Browsing by Author "Pinto, Francisco"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    33
    (33, 33) Guajardo, Benjamín; Pinto, Francisco; Astroza, Rodrigo; 33
    Soil exhibits inherent spatial variability, creating a significant source of uncertainty in geotechnical assessments. This variability becomes particularly critical when evaluating the seismic performance of infrastructure such as multi-span highway bridges, since traditional methodologies in bridge design often oversimplify soil properties by assuming uniformity. This approach, however, may lead to considerable inaccuracies in determining structural response under seismic activity. The complexity of soil–structure interaction (SSI) in such multi-span structures further exacerbates the influence of soil spatial variability on the overall structural response to seismic events. Although numerous studies have explored the impact of spatial variation in ground motions on seismic performance, a noticeable gap exists in the literature addressing soil spatial variability in the SSI modeling and its impact in the seismic response of multi-span bridges. Accordingly, this research aims to address this gap by proposing a numerical framework that integrates the inherent spatial variability of soil in SSI modeling by means of random fields theory and 3D nonlinear dynamic finite element models into the seismic performance analysis of multi-span bridges. The findings from a case study reveals a significant influence of soil spatial variability on structural response, leading to discrepancies in vulnerability assessment between different bridge components and highlighting the importance of incorporating spatial variability in soil parameters into seismic assessments of bridges. Moreover, soil variability appeared to slightly impact system-level vulnerability. Although the main conclusions are developed from a case study and are applicable to bridges with similar characteristics and seismic demand, the proposed approach can readily be applied to other bridge configurations and seismic environments.
  • Thumbnail Image
    Item
    33
    (33, 33) Pinto, Francisco; Astroza, Rodrigo; Bazáez, Ramiro; Hernández, Francisco; Navarro, Nibaldo; 33
    Deterioration mechanisms, such as chloride-induced corrosion, affect bridges in aggressive environments, making them more vulnerable to extreme events like earthquakes. Although many studies have assessed the impact of chloride-induced corrosion on the seismic vulnerability of reinforced concrete (RC) highway bridges, several gaps still need to be addressed. Accordingly, this research primarily focuses on evaluating the seismic performance of RC highway bridges in aggressive environments susceptible to chloride-induced corrosion and earthquakes. To achieve this, a probabilistic framework is employed, which incorporates uncertainties associated with corrosion progression, seismic events, and the impact of different modeling approaches for boundary condition. The framework considers the time-dependent effects of corrosion on the physical and material properties of steel and concrete in bridge columns. Monte Carlo Simulations (MCS), probabilistic seismic hazard analysis (PSHA), and record selection strategies are utilized to address the uncertainties in corrosion and seismic demand. Nonlinear dynamic models and multiple stripe analysis (MSA) are employed to obtain fragility surfaces and curves for main bridge components and the entire system under different boundary conditions. The study focuses on a five-span highway bridge with simply-supported prestressed concrete I-girder and RC multi-column bents located in Chile. The results reveal that elastomeric bearings are the most vulnerable components, exhibiting varying vulnerability levels under different corrosion exposure and boundary conditions. Abutments, although the second most susceptible, are unaffected by corrosion uncertainty in terms of seismic fragility. Bridge columns are identified as the third most vulnerable components, with the probability of exceeding slight damage state consistently increasing with more prolonged corrosion exposure. It is noted that only flexure failure mode in column is analyzed and possible shifting to shear or flexure-shear modes is not accounted for. The findings of this study underscore the exceptional resilience of Chilean highway bridge columns to seismic demand and corrosion uncertainties, contrasting with the situation in US regions where columns are more vunerable. Additionally, the study indicates that Soil-Structure Interaction (SSI) tends to reduce bridge vulnerability under combined corrosion and earthquake effects. The insights obtained from this study and the proposed framework can inform the development of maintenance programs based on bridge performance expectations and enhance the seismic resilience of bridge systems worldwide.
  • Thumbnail Image
    Item
    33
    (33, 33) Pinto, Francisco; Astroza, Rodrigo; Pizarro, Alonso; Bazáez, Ramiro; Hernández, Francisco; 33
    Bridges are vital infrastructures but face compounding risks from scouring and seismic events. Although recent studies have explored these individual and overlapping hazards, they often utilize deterministic approaches and focus on specific bridge types in limited geographical areas. This study addresses these gaps by introducing a probabilistic framework for assessing the seismic performance of multi-span reinforced concrete (RC) highway bridges in the presence of scour-related uncertainties. A time-dependent scour model, informed by a Bayesian approach and literature-based uncertainties, is integrated into the framework. Probabilistic Seismic Hazard Analysis (PSHA) and record selection strategies are employed to define the seismic demand probabilistically. Monte Carlo simulation (MCS) is utilized to propagate combined uncertainties during the analysis, considering different scour scenarios defined according to the discharge peak condition of the flood event at the specific location of the bridge. In the last step of the probabilistic framework, fragility analyses are conducted on individual components and the entire system by means of nonlinear dynamic models and multiple stripe analysis (MSA). An actual five-span bridge with simply supported prestressed concrete girders, located in Chile, is used as application example of the proposed approach, considering measured hydrological data and seismic hazard of the real location of the bridge. The study identifies distinct vulnerabilities across various bridge components under scour conditions. Elastomeric bearings are particularly susceptible in scenarios characterized by low scour depths (low discharge peak condition). Conversely, abutments display heightened vulnerabilities as scour depth increases (medium discharge peak condition). Piles also exhibit notable vulnerability, escalating in scenarios with great scour depth (medium discharge peak condition). Notably, piles tend to be more susceptible than columns to scour effects, especially in bents featuring shorter columns. These disparities in vulnerability are influenced by the bridge design philosophy, as well as the synergistic effects of seismic forces and varying scour depths. System fragility curves further elucidate that the likelihood of exceeding specific damage thresholds varies according to different scour scenarios, with the deeper scour depth scenario, displaying the higher vulnerabilities.
  • Thumbnail Image
    Item
    33
    (33, 33) Pinto, Francisco; Torres, César; Birrell, Matias; Li, Yong; Fayaz, Jawad; Astroza, Rodrigo; 33
    This study proposes a probabilistic, uncertainty-informed framework for calibrating advanced soil constitutive models (SCMs), particularly, advanced critical state-based models, to accurately capture uncertainty in soil behavior in geotechnical applications. The proposed framework incorporates Polynomial Chaos Expansion (PCE) metamodels to optimize sensitivity analysis (SA) and enable Bayesian updating of SCM parameters, ensuring precise calibration that addresses inherent and epistemic uncertainties. Additionally, Random Forest (RF) analysis is employed to validate initial statistical assumptions and parameter trends during SA and enhance the robustness in the calibration process. Monotonic drained triaxial tests are used within this framework to calibrate the SANISAND model, an advanced critical state-based SCM for sand, with a focus on Nevada Sand soil due to its significance in geotechnical engineering. The framework estimates parameters’ joint probability density functions (PDFs) from experimental data, providing probabilistic insights into model responses under varying confining pressures and relative densities. By reducing computational demands and integrating uncertainty quantification, this approach offers an efficient and accurate calibration process, improving SCM predictive capability and reliability for use in finite element (FE) analyses. This study demonstrates the framework's application and validation to Nevada Sand and proposes PDFs with correlation coefficients for the SANISAND model, accelerating its integration in posterior stochastic geotechnical system-level modeling.