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Dr. Abdollah Shafieezadeh
Department of Civil, Environmental and Geodetic Engineering, Ohio State University, 470 Hitchcock Hall, 2070 Neil Ave, Columbus, OH 43210, USA

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Research Keywords & Expertise

0 Machine Learning
0 system reliability
0 infrastructure resilience
0 Life-cycle analysis
0 Risk-based decision making

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Life-cycle analysis
system reliability
Machine Learning
Sustainability of the built environment

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Research paper
Published: 13 April 2021 in Structural and Multidisciplinary Optimization
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Metamodel-based approaches to reliability analysis, e.g., adaptive Kriging, are computationally challenged by the complexity of reliability problems, thus limiting the application of these methods to problems that are low-dimensional or not rare. Here, we propose a reliability analysis approach via a deep integration of subset simulation and adaptive kriging (RASA) for an unbiased estimation of failure probabilities of high-dimensional or rare event problems. Concepts of conditional failure probability curves and dynamic learning function are introduced to decompose the original problem to subreliability problems and adaptively identify intermediate failure thresholds of limit state functions corresponding to the subreliability problems. The reliability decomposition and the establishment of target intermediate failure thresholds are guided by the available computational capacity, thus, enabling RASA to control the computational cost associated with the estimation of the intermediate failure thresholds in each subset and consequently to analyze the reliability of medium to high-dimensional problems or rare events. Three numerical examples are investigated as benchmark to explore the performance of the proposed method. Results indicate that the proposed method has high accuracy and has the ability to adjust to available computational resources.

ACS Style

Zeyu Wang; Abdollah Shafieezadeh. Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis. Structural and Multidisciplinary Optimization 2021, 1 -27.

AMA Style

Zeyu Wang, Abdollah Shafieezadeh. Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis. Structural and Multidisciplinary Optimization. 2021; ():1-27.

Chicago/Turabian Style

Zeyu Wang; Abdollah Shafieezadeh. 2021. "Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis." Structural and Multidisciplinary Optimization , no. : 1-27.

Original article
Published: 01 April 2021 in Bulletin of Earthquake Engineering
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An appropriate seismic intensity measure (IM) for response prediction is central to reliable probabilistic seismic demand modeling of structures and subsequently, risk and resilience quantification. Bridges in liquefiable and laterally spreading ground may undergo nonlinear responses with large uncertainties when subjected to earthquakes. These issues often lead to low-confidence demand models based on traditional IMs. Fractional order IMs have shown the potential to yield improved demand models in recent studies. To further increase confidence in demand models, this study proposes Fractional Order Spectrum intensity considering Integral period and Damping ratio (named FOSID). The viability of FOSID for use in probabilistic seismic demand modeling of structures is evaluated in the context of extended pile-shaft-supported bridges against liquefaction-induced lateral spreading. The performance of FOSID is systematically assessed by comparisons to an existing fractional order spectrum intensity (SIr,α), Housner intensity (HI)—an optimal traditional IM for these structures, and the average spectral acceleration (Saavg)—a state-of-the-art non-fractional-order IM. Multiple metrics for characterizing an optimal IM are adopted, including practicality, efficiency, proficiency, sufficiency, and relative sufficiency. Optimal variables of integral period, damping ratio, and fractional order for FOSID are identified for different demand parameters such as peak and residual column-drift-ratios. Results show that FOSID is generally more practical, efficient, proficient and sufficient than SIr,α, HI and Saavg. In particular, FOSID significantly outperforms HI by improving the proficiency by nearly 40% and 20% for the peak and residual column-drift-ratios, respectively. With respect to SIr,α, FOSID improves the proficiency by 20% and 15% on average. When compared with Saavg, such improvements are as large as 13% and 24% on average for the peak and residual column-drift-ratios, respectively.

ACS Style

Xiaowei Wang; Abdollah Shafieezadeh; Jamie Ellen Padgett. FOSID: a fractional order spectrum intensity for probabilistic seismic demand modeling of extended pile-shaft-supported highway bridges under liquefaction and transverse spreading. Bulletin of Earthquake Engineering 2021, 19, 2531 -2559.

