Water quality assessment in lentic water bodies relies on crucial indicators such as Total Suspended Matter (TSM), which is influenced by factors like sediment transport, deposition, and resuspension. A significant research gap regarding the immediate and short-term effects of major seismic events on TSM concentrations underscores the need for alternative modern methods. In this study, we employ a remote sensing technique to analyze the impact of the devastating 2023 earthquakes in Turkey on water quality, with a specific focus on TSM levels. Utilizing the Case 2 Regional Coast Color (C2RCC) processor, we analyze data collected from Sentinel-2, Landsat-8, and Landsat-9 between November 2022 and May 2023. Our findings reveal noticeable increases in TSM concentrations in reservoirs located near the epicenters of the earthquakes, such as Atat & uuml;rk Dam, Sir Dam, and Aslanta & scedil; Dam. Conversely, more distant water bodies like Hirfanli Dam and Lake E & gbreve;irdir demonstrate decreases in TSM levels. In the aftermath of seismic events at the Atat & uuml;rk, Aslanta & scedil;, and Sir Dams, notable shifts in TSM concentrations were observed. Post-earthquake, TSM concentrations surged by 189%, 175%, and 51% at the Atat & uuml;rk, Aslanta & scedil;, and Sir Dams, respectively, with corresponding increases of 51% and 105% in standard deviations, based on a statistical comparison of pre- and post-earthquake TSM estimates. A non-parametric Mann-Whitney U test was applied to compare pre- and post-earthquake TSM distributions. Although the differences were not statistically significant at the 0.05 level, the effect sizes indicated moderate to large changes in reservoirs located near the earthquake epicenters. These results indicate that seismic events can substantially alter TSM concentrations in reservoirs located near the epicenters.
Deeply buried underground structures in liquefiable sand layers possess the potential for liquefaction-induced uplift under main-aftershock sequences. However, understanding of this risk is limited because prior investigations have largely been confined to either shallow burial conditions or analyses of single seismic events. To address this gap, this study employs a three-dimensional finite difference model incorporating fluid-solid coupling to systematically investigate the influence of burial depth (ranging from 10m to 40m) on the displacement and internal forces of structures subjected to the main-aftershock. The effects of multiple aftershocks and soil relative density are also examined. The research results validate the known phenomena that multiple low-intensity aftershocks and an increase in relative density can enhance the liquefaction resistance of sandy soils and confirm that the degree of soil liquefaction beneath structures is a key factor controlling their uplift behaviors. In addition, the key findings include: (1) the soil-structure interaction increases with the depth of the structure, the influence range of the structure on the surrounding sand and liquefaction resistance also increase accordingly; (2) for a given set of soil and seismic parameters, a critical burial depth exists at which the structure experiences negligible net uplift or settlement after the seismic sequence; (3) the structural internal forces significantly increase after a strong mainshock and may remain at levels close to or exceeding the design values, posing a risk of structural damage. These findings can have direct implications for engineering practice. The identified critical burial depth can provide a quantitative guideline for optimizing the design of underground structures in liquefiable sites, while the sustained elevation of structural internal forces suggests a potential long-term risk of creep failure that should be addressed in design codes and safety evaluations. Consequently, this study demonstrates that a rigorous seismic design should account for the full effects of main-aftershock sequences and cannot simply assume that greater burial depth invariably leads to improved safety.
Earthquake detection systems utilize sophisticated algorithms and seismic data to monitor and analyze seismic activity in real-time, which provides early warnings that reduce potential damage. However, these systems face several challenges, such as accurately forecasting seismic events, filtering out background noise from actual seismic signals and maintaining consistent performance in areas with limited infrastructure or sparse sensor networks. These limitations highlight the need for more robust and efficient detection models that can effectively process seismic data and improve detection reliability. To address these challenges, a new model called Levenberg–Marquardt DenseNet (LM_DenseNet) is proposed for detecting earthquakes. Initially, the input data is passed to the data transformation phase, which is done by the Box–Cox transformation. Next, a feature fusion technique is applied using a Siamese Convolutional Neural Network (SCNN) with hybrid distance measures that integrate city block and Bhattacharyya distances. Finally, an earthquake is detected using the LM_DenseNet model, which is the incorporation of Levenberg–Marquardt (LM_Net) with DenseNet. The effectiveness of LM_DenseNet is examined by considering the metrics, like accuracy, sensitivity, specificity and F-measure with superior values of 96.87%, 96.17%, 96.72%, and 96.22%.
