About

Simos Gerasimidis is an Associate Professor in the Civil and Environmental Engineering department at UMass Amherst, affiliated with the Riccio College of Engineering. His research focuses on infrastructure resilience, specifically the structural response of critical infrastructure systems subjected to extreme-loading events, resilience-oriented structural design, and damage propagation. He has contributed to innovative methods for bridge repair, including a team-developed repair method awarded as ‘Idea That Could Change the Future,’ and has obtained NSF funding to support pioneering bridge corrosion repair techniques. His work also involves exploring the potential of 3D printing in repairing aging bridges, with projects testing real-world applications such as the former ‘Brown Bridge’ in Great Barrington. Gerasimidis has been recognized in the civil engineering community through features in ASCE Civil Engineering Magazine and has authored papers selected as “Editor’s Choice” in Communications Engineering. He holds a Ph.D. from Aristotle University of Thessaloniki and an M.Eng. from MIT, with his academic background rooted in civil and environmental engineering.

Research topics

• Materials science
• Engineering
• Structural engineering
• Composite material
• Computer Science
• Mathematics
• Geometry
• Physics
• Mechanical engineering
• Optics

Selected publications

• Cold-Spray Repair of Corroded Steel Bridge Girders via an Optimization-Driven Cyber-Physical Workflow and Robotic Deposition Architecture

  2026-01-29

  articleOpen access

  Corrosion-induced material loss in steel bridge poses persistent persistent challenges for inspection, load rating, and rehabilitation, often leading to conservative decisions and labor-intensive repair strategies. This paper presents a cyber-physical workflow for optimized repair design and a robotic cold-spray deposition architecture targeting corroded steel bridge girders. The framework integrates laser scanning, corrosion mapping, nonlinear finite element analysis, and gradient-based optimization to generate material-efficient cold-spray repair geometries tailored to the as-is condition of deteriorated members. Three-dimensional point cloud data are processed into a structured thickness field that captures localized corrosion while remaining computationally efficient for iterative optimization. Using this representation, spatially varying cold-spray deposition thickness fields are determined to maximize load-carrying capacity recovery while minimizing added material. Both Pareto-based and penalty-based optimization formulations are explored, enabling efficiency-driven trade-off analysis or direct targeting of prescribed capacity levels. The computational framework is validated against full-scale experimental testing of a naturally corroded steel girder, demonstrating close agreement between predicted and measured structural response. To connect optimized repair design with execution, a robotic cold-spray deposition architecture and a dedicated slicing strategy are introduced, together with a virtual environment for simulating deposition kinematics and process constraints. The proposed workflow establishes an integrated, data-driven pathway toward automated, performance-informed cold-spray repair of steel bridge infrastructure.

  PublisherOA PDFDOI

• Robotic fabrication of structural steel architected auxetic lattices for reinforced concrete confinement

  Structures · 2026-02-19 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• Mechanics of reinforced concrete column confinement with architected auxetic steel lattices

  npj Metamaterials · 2026-03-25

  articleOpen accessSenior author

  Negative Poisson’s ratio (auxetic) truss lattice metamaterials have recently emerged as highly effective reinforcements for brittle matrices, enabling strength and ductility levels that were previously unattainable. In this paper, we demonstrate how these architectures can be used to confine axially loaded structural elements, thereby achieving superior mechanical performance. We show that the enhancement arises primarily from exploiting the strain mismatch between the composite phases, which amplifies lateral confinement and induces higher hydrostatic stresses in the matrix. Experimental tests on high aspect ratio prismatic specimens confirm the reproducibility of this effect, extending prior findings from near-cubic samples to structural scale geometries. Through combined analytical and numerical studies, we quantify the differences between auxetic confinement and conventional schemes, and propose new predictive expressions for the load capacity of auxetically confined members. These results establish a direct link between reinforced concrete confinement theory and architected metamaterial design, opening new pathways for structural applications of auxetic lattices.