AMA Style

Xiaowei Wang, Abdollah Shafieezadeh, Jamie Ellen Padgett. FOSID: a fractional order spectrum intensity for probabilistic seismic demand modeling of extended pile-shaft-supported highway bridges under liquefaction and transverse spreading. Bulletin of Earthquake Engineering. 2021; 19 (6):2531-2559.

Chicago/Turabian Style

Xiaowei Wang; Abdollah Shafieezadeh; Jamie Ellen Padgett. 2021. "FOSID: a fractional order spectrum intensity for probabilistic seismic demand modeling of extended pile-shaft-supported highway bridges under liquefaction and transverse spreading." Bulletin of Earthquake Engineering 19, no. 6: 2531-2559.

Journal article
Published: 31 March 2021 in Engineering Structures
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Reliable prediction of seismic responses of bridges against lateral spreading is critical for fragility and resilience assessment of transportation infrastructure. This problem, however, has remained a significant challenge due to the high complexity of the liquefaction phenomenon and its significance for the seismic performance of structures. The present study explores machine learning (ML) approaches, particularly for bridges supported by extended pile-shafts, for reliable estimation of bearing deformation and column drift ratio responses of bridges. The study considers a large set of covariates across soil, structural, and ground motion features. Five ML algorithms are examined including the traditional multiple linear regression (MLR) as the baseline reference, as well as Lasso regression, neural network (NN), random forests (RF), and gradient tree boosting (GTB). A large number of nonlinear soil-bridge finite element models considering the soil and structural uncertainties are dynamically analyzed under 720 ground motions, and the optimal parameters of the ML models are determined via five-fold cross validation. The results indicate that NN and GTB can well predict the seismic responses of the studied soil-bridge systems, followed by RF, while Lasso and MLR are generally not able to yield reliable estimates. Furthermore, a variable importance analysis is conducted using the produced regression models. Results indicate that intensity measures (IMs) (particularly the spectral acceleration at 2.0 s) are generally more significant than the soil- and structure-related variables. In addition to the IMs, variables associated with the nonliquefiable crust layer (i.e., thickness, strength, and sloping angle) and the concrete strength and longitudinal reinforcement ratio are generally significant variables, whereas the column diameter and rebar yielding strength are relatively insignificant.

ACS Style

Xiaowei Wang; Zequn Li; Abdollah Shafieezadeh. Seismic response prediction and variable importance analysis of extended pile-shaft-supported bridges against lateral spreading: Exploring optimized machine learning models. Engineering Structures 2021, 236, 112142 .

AMA Style

Xiaowei Wang, Zequn Li, Abdollah Shafieezadeh. Seismic response prediction and variable importance analysis of extended pile-shaft-supported bridges against lateral spreading: Exploring optimized machine learning models. Engineering Structures. 2021; 236 ():112142.

Chicago/Turabian Style

Xiaowei Wang; Zequn Li; Abdollah Shafieezadeh. 2021. "Seismic response prediction and variable importance analysis of extended pile-shaft-supported bridges against lateral spreading: Exploring optimized machine learning models." Engineering Structures 236, no. : 112142.

Research article
Published: 09 February 2021 in Structure and Infrastructure Engineering
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Existing bridge performance metrics are limited in their ability to objectively reflect the safety and serviceability of bridges and identify effective Maintenance, Repair and Replacement (MR&R) actions for a large number of bridges in the face of budget constraints. This study presents a comprehensive optimal budget allocation framework with an integer linear programing formulation for a portfolio of bridges based on element-level inspection data. Considering budget constraints, this method determines optimal MR&R actions at element-level such that agency and user costs of repair actions required to have bridges in their like-new state at the next budget allocation year are minimized. The framework incorporates structural safety risks via the probability that the designated structural functionality of a bridge is or deemed to be compromised. The approach is applied to 484 National Highway Systems bridges in district 3 of Ohio. Results indicate that the proposed method can identify work plans comprising the optimal set of MR&R actions that maximize the network-level performance of bridge portfolios, while satisfying budgetary constraints. It is observed that this method consistently assigns higher priority to work plans that reduce structural safety risks of bridges, and to bridges with high traffic demands and long detour lengths.