The High Atlas Mountains constitute a major intracontinental orogen shaped by the long-term convergence between the Nubian and Eurasian plates. However, the present-day seismotectonic deformation and stress regime of the Western High Atlas remains insufficiently constrained. Here, we analyze relocated earthquakes recorded between 2014 and 2023, with local magnitudes ranging from 3.3 to 6.8, along with focal mechanism solutions and stress tensor inversion, to develop an updated seismotectonic interpretation of the Western High Atlas and to better constrain the mechanisms governing crustal deformation. The focal mechanism solutions indicate a predominance of oblique-reverse faulting, with secondary strike-slip and local normal components. Stress tensor inversion yields a nearly horizontal, approximately N–S to NNW–SSE oriented maximum compressive stress axis ([Formula: see text], consistent with the inferred regional SHmax direction. These results support active compressional to transpressive deformation within the Western High Atlas and suggest that crustal shortening is partly accommodated by the reactivation of inherited structures. The results indicate that strain partitioning is controlled by the interaction between far-field Nubia–Eurasia convergence and local crustal heterogeneities. This study provides new constraints on the active tectonic framework of the Western High Atlas and contributes to seismic hazard assessment in this intraplate mountain belt.
Accurate estimation of dynamic earth pressure on basement walls in liquefiable soil is essential for seismic safety, yet remains challenging because of complex soil–structure interaction. This study proposes a practical method for estimating dynamic earth pressure under liquefaction conditions by decomposing the dynamic thrust into non-fluctuating and fluctuating components, each primarily governed by the free-field excess pore water pressure ratio and the basement base acceleration. Six 1 g shaking table tests were conducted on model buildings with three different heights and two basement support conditions (fixed and embedded) to examine the seismic performance of free-field soil, buildings, and basement walls. Free-field soil displacement was initially aligned with basement floor displacement during early shaking but decreased significantly once liquefaction developed. Dynamic thrust was consistently out of phase with basement displacement and increased markedly under liquefied backfill compared with dry backfill. Under liquefaction, the pressure distribution consistently exhibited a triangular shape regardless of building height or basement condition. The proposed method reasonably estimated dynamic thrust in liquefied soil, outperforming conventional approaches and reducing estimation errors. These findings provide a practical framework for evaluating dynamic earth pressure in liquefiable soils, contributing to safer and more rational seismic design of basement structures.
The unique period characteristics of long-period ground motions pose significant seismic threats to high-rise and super-tall structures, making the incorporation of these characteristics into response spectrum models essential for accurate seismic assessment. The ratio of peak ground velocity to peak ground acceleration (PGV/PGA) was introduced as a key indicator to characterize the period characteristics of ground motions. Based on the observed spectral features, a displacement spectrum model incorporating PGV/PGA effects was established. Statistical methods were then employed to determine the model's amplification factors. Following model verification, the effects of PGV/PGA, PGA, PGD/PGV, and damping ratio on the displacement spectra were systematically investigated. Subsequently, the proposed model was compared with the design displacement spectra specified in the code for seismic design of buildings (GB/T50011-2010), leading to relevant design recommendations. The results indicate that, in the acceleration-sensitive region, displacement spectra are positively correlated with PGA, while the influence of PGV/PGA is negligible, suggesting the period characteristics of ground motions have minimal effect here. In the velocity- and displacement-sensitive regions, for a given PGA, displacement spectra increase almost linearly with increasing PGV/PGA, highlighting the significant influence of period characteristics of ground motions. As the damping ratio increases, the displacement spectra decrease, albeit at a diminishing rate. This increase in damping ratio effectively mitigates the influence of period characteristics of ground motions. In the displacement-sensitive region, the predicted displacement spectra for long-period ground motions are consistently lower than the design values prescribed by the Chinese code GB/T50011-2010, indicating that the code's provisions are relatively conservative in this region. However, for sites with characteristic periods less than 0.45s subjected to long-period ground motions, the code may underestimate spectral demands in the acceleration- and velocity-sensitive regions. These nuanced findings highlight the need for region-dependent design considerations that account for both period characteristics and site conditions.