  PublisherOA PDFDOI

• Cooperative Robotic Fabrication of Structural Steel Architected Auxetic Lattices for Reinforced Concrete Confinement

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• The comparative effects of arboricultural cabling, bracing and species-specific morphology on tree biomechanics

  Urban forestry & urban greening · 2025-06-16

  article
  PublisherDOI

• Progressive Collapse Analysis of Half-Through Truss Bridges Considering Corrosion Effects

  International Journal of Steel Structures · 2025-06-21 · 1 citations

  article
  PublisherDOI

• Enhancing mortar composite matrices with three-dimensional auxetic truss lattice materials for reinforced concrete structures

  Construction and Building Materials · 2024-12-01 · 20 citations

  articleSenior authorCorresponding
  PublisherDOI

• Structural integrity of aging steel bridges by 3D laser scanning and convolutional neural networks

  Communications Engineering · 2024-08-01 · 6 citations

  articleOpen accessSenior author

  For steel bridges, corrosion has historically led to bridge failures, resulting in fatalities and injuries. To enhance public safety and prevent such incidents, authorities mandate in-situ evaluation and reporting of corroded members. The current inspection and evaluation protocol is characterized by intense labor, traffic delays, and poor capacity predictions. Here we combine full-scale experimental testing of a decommissioned girder, 3D laser scanning, and convolutional neural networks (CNNs) to introduce a continuous inspection and evaluation framework. Classification and regression CNNs are trained on a databank of 1,421 naturally inspired corrosion scenarios, generated computationally based on point clouds of three corroded girders collected in lab conditions. Results indicate low errors of up to 2.0% and 3.3%, respectively. The methodology is validated on eight real corroded ends and implemented for the evaluation of an in-service bridge. This framework promises significant advancements in assessing aging bridge infrastructure with higher accuracy and efficiency compared to analytical or semi-analytical approaches. Dr Georgios Tzortzinis and colleagues use a combination of experimental testing and 3D laser scanning to describe the corrosion profile of bridge girders. Their results demonstrate how laser scanners and convolutional neural networks can provide accurate predictions on the structural capacity of ageing steel bridges.

  PublisherOA PDFDOI

• Enhancing Mortar Composite Matrices with Three-Dimensional Auxetic Truss Lattice Materials for Reinforced Concrete Structures

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Failure mode and load prediction of steel bridge girders through 3D laser scanning and machine learning methods

  ce/papers · 2024-09-01 · 1 citations

  articleOpen accessSenior author

  Abstract Corrosion poses a significant threat to the longevity of steel bridges, impacting overall structural integrity. To effectively assess the structural condition of corroded steel bridges, conventional methods rely on visual inspections or single point measurements. To enhance and modernize this approach, this study introduces a novel framework integrating laser scanning data, computational models, and convolutional neural networks (CNNs). The CNN models are trained on a data set consisting of more than 1400 artificial corrosion scenarios generated by parameterizing real scan data from naturally corroded girders. This innovative method predicts the residual capacity and failure mode of corroded beam ends, achieving a low error rate of up to 3.3%. Unlike established evaluation procedures, the proposed evaluation framework directly utilizes post‐processed laser scanner output, eliminating the need for feature extraction and calculations.

  PublisherOA PDFDOI

Recent grants

• CAREER: Auxetic Lattice Reinforcing Metamaterial Architectures for a New Class of Concrete Metastructures

  NSF · $548k · 2021–2027

Frequent coauthors

• Charalampos Baniotopoulos

  17 shared

• Georgios Tzortzinis

  16 shared

• Kshitij Kumar Yadav

  10 shared

• George Deodatis

  8 shared

• Mohammed Ettouney

  8 shared

• Sergio F. Breña

  University of Massachusetts Amherst

  7 shared

• Panos Pantidis

  7 shared

• Fani Derveni

  6 shared

Awards & honors

• Innovative Bridge Repair Method Developed by UMass Amherst-L…