ACS Style

Ehsan Fereshtehnejad; Abdollah Shafieezadeh; Jieun Hur. Optimal budget allocation for bridge portfolios with element-level inspection data: a constrained integer linear programming formulation. Structure and Infrastructure Engineering 2021, 1 -15.

AMA Style

Ehsan Fereshtehnejad, Abdollah Shafieezadeh, Jieun Hur. Optimal budget allocation for bridge portfolios with element-level inspection data: a constrained integer linear programming formulation. Structure and Infrastructure Engineering. 2021; ():1-15.

Chicago/Turabian Style

Ehsan Fereshtehnejad; Abdollah Shafieezadeh; Jieun Hur. 2021. "Optimal budget allocation for bridge portfolios with element-level inspection data: a constrained integer linear programming formulation." Structure and Infrastructure Engineering , no. : 1-15.

Journal article
Published: 20 January 2021 in Structural Safety
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Distribution systems in the US are commonly supported by wood utility poles. Since wood poles may experience substantial decay rates, current standards specify a strength-based maintenance program for pole replacement regardless of the poles’ vulnerability and importance in the system. While state-of-the-art methods have developed risk-based metrics to guide system hardening decisions, such metrics are analyzed for the current conditions of the system. In this context, the potential of a stochastic series of hazards over extended horizons and the subsequent effects on the resilience of systems have been largely neglected. To address these limitations, a risk-based methodology is proposed for quantifying the life cycle resilience of power distribution systems. To project pole vulnerability, a recursive approach is developed that captures the stochasticity of precedent failures and subsequent corrective actions over extended horizons. Furthermore, a risk-based replacement index called Expected Outage Reduction (EOR) is introduced that estimates the expected power outage reduction if an existing pole is replaced by a new pole. The application of the proposed method for life cycle resilience analysis and management of a realistic distribution system subjected to stochastic hurricanes indicates that EOR can improve the cumulative life cycle resilience by up to 22.3% over 70 years.

ACS Style

Yousef Mohammadi Darestani; Keoni Sanny; Abdollah Shafieezadeh; Ehsan Fereshtehnejad. Life cycle resilience quantification and enhancement of power distribution systems: A risk-based approach. Structural Safety 2021, 90, 102075 .

AMA Style

Yousef Mohammadi Darestani, Keoni Sanny, Abdollah Shafieezadeh, Ehsan Fereshtehnejad. Life cycle resilience quantification and enhancement of power distribution systems: A risk-based approach. Structural Safety. 2021; 90 ():102075.

Chicago/Turabian Style

Yousef Mohammadi Darestani; Keoni Sanny; Abdollah Shafieezadeh; Ehsan Fereshtehnejad. 2021. "Life cycle resilience quantification and enhancement of power distribution systems: A risk-based approach." Structural Safety 90, no. : 102075.

Journal article
Published: 12 January 2021 in Applied Energy
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Power distribution systems are continually challenged by extreme climatic events. The reliance of the energy sector on overhead infrastructures for electricity distribution has necessitated a paradigm shift in grid management toward resilience enhancement. Grid hardening strategies are among effective methods for improving resilience. Limited budget and resources, however, demand for optimal planning for hardening strategies. This paper develops a planning framework based on Deep Reinforcement Learning (DRL) to enhance the long-term resilience of distribution systems using hardening strategies. The resilience maximization problem is formulated as a Markov decision process and solved via integration of a novel ranking strategy, neural networks, and reinforcement learning. As opposed to targeting resilience against a single future hazard – a common approach in existing methods – the proposed framework quantifies life-cycle resilience considering the possibility of multiple stochastic events over a system’s life. This development is facilitated by a temporal reliability model that captures the compounding effects of gradual deterioration and hazard effects for stochastic hurricane occurrences. The framework is applied to a large-scale power distribution system with over 7000 poles. Results are compared to an optimal strategy by a mixed-integer nonlinear programming model solved using Branch and Bound (BB), as well as the strength-based strategy by U.S. National Electric Safety Code (NESC). Results indicate that the proposed framework significantly enhances the long-term resilience of the system compared to the NESC strategy by over 30% for a 100-year planning horizon. Furthermore, the DRL-based approach yields optimal solutions for problems that are computationally intractable for the BB algorithm.

