitate cracks and crack propagation in the matrix. Surface precipitates play a dominant role in both the precipitate cracking and matrix cracking stages. Surface precipitates generally fail earlier compared to interior precipitates. Dominant cracks are formed by coalescence of precipitate pairs that include surface precipitates.

Thesis Defense: Fardad Haghpanah, “Multi-Scale Evacuation Models To Support Emergency And Disaster Response”





Doctoral Candidate

Fardad Haghpanah

Tuesday, June 23, 2020


“Multi-Scale Evacuation Models To Support Emergency And Disaster Response”

Evacuation is a short-term measure to mitigate human injuries and losses by temporarily relocation of exposed population before, during, or after disasters. With the increasing growth of population and cities, buildings and urban areas are over-populated which brings about safety issues when there is a need for emergency evacuation. In disaster studies, simulation is widely used to explore how natural hazards might evolve in the future, and how societies might respond to these events. Accordingly, evacuation simulation is a potentially helpful tool for emergency responders and policy makers to evaluate the required time for evacuation and the estimated number and distribution of casualties under a disaster scenario.

The healthcare system is an essential subsystem of communities which ensures the health and well-being of their residents. Hence, the resilience of the healthcare system plays an essential role in the resilience of the whole community. In disasters, patient mobility is a major challenge for healthcare systems to overcome. This is where the scientific society enters with modeling and simulation techniques to help decision-makers. Hospital evacuation simulation considering patients with different mobility characteristics, needs, and interactions, demands a microscopic modeling approach, like Agent-Based Modeling (ABM). However, as the system increases in size, the models become highly complex and intractable. Large-scale complex ABMs can be reduced by reformulating the micro-scale model of agents by a meso-scale model of population densities and partial differential equations, or a macro-scale model of population stocks and ordinary differential equations. However, reducing microscopic models to meso- or macro-scale models implies certain drawbacks.

This dissertation contributes to the improvement of large-scale agent-based evacuation simulation and multi-scale hospital evacuation models.  For large-scale agent-based models, application of bug navigation algorithms, popular in the field of robotics, is evaluated to improve the efficiency of such models. A candidate bug algorithm is proposed based on a performance evaluation framework, and its applicability and practicability are demonstrated by a real-world example. For hospital evacuation simulation, crowd evacuation considering people with different physical and mobility characteristics is modeled on three different scales: microscopic (ABM), mesoscopic (fluid dynamics model), and macroscopic (system dynamics model). Similar to the well-known Predator-Prey model, the results of this study show the extent to which macroscopic and mesoscopic models can produce global behaviors emerging from agents’ interactions in ABMs. To evaluate the performance of these multi-scale models, the evacuation of the emergency department at Johns Hopkins University is simulated, and the outputs and performance of the models are compared in terms of implementation complexity, required input data, provided output data, and computation time.

Cancellation of Richard J. Carroll Memorial Lectureship

The Whiting School and Johns Hopkins University’s approach to the COVID-19 epidemic is guided by our commitment to the safety and security of our community, our constituents, and our visitors.  With that, we have determined it is prudent to cancel or postpone any Whiting-sponsored events or activities, on or off campus, that involve 25 people or more until at least the end of April.

Unfortunately, that means that tonight’s Richard J. Carroll Memorial Lectureship will be cancelled.

We sincerely apologize for any inconvenience this might cause.

Announcing the Spring 2020 Graduate Seminar Speakers

Below is a listing for the speakers being showcased during the Spring 2020 Civil and Systems Engineering Graduate Seminar Series. All civil engineering graduate seminars are FREE and open to the public. Attendance is required for all enrolled Civil and Systems Engineering graduate students. For information on individual seminars, please refer to the Events Calendar.

For directions and information on parking please see Maps & Directions link at and select information on Homewood Campus.