This study examines the advantages of finite element methods for simulating the shallow water equations using spherical Hankel (SH) shape functions. To this end, the run-up and run-down of a solitary wave over a vegetated bed is simulated, representing tsunami wave propagation over wet and dry beds. A momentum-conserving Taylor-Galerkin algorithm is developed, and the Hansen filter is implemented to eliminate numerical instabilities and spurious oscillations. The model is verified by comparing its results with experimental measurements, demonstrating that the finite element model can accurately capture wave propagation. Furthermore, the performance of the model employing SH shape functions is compared with that of the model using classical Lagrange shape functions through error metrics such as the Root Mean Square Error (RMSE). The results show that adopting SH shape functions can improve model accuracy while reducing the required number of elements, indicating their suitability as an alternative to classical Lagrange shape functions for solving shallow water problems.
Tsunamis generated by volcanic mass-failure processes pose a distinct monitoring challenge because their energy is concentrated at shorter periods than typical tectonic events. Conventional tide gauge sampling rates are often too coarse to capture these signals. This study quantifies how sampling interval controls waveform fidelity and automatic detection performance using the 2018 Anak Krakatau tsunami as a benchmark. Clean model tsunami records from three landslide-volume scenarios and three representative stations spanning contrasting bathymetric settings were resampled to practical intervals (1-60s) and evaluated using time-domain errors, spectral distortion, and detection latency with three commonly used algorithms. To emulate realistic coastal conditions, mixed records were constructed by combining tsunami signals with short-period harbor and vessel-related disturbances at multiple intensity scales, and real-time noise handling was tested using sliding-window filters. Results show that coarse sampling (15-60s) produces severe distortion consistent with undersampling and aliasing, while a 3-5s interval provides the best overall compromise between waveform fidelity and detection speed. A lightweight Moving-Average filter (optimal window 32s under low SNR) improves signal clarity while preserving dominant tsunami structure. These findings support site-calibrated high-frequency monitoring with station-tuned filtering for short-period non-seismic tsunamis.
This study employs numerical simulation methods to investigate the disaster-causing mechanisms of tsunamis in the South China Sea and their coupled effects with sea level rise. Using the GeoClaw shallow water equation model, the study simulated the impact of different sea level heights (SLH) - 0, 1.0, 1.5, and 2.5m - on tsunami wave amplitudes in the South China Sea and surrounding regions under Mw 8.0 and Mw 9.35 earthquakes. The results indicate that earthquake magnitude has a nonlinear amplifying effect on wave amplitudes, compared to an Mw 8.0 earthquake, wave amplitudes at observation points increase by 10-40 times under a Mw 9.35 earthquake. Sea level rise significantly enhances tsunami disaster intensity. For the Mw 9.35 earthquake, wave amplitude differences accumulate with increasing sea level, with wave amplitude differences reaching 0.2 m in central Philippines and along the Chinese mainland coast. Tsunami hazard exhibits spatial heterogeneity, with the largest wave amplitudes near Taiwan Island, northern Philippines, and the northern South China Sea, while the central and southern South China Sea exhibit smaller wave amplitudes. Coastal cities face significantly increased flood risks under sea level rise scenarios. This study reveals the patterns of sea level rise's impact on tsunami hazards in the South China Sea, recommending optimized monitoring systems and enhanced coastal infrastructure resilience for high-risk areas, providing scientific basis for hazard prevention and long-term planning in the South China Sea region.
Traditional tsunami prediction relies on binary classification using machine learning, which lacks physical interpretability and extrapolation capability. This study introduces a novel probabilistic framework for tsunami genesis characterization using Multivariate Extreme Value Theory (MEVT) and Archimedean copula dependence modeling. We analyze an expanded dataset of 1002 significant earthquakes (M >= 6.5) drawn from the USGS/NEIC catalog spanning 1976-2022 and supplemented with moment-magnitude conversions following [Scordilis, E. M. [2006] "Empirical global relations converting MS and mb to moment magnitude," J. Seismol.10(2), 225-236], yielding 391 confirmed tsunami events (39.0%). Generalized Extreme Value and Generalized Pareto distributions are fitted to the marginal parameters; negative shape parameters (xi GEV=-0.452) confirm light-tailed magnitude behavior consistent with tectonic fault-dimension constraints. Among three competing Archimedean families, the Clayton copula provides the best fit for magnitude-significance dependence (theta=2.367, AIC =-725.17, AIC weight =98.4%) with strong lower tail dependence (lambda L=0.746, Kendall's tau=0.542), while magnitude-depth and depth-significance pairs display near-independence best described by the Frank copula. These bivariate structures are embedded in a trivariate nested Gumbel-Hougaard copula for complete joint modeling. A closed-form limit state equation is derived via the First Order Reliability Method (FORM): Mcritical=10.71-35.96(1/D)-0.0025S, where inverse depth contributes 26.4% of total variance. Cross-validation yields AUC =0.607 +/- 0.038, Brier score =0.233, and ECE =0.031. This framework provides engineers with a physics-based, directly calculable tool for probabilistic tsunami hazard assessment (PTHA), bridging structural reliability theory with seismological practice.
