Introduction: Tackling Analysis Bottlenecks in Large RISA Models

Structural engineers frequently face significant delays when running analyses on large RISA models. These delays are not merely a nuisance—they can disrupt project timelines, reduce productivity, and increase costs. Large models, especially those with thousands of members, complex connections, or nonlinear behavior, can take hours or even days to solve if not carefully configured. However, the time required for analysis can be drastically reduced through strategic modeling choices and a thorough understanding of RISA’s solver capabilities. This article provides actionable techniques to minimize analysis time without sacrificing the accuracy and reliability that engineering decisions depend on. By implementing these practices, you will not only speed up your current projects but also build more efficient modeling habits for the long term.

Optimize Model Geometry and Detailing

Eliminate Non‑Structural Elements

One of the easiest ways to reduce analysis time is to remove any element that does not contribute to the structural load path. Curtain walls, architectural cladding, and decorative features should be omitted from the model. If their weight is required for loading, apply it as uniform distributed loads (UDLs) or point loads rather than as discrete elements. This technique drastically reduces the number of degrees of freedom the solver must process.

Simplify Beam and Column Representations

Use the coarsest subdivision possible for beams and columns. For instance, model continuous beams as single members with appropriate releases rather than dividing them at every intermediate support. Similarly, columns that are continuous through several floors should be modeled as a single member spanning the entire height, with moment releases at floor levels where pinned connections exist. This reduces the number of nodes and elements, directly speeding up the stiffness matrix assembly and solution.

Leverage Symmetry and Repetition

If your structure is symmetric about one or more axes, model only a symmetric quadrant or half. Apply appropriate boundary conditions along the symmetry planes. Many large structures, such as industrial buildings or towers, exhibit regular repetition. Using RISA’s ability to copy and mirror geometry can cut model size in half or more, cutting analysis time proportionally.

Use Rigid Diaphragms Where Appropriate

For concrete floor slabs or metal decks with sufficient stiffness, apply rigid diaphragm constraints at each floor level. This prevents the solver from computing in‑plane deformations for every slab node, condensing many degrees of freedom into a single master node per diaphragm. The result can be a 20–40% reduction in solution time for multi‑story models.

Apply Strategic Mesh Density

Refine Only in Areas of Interest

The mesh density directly affects the number of elements and nodes, which in turn impacts memory usage and compute time. In large models, it is tempting to use a uniform fine mesh everywhere, but this is rarely necessary. Use coarse meshes (larger elements) in areas where stresses are largely uniform, such as the middle spans of long beams or the central regions of slab panels. Apply fine meshes only around point loads, openings, column supports, and other locations where stress gradients are steep. RISA’s Automatic Mesh Generation tool can be set to produce a variable mesh, but manual oversight ensures that resources are focused where they matter most.

Edge vs. Surface Meshing

For plate and shell elements, consider using edge‑based meshing for slab edges that need refined definition only at boundaries. In many cases, a uniform mesh of 2‑foot or 3‑foot squares is sufficient for gravity load analysis. For seismic or wind drift checks, a slightly finer mesh may be warranted, but it is rarely necessary to go below 1‑foot elements in planar surfaces unless you are checking punching shear or detailing studs.

Use Adaptive Meshing Techniques

RISA supports adaptive meshing in some analysis types, which automatically iterates the mesh density based on convergence criteria. While adaptive meshing adds some overhead due to the iterative process, it often results in a final mesh that is much smaller than a uniform fine mesh. Enable adaptive meshing only when the accuracy of stress contours is critical, and set a reasonable maximum iteration limit to avoid runaway computation.

Divide and Conquer with Model Grouping and Submodels

Separate Gravity from Lateral Systems

A highly effective strategy is to split the analysis into separate models for gravity loads and lateral loads. The gravity model includes all beams, columns, and slabs, but only applies dead and live loads. The lateral model focuses on the lateral‑force‑resisting system (LFRS) such as shear walls, braces, or moment frames. By isolating the lateral system, the number of elements and constraints is reduced, allowing the solver to converge faster. The results can then be superimposed using load combinations.

Use RISA’s Submodel Capabilities

For very large structures, break the model into physically distinct zones—for example, floors, towers, or expansion joints. Analyze each zone as an independent RISA model, then manually combine boundary forces to check overall compatibility. Many engineering firms use this approach for high‑rise buildings or industrial facilities with multiple independent frames. The total time spent is often less than solving a single monolithic model because each submodel fits comfortably in memory and solves in minutes.

Leverage Grouping for Post‑Processing

Even if you do not split the model, take advantage of RISA’s Member Grouping and Section Sets. When you define a group of members sharing the same properties, the solver can reuse computation for similar elements. This reduces the number of unique stiffness formulations and speeds up both the assembly and the results extraction phases.