ACS Style

Nariman L. Dehghani; Ashkan B. Jeddi; Abdollah Shafieezadeh. Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning. Applied Energy 2021, 285, 116355 .

AMA Style

Nariman L. Dehghani, Ashkan B. Jeddi, Abdollah Shafieezadeh. Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning. Applied Energy. 2021; 285 ():116355.

Chicago/Turabian Style

Nariman L. Dehghani; Ashkan B. Jeddi; Abdollah Shafieezadeh. 2021. "Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning." Applied Energy 285, no. : 116355.

Research paper
Published: 07 January 2021 in Structural and Multidisciplinary Optimization
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A significant challenge with reliability-based design optimization (RBDO) is the high computational cost associated with the double-loop structure that entails a large number of function calls for both the optimization process and reliability analysis. Several decoupling methods have been developed to improve the efficiency of RBDO. In addition, surrogate models have been used to replace the original time-consuming models and improve the computational efficiency. This paper proposes a novel quantile-based sequential RBDO method using Kriging surrogate models for problems with independent constraint functions. An error-controlled adaptive Kriging scheme is integrated to derive accuracy information of surrogate models and develop a strategy that facilitates independent training of the models for the performance function. The proposed independent training avoids unnecessary performance function evaluations while ensuring the accuracy of reliability estimates. Moreover, a new sampling approach is proposed that allows refinement of surrogate models for both deterministic and probabilistic constraints. Five numerical examples are carried out to demonstrate the performance of the proposed method. It is observed that the proposed method is able to converge to the optimum design with significantly fewer function evaluations than the state-of-the-art methods based on surrogate models given the constraint functions are independent.

ACS Style

Chi Zhang; Abdollah Shafieezadeh. A quantile-based sequential approach to reliability-based design optimization via error-controlled adaptive Kriging with independent constraint boundary sampling. Structural and Multidisciplinary Optimization 2021, 63, 2231 -2252.

AMA Style

Chi Zhang, Abdollah Shafieezadeh. A quantile-based sequential approach to reliability-based design optimization via error-controlled adaptive Kriging with independent constraint boundary sampling. Structural and Multidisciplinary Optimization. 2021; 63 (5):2231-2252.

Chicago/Turabian Style

Chi Zhang; Abdollah Shafieezadeh. 2021. "A quantile-based sequential approach to reliability-based design optimization via error-controlled adaptive Kriging with independent constraint boundary sampling." Structural and Multidisciplinary Optimization 63, no. 5: 2231-2252.

Journal article
Published: 18 November 2020 in Reliability Engineering & System Safety
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New information obtained through measurements provide an opportunity to update estimates of a system's reliability. For equality type information, reliability updating is a daunting task. The current state-of-the-art method, reliability updating with equality information using adaptive Kriging (RUAK), integrates an adaptive Kriging process with a transformation of equality information into inequality information. The stopping criterion for training the Kriging model relies on the estimated error for prior failure probability, thus leaving the potential for the true error in posterior failure probability to exceed acceptable thresholds. This study presents an approach to estimate the maximum error in posterior failure probability for a given confidence level . Moreover, a two-phase approach is proposed for active learning and adaptive training of Kriging models in reliability updating problems. The new stopping criterion based on the maximum error of posterior failure probability ensures the accuracy of Kriging and thus the reliability estimates, while the two-phase scheme avoids unnecessary training hence improving the efficiency of reliability updating. Four numerical examples are considered to investigate the performance of the proposed approach. It is demonstrated that this method offers the ability to set and meet target accuracies for reliability updating, which is critical for applications where the consequences of decisions are significant.

ACS Style

Chi Zhang; Zeyu Wang; Abdollah Shafieezadeh. Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information. Reliability Engineering & System Safety 2020, 207, 107323 .

AMA Style

Chi Zhang, Zeyu Wang, Abdollah Shafieezadeh. Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information. Reliability Engineering & System Safety. 2020; 207 ():107323.

