A. Pimanmas

Stress-Driven Two-Phase Nonlocal Model for Nanobars Embedded in Elastic Substrates with Surface Effects

This paper develops a nonlocal bar–substrate medium model to study the axial (longitudinal) response of nanobars embedded in an elastic foundation. Small-scale behavior is represented through a two-phase mixture, stress-driven, nonlocal integral formulation. To reflect the surrounding medium and surface-related size ef...

Experimental investigation of the mechanical properties of recycled aggregate concrete reinforced with water hyacinth fibres for sustainable construction

Physics-informed neural networks for early-warning fatigue damage detection and localization in a steel railway bridge using sparse ambient monitoring data

This study develops a strain-based monitoring approach, supported by a physics-informed neural network (PINN), for early detection of fatigue damage in a steel railway bridge and provides a reproducible, low-cost, and interpretable basis for proactive bridge maintenance.

Interpreting Satellite Radar for Bridges: Integrating MT-InSAR with Physics-Based and Data-Driven Models for Scalable Structural Health Monitoring

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has matured into a credible, non-contact technique for monitoring bridge deformation from individual structures to regional portfolios. The main challenge for routine engineering use is no longer measuring millimetre-scale line-of-sight (LOS) displace...

Data-driven modeling of compressive strength in sustainable self-compacting concrete incorporating recycled aggregates using ensemble learning techniques

Abstract This study develops a robust framework for estimating the compressive strength of self-compacting concrete (SCC) incorporating recycled aggregates using supervised machine learning (ML) techniques. A comprehensive experimental database comprising 582 concrete mix designs was used, encompassing diverse input va...