Fine‑Tune Solver Settings and Analysis Parameters

Choose the Right Solver Algorithm

RISA offers multiple solver options: sparse direct, iterative, and skyline. For large models, the sparse direct solver is generally the fastest and most memory‑efficient. However, if your model includes many nonlinear elements (e.g., P‑Delta, tension‑only braces), the iterative solver can sometimes converge faster because it exploits the structure’s nonlinearities. Experiment with both options on a subset of your model to determine which performs better. Remember to turn off Precision Iterations unless you need extremely high residual accuracy.

Limit Solution Outputs

By default, RISA may calculate detailed member forces, stresses, deflections, and reactions for every load combination. This can generate enormous amounts of data and slow down the solver as it writes to disk and memory. Disable output that you do not immediately need. For example, if you are only checking strength, you can suppress deflection calculations for service load combinations. Similarly, turn off Member Stress Recovery at intermediate increments unless you require actual stress contours for code checking.

Reduce Iteration Counts for Nonlinear Analysis

For nonlinear static analysis (e.g., P‑Delta, large displacement), set the maximum iteration count to a number just above what your convergence typically requires. A common default is 30–50 iterations, but many models converge in 5–10. Lowering the limit saves time when a solution is unlikely to converge—the solver stops early and allows you to investigate stability issues rather than spinning through dozens of wasted cycles.

Disable Unnecessary Load Combinations

Review your load combinations list and remove any that are not required by the governing code. For example, if your project does not include thermal loads, delete any combination that includes temperature. Many engineers import standard combination sets and never prune them, leading to hundreds of extraneous load cases. Each additional load combination multiplies the solver’s work. Keep the combination list lean.

Leverage Hardware and Parallel Processing

Enable Multi‑Core Processing

RISA’s solver can utilize multiple CPU cores. Ensure that parallel processing is enabled in the application configuration. On a machine with 8 or more cores, the solution time can drop by a factor of 3–5 compared to single‑core execution. Note that the speedup is not perfectly linear due to memory bandwidth limitations, but it remains one of the most impactful hardware interventions.

Upgrade Hardware Strategically

The most important hardware upgrade for large RISA models is RAM. Models with hundreds of thousands of elements can consume many gigabytes of memory. If your system runs out of physical RAM and begins swapping to disk, analysis time can increase tenfold. A minimum of 16 GB is recommended for moderately large models; 32 GB or 64 GB is better for complex 3D structures. Also, use a solid‑state drive (SSD) for your RISA project directory. The solver reads and writes temporary files during the solution; an SSD can reduce I/O bottlenecks by 80% compared to a traditional hard disk.

Close Unnecessary Applications

Before launching a large analysis, close web browsers, email clients, and other memory‑hungry programs. Background applications compete for CPU and memory, causing the solver to run slower. On Windows, use Task Manager to verify that CPU and RAM usage are low before starting the solve.

Adopt Workflow Efficiency and Automation

Develop a Systematic Validation Process

Instead of running one enormous analysis at the end of the modeling phase, perform incremental checks. After every ten members or every one floor, run a quick linear static analysis to catch errors early. This prevents a long solve from failing due to an ill‑conditioned joint or a restraint conflict. A common practice is to run a gravity check on the raw model before adding lateral loads, then run lateral checks on a copy of the validated gravity model.

Create a Component Library

Save frequently used subassemblies—such as truss panels, stair landings, or roof frames—as separate RISA files. When you need to incorporate them into a new model, import the component rather than rebuilding it. This reuse not only saves modeling time but also ensures that the components are already optimized for performance.

Use Scripting and Macros

RISA supports a powerful scripting interface (RISA‑API) that allows you to automate repetitive tasks like applying loads, generating combinations, or extracting results. Investing a few hours to write a VBA or Python script can shave days of manual effort over the life of a project. For example, a script that automatically applies wind loads based on ASCE 7 parameters can reduce model setup time by 50%.

Summary of Key Actions

  • Simplify geometry by removing non‑structural elements, using rigid diaphragms, and modeling symmetry.
  • Optimize mesh density locally around stress concentrations; use adaptive meshing selectively.
  • Split large models into gravity/lateral submodels or physically distinct zones.
  • Adjust solver settings – choose the right solver, limit iterations, prune load combos, and suppress unneeded output.
  • Upgrade and configure hardware – enable multi‑core, add RAM, and use SSDs.
  • Automate validation and reuse component libraries to streamline the overall workflow.

Applying these tips consistently will reduce analysis time in large RISA models from hours to minutes, enabling faster design iterations and more confident decisions. Regularly review your modeling practices and stay updated on RISA’s evolving solver features. For further reading, refer to RISA’s official support knowledge base for performance‑related technical articles, and explore the AISC Engineering Data Library for proven modeling techniques that complement these strategies.