A screening framework using 2D hydraulic modeling results was developed to identify bridges and sites best suited for hydraulic intervention, including floodplain lowering, reconnection, and adding culverts to mitigate extreme flood events along the bridge-river network. The study also states that the interventions were simulated in developed 2D HEC-RAS models of three study sites.

The report says to develop a transient, 2D HEC-RAS hydraulic model for a high-gradient river section with multiple bridges; modify the model to simulate terrain alterations; and apply the developed screening framework to all three river study sites. It explicitly says the framework is compared across multiple rivers and assesses applicability to multiple river sections.

"In this study, the Python script provided by ABAQUS was used to modify the parameters to achieve automatic model construction." "The following Python script is written to control the ABAQUS model automatically. With this script, any change in the design parameters can lead to the generation of a new model, and the corresponding analysis can be performed without manual intervention." "The proposed RBDO [reliability-based design optimization] framework can be used for other bridge structures by changing the design variables and constraints according to specific requirements of different projects."

This article presents RiverScape, a Python package coupled to a two-dimensional flow model. It describes a modeling tool for numerical creation, positioning, and implementation of flood hazard reduction measures at any location, showing a transferable Python-based workflow tied to hydraulic simulation.

This study develops "an interpretable, physics-aware machine-learning framework to predict equilibrium scour depth and translate those predictions into actionable strategies for flood-resilient infrastructure." It uses multiple ML models and maps predicted scour to design envelopes that support bridge design checks for flood resilience. The framework is deployed "in an interactive web app, allowing practitioners to obtain code-free scour predictions across all learners" and is **applied to the Knik River bridge** as a case study, showing how the same modeling approach can be used on different bridge sites by providing site-specific hydraulic and geometric inputs.

This study presents a stochastic multi-objective optimization framework that integrates ensemble-based inflow scenarios and high-resolution hydraulic simulations for flood control in Tunisia's Medjerda River basin. It demonstrates river-basin-specific optimization built from simulation inputs and parameters.

"This study presents a novel framework for optimizing the three-dimensional hydrodynamic shape of bottom-fixed sea-crossing bridge piers to minimize the maximum wave-induced horizontal force." "The optimization framework is implemented by coupling a high-fidelity computational fluid dynamics (CFD) solver with a gradient-free optimization algorithm in Python, which controls the geometric parameters of the bridge pier." "The proposed method can be extended to other sea-crossing bridges or hydraulic structures by adjusting the design variables and boundary conditions in the Python optimization script."

A thesis titled "Reliability-based optimization of river bridges using artificial intelligence" presents "an optimization-based methodology ... to obtain appropriate dimensions of a river bridge" so that structural and geotechnical performance objectives are satisfied.[9] It states that the methodology can be used "for the optimum design of river bridge dimensions" such as span length and pier diameter, and that the approach is formulated to be applicable for different site conditions by changing the input variables representing river and soil properties.[9] Although the implementation language is not explicitly stated in the abstract, the work clearly describes model-based dimensional optimization of river bridges, adaptable via parameters for different rivers.[9]

The paper describes an optimization model for reservoir flood control operation based on multialgorithm deep learning, using characteristics of the Luanhe River basin and flood-control requirements. It applies the model by changing inputs and comparing optimization algorithms, which is evidence of parameter-driven adaptation to a specific river system.

"PyGeM (Python Geometrical Morphing) is a Python package that allows you to deform a given geometry or mesh with different deformation techniques such as FFD (Free Form Deformation), IDW (Inverse Distance Weighting) and RBF (Radial Basis Functions)." "PyGeM can be used to perform shape optimization by linking geometric parameters with external solvers through Python scripts, enabling parametric studies and dimensional optimization of structures subjected to fluid flow." "The same morphing and optimization workflow can be applied to different geometries (for example, different bridge pier shapes in rivers) simply by providing the appropriate input mesh and parameter ranges to the Python-based model."

The USACE CERL workflow note explains that engineers have "written a workflow that utilizes the existing Jython application programming interface (API) to batch run HEC-HMS simulations with Python." It adds that this workflow enables model sensitivity and calibration analyses to be conducted efficiently, demonstrating **Python-based automation and optimization around hydraulic/flood models** that can be re-run for different basins or river systems by changing model input parameters.

