Recovery-Level Substructure Design in Landslide-Prone Corridors: Lessons from Two Washington State Megaprojects
Presented By: Gavin Viriyincy
Affiliation: Aecom
Description: Two major bridge programs in Washington State illustrate complementary approaches to substructure design for Recovery-Level bridges in landslide-prone and seismically demanding environments. The Portage Bay Bridges on Seattle’s SR-520 are ~2,700-ft-long twin semi-balanced cantilever CIP segmental viaducts crossing Portage Bay, while the Sammamish River Bridges comprise of three new structures within the I-405/SR-522 interchange in Bothell as part of a 4.5-mile enhancement to the corridor. Both projects are designated as seismic Recovery-Level Bridges by WSDOT. The abutments and first two piers of all bridges are located within an active landslide.
At Portage Bay, the active Delmar Landslide and liquefiable soils required integration of landslide mitigation walls, refined P-multipliers and soil-structure interaction modeling, and foundation flexibility refinement under fixed shaft cutoff elevations. Balanced seismic response across four structurally distinct frames was achieved by adjusting shaft casing depth and thickness, column silos, and foundation stiffness. Constructability was maintained through pilot-hole excavation and using Gr.80 reinforcement to reduce steel congestion in shafts.
In contrast, the Sammamish River Bridges required substructure solutions compatible with precast column systems within a constrained interchange corridor. A special steel pin connection was developed at the base of precast columns to satisfy Recovery-Level displacement and ductility demands while accommodating landslide-induced ground deformations.
Geopolymer Cement from Kaolin for Durable, Low-Carbon Bridge Substructures
Presented By: Hemenandu Bob-Nwachukwu
Affiliation: Hemeco Renewable Solutions
Description: Bridge substructures demand durable, resilient materials that can withstand mechanical stress, environmental exposure, and long-term deterioration. Conventional Portland cement-based concrete, while widely used, contributes heavily to global CO2 emissions and is increasingly challenged by sustainability imperatives. This presentation introduces a novel geopolymer cement produced from locally sourced kaolin and industrial by-products, offering a viable low-carbon alternative for bridge substructures. Laboratory testing demonstrates high compressive and tensile strength, superior water and fire resistance, and enhanced resistance to acid and freeze–thaw cycles compared to traditional cement. These properties directly address durability and resiliency requirements in bent caps, piers, and abutments. The study further explores retrofitting mechanisms and the potential integration of machine learning to optimize mix design and performance modeling. By combining environmental benefits with structural reliability, this research highlights the potential of geopolymer concrete to transform bridge substructure design and construction, particularly in developing regions where both cost efficiency and climate resilience are critical.
Machine Learning–Assisted Structural Design of Bridge Substructures with Ultra-High-Performance and Graphene-Enhanced Concrete
Presented By: Mahbubur Rahman
Affiliation: University of Asia Pacific
Description: This study proposes a computational framework for optimizing concrete bridge substructure design by integrating supervised machine learning algorithms with the material advantages of ultra-high-performance concrete (UHPC) and graphene-enhanced concrete (GEC). The framework applies gradient-boosted decision trees and multi-objective genetic algorithms to evaluate and refine cross-sectional geometry, reinforcement ratios, and strut-and-tie load paths for bent caps, piers, and drilled shafts under combined dead, live, seismic, and vehicular impact loads. Material property datasets—incorporating compressive strengths exceeding 120 MPa for UHPC and tensile strength enhancements of up to 30% from graphene nanoplatelet additives—are embedded into the optimization process to ensure precise correlation between mechanical performance and design variables. Finite element analysis (FEA) models validate predicted stress distributions, crack propagation patterns, and serviceability limits, while deterioration progression is forecast using long short-term memory (LSTM) neural networks trained on historical inspection and environmental exposure data. The methodology reduces total reinforcement demand by up to 20% and extends predicted service life by over 25% compared to conventional substructure designs. This work demonstrates that the synergy between AI-based design optimization and high-performance cementitious composites can yield structurally efficient, impact-resistant, and maintenance-minimized bridge substructures, providing a technically robust solution for the next generation of transportation infrastructure.
Pier Cap Design and Evaluation using Strut-and-Tie Analysis
Presented By: Serhan Guner
Affiliation: University of Toledo
Description: Pier caps play a vital role in transferring bridge girder loads to columns, with millions existing across the United States. A significant concern arises when these components are frequently classified as shear-overloaded despite showing no discernible signs of distress. This disparity highlights limitations in current analysis approaches and poses a massive, cost-prohibitive challenge if all such ‘overloaded’ pier caps were to be rehabilitated. Consequently, there is an urgent need for more accurate analysis methods to determine realistic shear load capacities and correctly identify truly deficient pier caps.