The Shandong Seismic Network is a key component of the China Earthquake Networks Center and one of the most advanced regional seismic monitoring systems in the country. It comprises four complementary observational subnetworks: (1) a traditional broadband seismic network consisting of 124 stations installed on bedrock or in deep boreholes; (2) an earthquake early warning (EEW) strong-motion network with 238 strong-motion sensors deployed on bedrock or shallow soil; (3) an EEW micro-electro-mechanical systems (MEMS) network comprising approximately 1230 low-cost sensors mounted on building floors or walls; and (4) a seismic array of 77 broadband seismometers installed in shallow boreholes on stable ground. Together, these four subnetworks operate a total of 1669 instruments, providing multi-scale observational data that are critical for detailed characterization of source rupture processes during moderate-to-strong earthquakes. In this study, we analyze the MW 5.5 Pingyuan earthquake that struck Shandong Province on August 6, 2023 (Beijing Time, UTC+8) using data from this integrated network. Our results show that: the high-density MEMS network is sufficient for rapid post-event inversion of the rupture process, yet its inversion accuracy is somewhat limited; the strong-motion network enables stable and efficient rapid finite-fault inversion; and multi-source data fusion yields the most reliable post-earthquake source rupture model. These capabilities support quasi-real-time (within 30 min of the origin time), rapid, and refined imaging of earthquake rupture processes, offering critical guidance for emergency response and optimization of regional seismic networks.
For estimating site response spectra during an earthquake, a Kriging model-based approach is proposed, which uses typical Ground Motion Prediction Equations (GMPEs) as a baseline and epsilon (epsilon) as a correction factor. The Kriging model is constructed to predict epsilons based on five input parameters: rupture distance (Rrup), epicenter distance (Epi), the clockwise angle (Az), the averaged shear-wave velocity in the top 30m (Vs30) and altitude of the site (Ha). A case study is conducted on the 2010 Darfield Mw 7.0 earthquake. The results demonstrate that, even with limited input data, the proposed method yields lower Mean Squared Error (MSE) values compared to the median estimates derived from standard GMPEs. Meanwhile, the method captures the "bumpy" shape of an individual site response spectrum. Prediction accuracy is high when the target site is close to the sites used for training the Kriging model, while deviations increase when the target site is far from the training sites.
Climate change has intensified the need for accurate and interpretable models to assess spatio-temporal climate risks. Existing approaches often fall short in capturing complex interactions across spatial and temporal scales, limiting their flexibility and predictive performance. This study introduces the Wavelet-Enhanced Temporal Fusion Transformer (W-TFT), a novel deep learning framework that integrates Discrete Wavelet Transformation (DWT) with the Temporal Fusion Transformer (TFT) to improve climate risk prediction. DWT decomposes climate time-series data into multi-resolution components, enabling the model to distinguish between short-term variations and long-term trends. The architecture employs attention mechanisms, gated residual networks, and interpretable covariates — including physical and socio-environmental factors such as land use and population density — to selectively focus on significant features. Additionally, W-TFT incorporates uncertainty quantification, allowing for reliable multi-horizon forecasting essential for informed decision-making under climate uncertainty. Experimental evaluations show that W-TFT outperforms conventional models, achieving low RMSE (0.1254), MAE (0.0073), and MSE (0.0157), while effectively modeling complex spatiotemporal dependencies. The proposed framework offers scalable and adaptive capabilities for applications in climate monitoring, early warning systems, and sustainable policy development, making it a valuable tool for mitigating climate risks across diverse geographic contexts.
