Civil and Energy Research

Seismic response comparison of an RC frame structure with fixed base and lead rubber bearing isolation

Moneef Mohamed Elobaid Musa — 2025-11-20

In earthquake-prone regions, ensuring structural resilience is crucial, particularly for essential buildings such as hospitals, schools, and commercial complexes. This study investigates the seismic performance of a G+12 story reinforced concrete (RC) building using ETABS 2022 and evaluates the effects of base isolation with Lead Rubber Bearings (LRBs). The model was analysed in both fixed-base and base-isolated configurations under seismic loading, to assess key parameters, including inter-story drift, story displacement, shear, stiffness, and natural periods. The results indicated significant improvements in the base-isolated model, with inter-story drifts reduced by up to 68% in the X direction and 69% in the Y direction, demonstrating a marked reduction in lateral deformations. Although absolute story displacements increased, this was due to the flexibility incorporated into the isolation system, which enabled the isolators to absorb and dissipate seismic energy, thereby reducing forces on the superstructure. Furthermore, story shear was reduced by 29–31% across all stories, and story stiffness was enhanced by up to 318% at lower stories, contributing to improved structural stability. The natural periods of the building’s vibration modes increased, with the fundamental period lengthening by 45.5%. This shifted the building’s response away from the predominant seismic frequencies and reduced seismic acceleration demands. However, the study assumes ideal behavior of the LRBs without accounting for potential long-term degradation. Despite increased displacements, the reduction in base shear and drift indicates significant cost savings in repairs and maintenance, providing insights into the cost-effectiveness and benefits of base isolation in earthquake-prone areas.

Study on structural response under vibration load with multiple degrees of freedom

Deyu Kong — 2025-11-23

In recent years, the frequency of seismic activity has increased, highlighting the importance of evaluating the response of structures to varying seismic intensities to ensure their safety. This study explores the stochastic incremental dynamic analysis (IDA) of multi-degree-of-freedom (MDOF) structures under unsteady seismic excitation. To address the complexity of unsteady seismic events, stochastic precesses are integrated into the IDA to generate stochastic seismic records that adhere to the elastic response spectra specified in Eurocode 8. A Monte Carlo simulation approach was employed to generate seismic waves using MATLAB, followed by a nonlinear time-history analysis in ABAQUS to evaluate the structural response, particularly focusing on the maximum interstory drift ratio. The findings indicate that (1) at low Peak Ground Acceleration (PGA) levels, the structure exhibits a minimal risk of failure. However, the risk of structural failure escalates significantly as the PGA increases, particularly beyond 0.4g. (2) The study also identifies gaps in current seismic analysis practices, especially the need for more robust stochastic IDA applications and the consideration of non-smooth excitations. This research offers a more comprehensive understanding of the seismic performance of MDOF structures and provides valuable insights for enhancing seismic design and risk assessment. Nevertheless, the study acknowledges certain limitations, such as the use of simplified structural models and the constraints imposed by computational time and suggests that future research should focus on more sophisticated modelling and simulations with larger sample sizes.

A review of tunnelling caused ground surface settlement prediction with artificial intelligence method

Zekun Zhu — 2025-11-04

The accuracy of surface settlement predictions, which aim to limit theoretical, numerical and experimental simulation errors, is influenced by several factors, including parameter values, assumption conditions and other limitations. However, the recent introduction of machine learning (ML) and deep learning (DL) has provided new ideas for surface settlement prediction. In this paper, the advances of ML and DL in surface settlement prediction are systematically reviewed. The classification of surface settlement prediction methods is first conducted based on the principles of commonly used ML and DL algorithms, including maximum surface settlement prediction and surface settlement time series prediction. Existing studies are then analysed, and common methods for improving prediction accuracy are presented. Finally, the performance of common ML and DL algorithms in predicting surface settlement is compared using the Kunming dataset. The study then draws conclusions based on the results of the comparative studies and literature research, highlighting the impact of dataset quality and feature selection on the generalisation ability of prediction models and the real-time prediction ability of existing studies.

Planar optical fibre sensor systems for real-time structural health monitoring of advanced composite structures

Elias Randjbaran — 2025-11-07

Structural health monitoring (SHM) of composite materials is critical for ensuring the reliability and longevity of high-performance engineering systems. This review comprehensively examines the advancements, challenges, and applications of flat optical fibre sensors (FOFS) for real-time strain monitoring in composite structures. Traditional electrical strain gauges and piezoelectric sensors face limitations in multiplexing, electromagnetic interference (EMI), and integration within composite layups. In contrast, FOFS offer unique advantages, including high spatial resolution, compatibility with composite manufacturing processes, and immunity to EMI. This paper analyses the working principles of FOFS, their fabrication techniques, integration methodologies, and signal interrogation systems. Case studies from aerospace, civil engineering, and renewable energy sectors underscore their practical efficacy. Challenges such as signal attenuation, temperature cross-sensitivity, and long-term durability are critically evaluated. The review concludes with future directions, including nanotechnology-enhanced sensors and machine learning-driven data analytics.

A Promising World of Civilization

Yong Yuan — 2025-09-03