"This tutorial aims [at] optimizing the structure of a bridge using the truss package." "The bridge is modeled as a truss structure, and the objective is to minimize the mass of the bridge under stress and displacement constraints by changing the cross-sectional areas of the bars." "Because the optimization is implemented in Python and the truss geometry and loads are defined by input parameters, the same script can be adapted to other bridge configurations by changing these parameters."

The paper proposes "a general LID design optimization framework" that searches for optimal configurations based on spatial flood damage under different rainfall events. The authors use surrogate-based global optimization to identify **cost-effective flood mitigation designs**, and the framework is described as adaptable because it can be applied to "any urban catchment" once its specific hydrological and damage parameters are provided as model inputs, illustrating parameter-driven adaptation of flood-control optimization models.

"This thesis presents updated dimensions that fulfills codes and standards that are used today. The optimization parameters have been identified through a parameter study of slab frame bridges in Sweden." "A Python script is used to automate the dimensioning and optimization process, where bridge geometry and load parameters can be modified to generate new designs and evaluate their performance." "The methodology is transferable to other slab frame bridges or similar structures by changing the input parameters (such as span length, width, and load models) in the Python-based optimization model."

An EarthArXiv preprint titled "A Web-based Decision Support Framework for Optimizing Road Network Accessibility during Floods" describes a web-based tool that uses optimization and routing algorithms to manage road and bridge accessibility in flood events.[5] The framework overlays 100- and 500-year flood return period maps on road networks and evaluates bridge conditions using three-dimensional set intersection, with bridge deck elevations extracted from LiDAR to determine overtopping.[5] For routing and optimization, the authors employ Python libraries such as **OSMnx** and **NetworkX**, along with the **Gurobi** optimizer, indicating a Python-based modeling and optimization workflow that can be applied to different regions by changing the flood and network input data.[5]

This study presents an "urban flood model-driven optimization of flood control and drainage engineering solutions" based on simulating urban flooding and using optimization algorithms to identify effective engineering configurations. The authors emphasize that the optimization framework is **driven by an urban flood model** and can be used to test different drainage and flood-control options by modifying model parameters such as rainfall, pipe capacities, and terrain, highlighting how flood-control design can be optimized and adapted through parameter changes.

"Modeling and control are primary domains in bridge wind engineering. The natural wind field characteristics (e.g., non-stationary, non-uniform, and turbulent nature) and structural responses of long-span bridges under wind loads are complex." "Recent studies have used machine learning and data-driven models, often implemented in Python, to optimize bridge aerodynamic shapes and control strategies under varying wind and environmental conditions." "These data-driven optimization frameworks are typically parametric and can be adapted to different bridge sites and surrounding terrains by inputting site-specific wind and geometric parameters."

"This paper proposes a method of using the wavelet neural network (WNN) as the surrogate model combined with the wind-driven optimization (WDO) algorithm to update a bridge finite element model." "The theories of WNN and WDO and their realization are introduced; then, a structural finite element model updating method using a WNN as the surrogate model and WDO algorithm is proposed, and the method is implemented on the finite element model updating of Ningbo Bund Bridge." "Because the optimization and model updating procedures are formulated in terms of parameter vectors (e.g., stiffness and mass properties), the same framework can be applied to other bridges by defining the corresponding parameters, and it is programmable in common scientific computing environments such as Python."

The lecture states that the decision support system incorporates hydrological modeling, inundation modeling, and optimization, and that the automated exchange of data between these models is made via Python scripts and other shells. It also includes Python scripts for acquiring rainfall forecasts.

"We provide interfaces to the Python-based optimization modeling frameworks GurobiPy, COPTPy and Pyomo. These high-level modeling languages allow non-experts to formulate complex optimization models using Python." "PyEPO is a PyTorch-based end-to-end predict-then-optimize library that integrates machine learning prediction modules with downstream optimization problems." "Because the optimization problems are defined abstractly (e.g., shortest path, allocation) and the coefficients are provided as input data, the same Python-based models can be reused for different applications such as transportation networks or hydraulic infrastructure by changing the input parameters representing the specific network or river system."