The failure of elevated liquid storage tanks during severe earthquakes can have direct or indirect impacts on public safety. The significance of their safe performance even after destructive earthquakes and the potential for their operational use underscore the necessity for appropriate seismic design. Hence, seismic isolation, specifically base isolation, has gained attention as a seismic control method to reduce damage to these infrastructures by increasing their period of vibration. One prevalent type of seismic isolator used for tanks and other structures is the friction pendulum system (FPS) isolator. However, due to its fixed period or frequency, it may be susceptible to resonance effects during long-period earthquakes. This research explores a solution by investigating the variable-curvature friction pendulum isolator (VFPI) as an alternative. This isolator type exhibits behavior similar to FPS isolators at low excitations and transforms into a purely frictional system at high excitations. The study proposes optimizing this VFPI by introducing an appropriate objective function to minimize the acceleration transmitted to the superstructure, thereby improving the dynamic performance of the elevated storage tank. The research employs a metaheuristic algorithm for optimization and evaluates the effectiveness of the proposed isolator through time-history analysis using the state-space procedure under various ground-motion records. Results, particularly under long-period ground motions, indicate a substantial reduction in the dynamic response of an elevated liquid storage tank equipped with the optimized VFPI. This underscores the potential of the proposed solution in enhancing the seismic resilience of liquid storage tanks.
Urban resilience is threatened by seismic activity, especially in rapidly urbanizing areas with infrastructure vulnerabilities that increase earthquake risk. Traditional mitigation strategies ignore natural seismic energy dissipation and focus on base isolators and reinforced structures. Urban Green Infrastructure (UGI) has been extensively studied for climate adaptation, flood control, and ecological restoration, but not seismic risk reduction. Vegetation-root interactions, soil reinforcement, and terrain modifications reduce seismic waves, increase shear strength, and prevent liquefaction at lower cost and environmental impact than traditional engineering solutions. Computational modeling, field data, and case studies evaluate UGI's earthquake mitigation. The Finite Element Analysis (FEA) and Hilbert-Huang Transform (HHT) simulations show Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), and spectral acceleration reductions across UGI configurations. Trees reduce PGA by 38.25% and improve soil cohesion and stability. Seismic refraction tomography and Ground Penetrating Radar (GPR)-based root mapping measured soil cohesion, root density, and S-wave velocity from 50 seismic events in three earthquake-prone urban areas. Experimental results show that UGI-integrated landscapes deform less and recover faster from earthquakes than impervious urban surfaces. Further research shows fault slip and dip angles affect UGI's directionality. Optimized vegetation buffers perpendicular to strike-slip faults and deep-rooted vegetation in normal/reverse fault areas reduce wave scattering and soil instability. Urban planning case studies show UGI reduces landslides and soil erosion. This study proposes AI-driven parametric modeling to optimize UGI placement despite land and policy constraints using real-time seismic hazard assessments. It suggests green infrastructure in municipal seismic zoning and building codes. Geophysics, landscape ecology, and urban design show how nature-based earthquake mitigation can make cities seismically resilient and sustainable.
A simplified computation method is suggested to compute the ductility performance of partially concrete-filled steel box piers with T-shaped stiffeners. The 3D elastoplastic finite element model for the steel piers under vertical and lateral loadings is verified numerically. Subsequently, numerical studies are performed to understand the influence of normalized stiffener and pier slenderness ratios, normalized flange width-to-thickness ratio, and axial compression ratio on the ductility behavior and ultimate strength of the piers. To meet the needs of actual bridge design, the 2D-beam model is employed to investigate the ductility behavior of the pier. The computational results show that the proposed 2D-beam model can predict the ductility capacity of the piers with T-shaped stiffeners more quickly than the 3D-shell model. Further, the 2D-beam model can predict the ductility behavior of the piers with an acceptable error when local buckling occurs in the hollow steel pipe above the infill concrete. However, when local buckling occurs on the flanges and webs near the pier bottom, a correction coefficient must be introduced to modify the ultimate strain criterion of the stub column. The modified ductility ratio obtained from the 2D-beam model after introducing a correction coefficient is consistent with the results of the 3D-shell model, and all data points fall within the +/- 10% error line. The proposed model is expected to provide a more efficient and convenient computational method for practical bridge design.
Tsunamis are among the natural disasters that cause maximum destruction and devastation of both life and property; an early prediction of such events can help mitigate their impacts. The existing methods for tsunami prediction suffer from some serious limitations in accuracy and real-time adaptability. In this line, we propose a hybrid tsunami prediction framework based on signal processing, IoT data, and advanced time-series modeling using a SARIMAX-GRU model. The workflow involves data collection, pre-processing, feature extraction, and model training, to ensure effective prediction of tsunami events. It's astonishing accuracy and same precision, recall, and F1-score of 98.25% place the proposed model among the best in predicting tsunami occurrences, when compared to other models such as SVM, RF, and KNN. The contribution of this work will offer tsunami prediction and risk management researchers a comprehensive approach in integrating the seismic and hydrological data with advanced deep learning methodologies.