Chicago/Turabian Style

Chi Zhang; Zeyu Wang; Abdollah Shafieezadeh. 2020. "Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information." Reliability Engineering & System Safety 207, no. : 107323.

Research article
Published: 12 October 2020 in Earthquake Engineering & Structural Dynamics
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Probabilistic life‐cycle cost (LCC) analysis facilitates optimal management of infrastructure systems under uncertainty. Despite recent advancements, accurate and efficient estimation of the LCC of systems facing risk of exposure to multiple stochastic occurrences of hazards has remained a significant challenge. This paper proposes a Markovian LCC framework that substantially reduces the often very high computational cost of probabilistic LCC analysis especially for large systems. The presented methodology also enhances the accuracy of LCC estimates in multiple fronts. It captures damage accumulations due to incomplete or no repair actions over multiple stochastic hazard exposures in a system's lifetime. Moreover, it tracks the state of infrastructure vulnerability during repair processes and incorporates uncertainties in downtimes associated with damage states of the system. Finally, it estimates the entirity of loss induced by a hazard and avoids the truncation error that exists in annual loss estimates for scenarios where repair downtimes are greater than one year. With these new capabilities, the proposed LCC framework is applied to a bridge in a seismic zone to estimate the LCC and analyze complex interactions of stochastic hazards and their effects with recovery efforts. Results indicate that neglecting effects of damage accumulation and partial improvements in the course of repair can lead to 20% underestimation and 14% overestimation of the expected hazard‐induced LCC, respectively.

ACS Style

Nariman L. Dehghani; Ehsan Fereshtehnejad; Abdollah Shafieezadeh. A Markovian approach to infrastructure life‐cycle analysis: Modeling the interplay of hazard effects and recovery. Earthquake Engineering & Structural Dynamics 2020, 50, 736 -755.

AMA Style

Nariman L. Dehghani, Ehsan Fereshtehnejad, Abdollah Shafieezadeh. A Markovian approach to infrastructure life‐cycle analysis: Modeling the interplay of hazard effects and recovery. Earthquake Engineering & Structural Dynamics. 2020; 50 (3):736-755.

Chicago/Turabian Style

Nariman L. Dehghani; Ehsan Fereshtehnejad; Abdollah Shafieezadeh. 2020. "A Markovian approach to infrastructure life‐cycle analysis: Modeling the interplay of hazard effects and recovery." Earthquake Engineering & Structural Dynamics 50, no. 3: 736-755.

Conference paper
Published: 06 August 2020 in Pipelines 2020
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ACS Style

Soroush Zamanian; Mehrzad Rahimi; Abdollah Shafieezadeh. Resilience of Sewer Networks to Extreme Weather Hazards: Past Experiences and an Assessment Framework. Pipelines 2020 2020, 1 .

AMA Style

Soroush Zamanian, Mehrzad Rahimi, Abdollah Shafieezadeh. Resilience of Sewer Networks to Extreme Weather Hazards: Past Experiences and an Assessment Framework. Pipelines 2020. 2020; ():1.

Chicago/Turabian Style

Soroush Zamanian; Mehrzad Rahimi; Abdollah Shafieezadeh. 2020. "Resilience of Sewer Networks to Extreme Weather Hazards: Past Experiences and an Assessment Framework." Pipelines 2020 , no. : 1.

Original research
Published: 15 July 2020 in Bulletin of Earthquake Engineering
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Suspended building structures have inherent architectural aesthetics and are able to achieve low seismic-induced displacements of the primary structure and accelerations of the suspended segments. A recently proposed subtype of suspended building structures harnesses discrete prefabricated modules to overcome the fragility originating from inter-story drift within the suspended segment and to enhance the overall attenuation. This paper presents the first shake table experimental study of this subtype to directly evaluate its aseismic performance and develop a physics-based modeling strategy that is validated and therefore is reliable. For this purpose, 1:15 scaled shake table experiments of modularized suspended structures were conducted with three fundamental configurations. Each model in each configuration was subjected to at least five ground motions. Results indicate that displacements at the top of the primary structure are reduced by around 50%; in the structure with discrete modules and inter-story dampers, quicker decay was shown, accompanied by lower accelerations of the modules. The inter-story drift ratio of the suspended segment reached 3.75% under 0.12 g PGA excitation, indicating the potential of drift-induced fragility if a regular structure is adopted and proving the benefit of modularization. Numerical models of the tested structural systems have been developed in OpenSees platform. Simulated responses show satisfactory agreement with the measured ones. Subsequent parametric analyses reveal that the performance is sensitive to both the stiffness and damping values especially when the damper is of viscous type. Optimal stiffness facilitates tuning between the primary and secondary structures while optimal damping enhances dissipation notably. Moreover, it is observed that the inherent friction handicaps dissipation instead of facilitating it.

