An Artificial Generation Approach for Spatially Variable Seismic Ground Motions Compatible with Algerian Design Response Spectra Using Real Records: The Role of Segmentation Techniques

Why It Matters

By providing code‑compatible, spatially variable ground motions, the approach enables more reliable seismic design for infrastructure in regions lacking dense strong‑motion networks. This enhances safety assessments and reduces reliance on limited recorded data.

Key Takeaways

• New algorithm creates spatially variable accelerograms.
• Ensures compatibility with Algerian design spectra.
• Iterative adjustment matches target response spectrum.
• Addresses lack of local recorded data.
• Supports bridge seismic analysis.

Pulse Analysis

In many seismically active regions, engineers struggle to obtain recorded ground motions that reflect local geology and the spatial variability needed for long structures. Traditional reliance on recorded accelerograms often falls short due to sparse station coverage, especially in North Africa where the Algerian design response spectra are the governing standard. Artificial generation fills this gap by synthesizing time histories that mimic the statistical and physical attributes of real earthquakes while offering control over site‑specific parameters.

The newly proposed algorithm advances the state of the art by integrating segmentation techniques that partition a region into zones with distinct seismic characteristics. Within each segment, the method produces non‑stationary accelerograms whose amplitude and frequency content evolve over time, mirroring the complex nature of real ground motions. An iterative spectral matching process then fine‑tunes these records to align precisely with the Algerian design response spectra, ensuring regulatory compliance without sacrificing realism.

For bridge engineers and designers, the ability to simulate spatially correlated motions across multiple supports transforms performance‑based analysis. The generated suite of accelerograms can be directly fed into nonlinear time‑history models, yielding more accurate demand estimates for bearings, piers, and decks. Beyond Algeria, the framework is adaptable to other jurisdictions, offering a scalable solution for infrastructure projects where recorded data are scarce, thereby strengthening resilience across the global built environment.