This study's primary objective is to develop a practical yet accurate analysis methodology for evaluating the shear capacities of pier caps identified as overloaded by conventional slender beam theory. The study examined the applicability of two advanced deep beam analysis methods: the strut-and-tie method (STM) and nonlinear finite element analysis. To facilitate practical implementation, a specialized computer tool was developed for STM (Fig. 1), which was verified using analysis results from pier caps located in the State of Ohio. The study further provides comprehensive guidelines for practitioners on when to employ each analysis method for evaluating the shear strength of common pier cap types, along with a discussion of the advantages and challenges associated with each approach.
Evaluation and Structural Repair of Existing Concrete Bridges
Presented By: Mohamed Anber
Affiliation: Kabbani Construction Group - Structural Systems
Description: In the field of bridge rehabilitation, one of the primary challenges lies in achieving a careful balance between restoring the structural integrity of substructure elements and minimizing disruption to traffic, surrounding infrastructure, and operational safety. This is particularly important when addressing critical components such as bent caps, piers, piles, drilled shafts, and abutments, where maintaining both load-bearing capacity and service continuity is essential. Meeting these demands requires thorough assessment, specialized repair techniques, and a clear understanding of structural performance criteria alongside long-term durability objectives.
This presentation will showcase a series of innovative, minimally invasive repair approaches successfully applied to concrete bridge substructures. These techniques demonstrate how structural efficiency and durability can be achieved while preserving the functionality and appearance of existing bridge systems. From addressing deterioration in pile caps exposed to aggressive environments to repairing cracked or spalled piers and abutments without extensive demolition, these strategies reflect a modern, practical approach to substructure rehabilitation.
Ultimately, this presentation will emphasize how contemporary evaluation and repair practices for bridge substructures can successfully balance structural performance, operational needs, and sustainability — extending the service life of critical infrastructure while minimizing cost and disruption.
Field Based NDT Meets Machine Learning: A Practical Tool for Unknown Foundation Bridge Assessment
Presented By: Manshib Tazowar
Affiliation: Engineering, University of Texas At Arlington
Description: Unknown bridge foundations pose critical challenges to Departments of Transportation (DOTs) across the United States, particularly for assessing scour vulnerability and planning maintenance or retrofits. Conventional methods such as excavation or boring are costly, invasive, and impractical for broad deployment, highlighting the need for scalable, non-invasive alternatives. This research proposes a data-driven framework integrating nondestructive testing (NDT) and machine learning (ML) to estimate unknown bridge foundation depth. Approximately 30 concrete bridges were assessed using Sonic Echo Impulse Response (SEIR) testing to obtain reliable ground truth data. Common foundation types—including drilled shafts, abutments, and shallow footings—were examined to ensure wide applicability. Structural and site-specific parameters such as built-year, geometry, soil type, and hydrology condition were also compiled. Multiple ML models—including Random Forest, Support Vector Machine, XGBoost, and LightGBM—were trained and cross-validated with known-foundation data. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative RMSE (RRMSE). The results indicate strong predictive capability and identify key parameters affecting foundation characteristics. This integrated approach combining practical field data with advanced computational techniques offers a practical, accurate, and interpretable solution for foundation classification, advancing infrastructure safety assessments and enabling data-informed asset management strategies.
A More Intuitive Understanding of the Strut-and-Tie Method
Presented By: Robin Tuchscherer
Affiliation: Northern Arizona University
Description: A survey of state DOTs revealed significant confusion among practicing engineers regarding the application of the strut-and-tie method (STM). Respondents felt “engineers have trouble with how open-ended the modeling process can be” and that “generating a workable, appropriate truss model is still a little difficult for the practicing engineer and the code doesn't really help in this area." Current practice is burdened by prescriptive and mandatory code language, which can cause structural engineers to lose sight of underlying theories. In contrast, STM offers a lower-bound approach grounded in first principles, making it a valuable method for visualizing, and establishing load paths in structures. The aim of this project was to reframe STM as an intuitive means for visualizing and providing load paths in structures. A database of more than 240 deep beam specimens was used to conduct a sensitivity analysis comparing experimental versus predicted capacities under varying nodal assumptions. Based on the results, the requirements of the AASHTO LRFD Bridge Design Specifications are reframed, placing emphasis on the underlying theory. The practical implications of these recommendations will be demonstrated through a case study of a post-tensioned straddle bent cap design for a bridge constructed in Arizona. By detailing the process of developing the load path, configuring the truss model, and detailing the required reinforcement, this example clarifies the foundational principles of the STM and simplifies its application.