Post-Doctoral Fellowship: Network Modeling of Infectious Diseases

Post-Doctoral Fellowship: Network Modeling of Infectious Diseases

Applications are invited for a full-time postdoctoral position at the Department of Civil Engineering and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University under the supervision of Associate Professor Lauren Gardner. The applicant will be expected to undertake and develop research on the topic of spatial epidemiology, with a focus on the development of models to infer outbreak risk factors, predict outbreaks, and optimize resource allocation for outbreak mitigation. Candidates should have expertise in one or more of the following areas: network modelling, optimization, machine learning, statistical modelling, and data visualization, with previous experience working on epidemiological applications.

The postdoc will work closely with an international multidisciplinary team of faculty and Ph.D. students across Engineering and Public Health. In addition, the PI will make every effort to mentor the postdoc for transition into a faculty position. This includes guidance on grant-writing, teaching opportunities, and translation of research. Women and Underrepresented Minorities are highly encouraged to apply. This is a year-long postdoc that can potentially be extended up to two years upon satisfactory performance and availability of funding.


The candidate will be expected to:

  • Possess a PhD degree in computer science, engineering, applied or computational mathematics, or a closely related field.
  • Have expertise in one or more of the following areas: network modelling, machine learning, statistical modelling, data visualization.
  • Previous experience working on epidemiological applications.
  • Strong programming and data visualization skills.
  • Demonstrated experience in analyzing large scale data sets.
  • Demonstrated experience in working on large-scale multi-disciplinary projects
  • The ability to work effectively as part of a multi-disciplinary research team
  • Illustrate the motivation and discipline to carry out autonomous research.
  • High level interpersonal, written and oral communication skills in English.
  • A record of research accomplishment as reflected in publications in peer-reviewed journals and conferences and presentations at scientific meetings.

Start date is flexible. Review of applications will begin immediately and continue until the position is filled. Complete applications should include the following (in a single pdf file) to

(1) A cover letter

(2) A full curriculum vitae

(3) Up to two research publications and/or preprints

(4) The names and contact information for three references

(5) (Optional) A one-page original research proposal on the topic of your choosing, with the following headings: Motivation, Research Questions, Research Approach, Methods, Data Sources, Timeline.

Thesis Defense: Mikhail Osanov, “Topology Optimization for Additive Manufacturing: from Mechanical Components to Orthopaedic Implants”





Doctoral Candidate

Mikhail Osanov

Thursday, September 26, 2019


Shriver Board Room

“Topology Optimization for Additive Manufacturing: from Mechanical Components to Orthopaedic Implants”


Additive Manufacturing (AM) is a free-form fabrication technique that creates structures in a layer-by-layer fashion. Topology Optimization is a free-form, systematic approach to designing structures. These technologies are therefore well-suited for each other, but must be integrated to fully leverage their capabilities. This dissertation seeks to more tightly couple Topology Optimization by proposing several novel algorithms that improve manufacturability of the optimized parts and components. These include cylindrical projection method, which mimics the layer-by-layer nature of Additive Manufacturing processes, and several extensions to overhang projection methods for eliminating support structures, including providing access points for easy removal of support structures, eliminating internal voids, accounting for build plate post-processing costs, and utilizing the overhang constraint within the design of support structures. These algorithms are demonstrated on several design examples and solutions are shown to be directly manufacturable, thereby requiring less post-processing operations that can be time and cost intensive.

The final Chapter of this dissertation is dedicated to the design of femoral implants used in Total Hip Arthroplasty (THA). With the growing number of yearly total joint replacements and the demand for improved mobility and quality of life, the need for high-performance implants is apparent. In this work we seek to alleviate the existing clinical issue of stress shielding, pertinent to current state-of-the-art implants, through new designs using Topology Optimization. We propose a novel Topology Optimization formulation that is capable of addressing regional stress levels in bone by manipulating the topology of the implant, and demonstrate solutions that are predicted to reduce stress shielding effects compared to implants that are currently used in practice.

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