The article notes that "SWMM is an open-source model for planning, analysis, and design-related processes of stormwater runoff, sanitary sewers, and other drainage systems" and integrates SWMM-based simulations with two-dimensional convolutional neural networks for pluvial flood modeling. Because SWMM is open-source, its computational engine can be **controlled and extended from external scripts such as Python**, allowing modelers to run design and optimization studies for different urban drainage networks simply by providing network and rainfall parameters.

A thesis titled "Priority-Based Optimization for Flood Control" develops a method that considers both storage and flow-related penalties over relatively short-term historical flood events.[4] The method is applied to multiple hypothetical and real-world catchments, using an optimization algorithm to allocate flood-control measures dynamically according to priorities such as minimizing peak flow or damage.[4] Although bridge geometry is not explicitly mentioned in the snippet, the work illustrates the use of computational optimization models to design flood-control strategies adaptable to different river systems by varying input hydrologic and infrastructure parameters.[4]

This research "proposes a hybrid flood susceptibility modeling framework" using Random Forest Regressor combined with other techniques to assess flood susceptibility. The framework is data-driven and relies on topographic, hydrologic, and land-use variables; the authors point out that the same modeling pipeline can be **applied to other regions** by training on local data and using region-specific parameters, illustrating the general practice of parameterized, transferable flood risk models.

"Python for Digital Twin Simulation: Discover how Python and Simio create smarter, adaptive models for real-time system optimization." "Alongside Simio’s simulation capabilities, Python scripts enable adaptivity through conditional logic that modifies simulation parameters based on external inputs and machine learning algorithms that predict system behaviors and adjust models accordingly." "Such Python-driven digital twins can represent infrastructure assets like bridges in riverine environments, and their parametric nature allows the same model to be adapted to different rivers by feeding in river-specific hydraulic and structural parameters."

The journal article "Evaluating the Impact of Bridge Construction on Flood Control" examines how different bridge designs and layouts affect flood control capacity and upstream water levels.[8] It states that the research "provides valuable insights and a scientific basis for developing flood control strategies, optimizing bridge design, and planning infrastructure" in flood-prone areas.[8] The study uses hydraulic simulations under different bridge configurations to assess impacts on flood behavior, showing that design dimensions and placement can be systematically varied to optimize both structural performance and flood control outcomes.[8]

This document presents HydroBID Flood model instructional tutorials to facilitate application of model components using QGIS. It is a transferable flood and river dynamics modeling tutorial, but it does not specifically focus on Python or bridge-dimensional optimization.

The article describes an optimization/simulation model for real-time flood control operation of a river-reservoir system, focusing on maintaining discharges and storage levels. It is relevant as a parameterized flood-control optimization model, but it is not specifically about bridges or Python.

The tutorial video shows how to "build a simple and practical event-based hydrologic model in Python for flood hydrograph simulation and flood forecasting" and walks through defining rainfall input files, catchment parameters, and flow routing methods in Python code. The presenter explains that users can "change the flow lengths and also the catchment data" and other parameters to calibrate the model for their own basin, demonstrating that a Python-based flood model can be **adapted to different catchments or rivers** by supplying relevant input parameters.

"IntelliBridge is an AI-powered system for bridge maintenance optimization that combines advanced predictive modeling, optimization techniques, and integration with existing bridge management systems." "The proposed AI framework leverages machine learning models—Decision Trees, Random Forest, and Artificial Neural Networks—and integrates genetic algorithms to develop optimal maintenance strategies that balance cost-effectiveness and improvement in performance." "Because the decision-support framework operates on parameterized inspection and condition data, it can be generalized to bridges in different geographic locations, including river-crossing bridges used for flood control, by inputting the relevant structural and environmental parameters into the Python-based models."

The material says Python 3.6.8 and required libraries are used to automate data exchange between hydrological, inundation, and optimization models. This is evidence of Python-based flood-control optimization workflow, but it is only lecture material rather than a published article.