ACS Style

Zhihang Ye; Gang Wu; De-Cheng Feng; Abdollah Shafieezadeh. Shake table testing and computational investigation of the seismic performance of modularized suspended building systems. Bulletin of Earthquake Engineering 2020, 18, 5247 -5279.

AMA Style

Zhihang Ye, Gang Wu, De-Cheng Feng, Abdollah Shafieezadeh. Shake table testing and computational investigation of the seismic performance of modularized suspended building systems. Bulletin of Earthquake Engineering. 2020; 18 (11):5247-5279.

Chicago/Turabian Style

Zhihang Ye; Gang Wu; De-Cheng Feng; Abdollah Shafieezadeh. 2020. "Shake table testing and computational investigation of the seismic performance of modularized suspended building systems." Bulletin of Earthquake Engineering 18, no. 11: 5247-5279.

Journal article
Published: 12 July 2020 in Engineering Structures
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As wastewater systems are reaching critical levels of aging and deterioration, simulation of realistic pipe behaviors is ever more needed to analyze and manage the associated risks. Developing such models is challenging, in part due to the complexity of aging and deterioration impacts on the structural and functional performance of pipes. This study investigates the impacts of a number of design and uncertain variables on the serviceability performance of concrete sewer pipes throughout their service life. This is facilitated by a high-fidelity three-dimensional Finite Element (FE) model of concrete sewer pipes, surrounding soil, and pavement layers in ANSYS platform. The FE model is capable of capturing nonlinear behaviors of concrete, soil, and pipe-soil interactions, in addition to simulating crack formation and crushing of concrete pipes. Corrosion impacts are simulated by sequentially removing concrete layers inside the pipe so that corrosion effects from previous years are taken into account. The influence of linear versus nonlinear assumptions for soil and pipe-soil interactions on the performance of the pipes especially crack width are investigated for cases where the models are subjected to external truckloads. Moreover, impacts of most common trench installations in terms of bedding, side-fill configuration, and soil properties are analyzed for the entire service life of the pipe. Results reveal that the soil surrounding the pipe needs to be modeled as a nonlinear material regardless of the soil grain size. It was observed that the error associated with modeling soil and soil-pipe interactions as linear increases as the pipe ages and the extent of corrosion increases. Results of this investigation can guide the development of reliable and computationally efficient numerical models for deteriorating underground concrete sewer pipes.

ACS Style

Soroush Zamanian; Jieun Hur; Abdollah Shafieezadeh. A high-fidelity computational investigation of buried concrete sewer pipes exposed to truckloads and corrosion deterioration. Engineering Structures 2020, 221, 111043 .

AMA Style

Soroush Zamanian, Jieun Hur, Abdollah Shafieezadeh. A high-fidelity computational investigation of buried concrete sewer pipes exposed to truckloads and corrosion deterioration. Engineering Structures. 2020; 221 ():111043.

Chicago/Turabian Style

Soroush Zamanian; Jieun Hur; Abdollah Shafieezadeh. 2020. "A high-fidelity computational investigation of buried concrete sewer pipes exposed to truckloads and corrosion deterioration." Engineering Structures 221, no. : 111043.