All-indoor substations, where all the electric equipment is set in one building, have been progressively promoted and utilized. Under seismic excitation, both the equipment and the building structures experience dynamic responses, which influence each other. In design practice, floor-mounted electrical equipment is often treated as nonstructural elements and modeled as vertical loads. However, per ASCE 7-23, if such equipment significantly affects the building's lateral stiffness, it should be modeled with its stiffness and mass considered together as a nonbuilding structure. To investigate the impact of equipment-structure interaction on the dynamic response of the system, a 220kV indoor substation was taken as the research subject. A frame-element model of the equipment building and a solid finite element model of the floor-mounted Gas-Insulated Switchgear (GIS) were established. Modal analysis and seismic time-history response analysis were conducted. Three seismic ground motion records (El Centro, Tangshan-NS, and an artificial Lianzhou-1 wave) are used in the time-history analysis, scaled to peak ground accelerations (PGAs) of 0.2g and 0.3g to represent frequent and rare earthquake scenarios, respectively. The results indicate that the interaction between the structure and equipment significantly affects the seismic response of both the structure and the equipment. Under rare earthquake conditions, equipment-structure interaction increases the floor acceleration response of the building, with a maximum increase of 33%. The higher the floor where the equipment is located, the greater the impact of interaction on the structural dynamic response. After seismic isolation for the building, the structural acceleration response and base shear are significantly reduced. However, the equipment-structure interaction still has a notable effect on the seismic response of the structure, resulting in a maximum increase of 12.5% in the story shear.
To simplify the construction of steel-reinforced concrete structures while maintaining structural performance, this paper proposes a new type of tied steel-engineered cementitious composite (ECC) structure with tie bars. The reinforcement cage is eliminated to improve constructability, ECC material is used to replace concrete, and tie bars are set between the steel flanges to maintain structural performance. Through a quasi-static test comparison of specimens with and without tie bars, it was found that the cumulative energy dissipation of the specimen with tie bars increased by 13.7%. The column foot exhibited flexural failure without ECC spalling, demonstrating excellent ductility and seismic performance. Finite element analysis indicates that the tie bars effectively suppress flange buckling and enhance core area confinement. Parameter variation analysis via finite element modeling shows that an increase in the axial load ratio reduces bearing capacity and energy dissipation. When the axial load ratio increases from 0.1 to 0.4, the bearing capacity decreases by 3.6%, and ductility decreases by 40.2%. The strength of the tie bars has a minimal impact on seismic performance. Reducing the tie bar spacing significantly improves bearing capacity and energy dissipation capacity. When the tie bar spacing is reduced from 200mm to 50mm, the bearing capacity increases by 10.7%. The steel-ECC structure with tie bars can maintain good seismic performance while simplifying construction.
This study investigates the dynamic property variations of a reinforced concrete (RC) elevated water storage tank subjected to sequential structural modifications: steel bracing retrofitting and ground floor partition wall addition. Ambient vibration surveys (AVS) and finite element analysis (FEA) were employed to characterize the structure across three distinct stages: original, retrofitted, and infill-walled. Due to the circular configuration of the system, dynamic properties were effectively represented in the cylindrical coordinate system, providing a specialized framework for characterizing radial and tangential modal behaviors. Both experimental and numerical analyses consistently identified the first three modes as radial translation, tangential torsion, and radial double curvature, respectively. AVS demonstrated that the first three modal frequencies shifted from 0.755, 1.00, and 2.905Hz in the original state to 1.56, 3.20, and 5.75Hz following the retrofit. These trends were corroborated by numerical simulations, which yielded shifts from 0.75, 0.86, and 3.24Hz to 1.71, 2.46, and 6.66Hz, respectively. The substantial increase in the second modal frequency, in particular, confirms that the bracing system was most effective in enhancing torsional rigidity. The addition of partition walls was also considered, and the impact on the dynamic properties was evaluated. By providing high-fidelity frequency increase factors across both experimental and numerical domains, this study establishes a quantifiable performance metric for rigidity enhancement in water storage systems. The strong correlation between the AVS and FEA results confirms the reliability of the performance metrics, thereby bridging the gap between theoretical analysis and the actualized retrofit of aged utility infrastructure.