Journal article
Published: 30 June 2020 in Transportation Geotechnics
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While backward erosion piping (BEP) and slope instability failure mechanisms have been investigated independently, they are not analyzed concurrently despite that the response of levees embankments to the BEP may likely impact the stability of the slope, especially if levees were to fail. This study presents a framework towards interfacing soil instability and BEP simulation with the goal of producing a performance model that considers the hydro-mechanical coupling between the material properties. This is afforded by updating the stiffness and strength properties of eroded zones based on the porosity changes that are determined according to a critical diameter model. The framework is then applied on a levee without a cutoff wall built on a silty sand foundation. The results show that the captured coupling between these two failure mechanisms crucially enhances our understanding of the performance of levee systems. Specifically, the results show that coupling effects are more substantial in cases where pipes extend to more than 50% of levee’s bottom width. Furthermore, the results indicate that ignoring the softening in the strength properties of the eroded zones due to BEP leads to overestimation of the slope’s factor of safety against instability by as much as 40%.

ACS Style

Mehrzad Rahimi; Abdollah Shafieezadeh. Coupled backward erosion piping and slope instability performance model for levees. Transportation Geotechnics 2020, 24, 100394 .

AMA Style

Mehrzad Rahimi, Abdollah Shafieezadeh. Coupled backward erosion piping and slope instability performance model for levees. Transportation Geotechnics. 2020; 24 ():100394.

Chicago/Turabian Style

Mehrzad Rahimi; Abdollah Shafieezadeh. 2020. "Coupled backward erosion piping and slope instability performance model for levees." Transportation Geotechnics 24, no. : 100394.

Research article
Published: 23 June 2020 in The Structural Design of Tall and Special Buildings
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Suspended buildings typically have a core as the primary and suspended floors as the secondary structures. These configurations offer visual transparency, smaller vertical components, and seismic attenuation via the primary–secondary structure interaction. Such attenuation is further enhanced by the modularization of the suspended segment which allows large drifts but prevents them from causing damage. Previously conducted shake‐table tests have confirmed these features. However, how the component performance contributes to system performance has not been quantitated. To address this gap, fragility analyses are conducted for 10‐story experimentally validated models with optimized supplemental dampers and inter‐module stiffness. Multiple limit state functions are proposed to provide a full account of damage sources. Additionally, a mapping rule from the component‐level to the system‐level limit states is developed which captures the influence of damage distribution on system‐level limit states. Results for the uncontrolled suspended building indicate that for the PGV of 0.5 m/s, the failure probabilities of the repairable and life safety limit states are 97% and 83%, respectively. These probabilities are 92% and 27% for the frame structure with viscous dampers, 58% and 5% for the passive‐controlled modularized suspended building system (MSBS), and 45% and 3% for MSBS with optimal vertical distributions of modularized secondary structure.

ACS Style

Zhihang Ye; Abdollah Shafieezadeh; Halil Sezen; Gang Wu; De‐Cheng Feng. Cross‐level fragility analysis of modularized suspended buildings based on experimentally validated numerical models. The Structural Design of Tall and Special Buildings 2020, 1 .

AMA Style

Zhihang Ye, Abdollah Shafieezadeh, Halil Sezen, Gang Wu, De‐Cheng Feng. Cross‐level fragility analysis of modularized suspended buildings based on experimentally validated numerical models. The Structural Design of Tall and Special Buildings. 2020; ():1.

Chicago/Turabian Style

Zhihang Ye; Abdollah Shafieezadeh; Halil Sezen; Gang Wu; De‐Cheng Feng. 2020. "Cross‐level fragility analysis of modularized suspended buildings based on experimentally validated numerical models." The Structural Design of Tall and Special Buildings , no. : 1.

Journal article
Published: 15 June 2020 in International Journal of Impact Engineering
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A prominent threat to key structures and components in critical facilities, especially during a tornado or hurricane, is impact by wind-borne projectiles. Identifying uncertain variables in these phenomena is key for developing cost-effective design formulae and efficient reliability analysis. This task however is challenging due to the scarcity of data and the large set of uncertain factors that contribute to the impact phenomenon. The present study performs a robust global sensitivity analysis (GSA) using Sobol's indices on the response of a concrete panel impacted by a Schedule 40 pipe. A high-fidelity, nonlinear Finite Element (FE) model is developed using the Smooth Particle Hydrodynamics (SPH) formulation in LS-DYNA. The developed model was validated with the experiments conducted by the Electric Power Research Institute (EPRI). Due to the high computational demand of the SPH model, a machine learning-based surrogate model called Bayesian Additive Regression Trees (BART) is applied to emulate the computational model. This surrogate model that is trained with a limited number of generated SPH simulations is capable of accurately predicting the behavior of concrete panels subjected to projectile impact over the space of predictors. The predictors here include uncertain variables associated with concrete and steel material properties. GSA is subsequently performed by integrating the constructed surrogate model into Sobol's algorithm. Results indicate that concrete tensile strength plays a significant role in panel damage, while concrete mass density and compressive strength and mass density of the steel pipe are also significant. These findings suggest that the development of new empirical design formulae and experimental studies on the impact of concrete panels should include the identified significant variables.

ACS Style

Soroush Zamanian; Brian Terranova; Abdollah Shafieezadeh. Significant variables affecting the performance of concrete panels impacted by wind-borne projectiles: A global sensitivity analysis. International Journal of Impact Engineering 2020, 144, 103650 .

AMA Style

Soroush Zamanian, Brian Terranova, Abdollah Shafieezadeh. Significant variables affecting the performance of concrete panels impacted by wind-borne projectiles: A global sensitivity analysis. International Journal of Impact Engineering. 2020; 144 ():103650.

Chicago/Turabian Style

Soroush Zamanian; Brian Terranova; Abdollah Shafieezadeh. 2020. "Significant variables affecting the performance of concrete panels impacted by wind-borne projectiles: A global sensitivity analysis." International Journal of Impact Engineering 144, no. : 103650.

Articles
Published: 04 May 2020 in Structure and Infrastructure Engineering
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Identifying key variables that influence crack formation in buried concrete sewer pipes and impact their structural integrity is crucial for evaluating the leakage and collapse vulnerability of these underground structures. The present study performs a comprehensive global sensitivity analysis for these critical pipe responses considering various uncertainties associated with material properties and loading. A high-fidelity three-dimensional nonlinear Finite Element (FE) model of a typical pipe is developed and used to capture pipe behavior. Considering the significantly high computational demand of the generated FE model, a surrogate model using Bayesian Additive Regression Trees (BART) is developed to accurately approximate input-output relationships in this high-dimensional problem. Significant variables are subsequently determined via a global sensitivity analysis based on Sobol’s theorem through the application of Markov Chain Monte Carlo simulation to the trained BART model. Results indicate that concrete and backfill soil properties and variables associated with truckloads contribute significantly to the variation of key pipe responses, thereby affecting the likelihood of leakage and collapse. Findings of this study can benefit the design and management of sewer pipes in large wastewater networks and significantly reduce the complexity of assessing the reliability of sewer pipes.

ACS Style

Soroush Zamanian; Jieun Hur; Abdollah Shafieezadeh. Significant variables for leakage and collapse of buried concrete sewer pipes: a global sensitivity analysis via Bayesian additive regression trees and Sobol’ indices. Structure and Infrastructure Engineering 2020, 17, 676 -688.

AMA Style

Soroush Zamanian, Jieun Hur, Abdollah Shafieezadeh. Significant variables for leakage and collapse of buried concrete sewer pipes: a global sensitivity analysis via Bayesian additive regression trees and Sobol’ indices. Structure and Infrastructure Engineering. 2020; 17 (5):676-688.

Chicago/Turabian Style

Soroush Zamanian; Jieun Hur; Abdollah Shafieezadeh. 2020. "Significant variables for leakage and collapse of buried concrete sewer pipes: a global sensitivity analysis via Bayesian additive regression trees and Sobol’ indices." Structure and Infrastructure Engineering 17, no. 5: 676-688.

Journal article
Published: 11 March 2020 in IEEE Access
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Large uncertainties in many phenomena have challenged decision making. Collecting additional information to better characterize reducible uncertainties is among decision alternatives. Value of information (VoI) analysis is a mathematical decision framework that quantifies expected potential benefits of new data and assists with optimal allocation of resources for information collection. However, analysis of VoI is computational very costly because of the underlying Bayesian inference especially for equality-type information. This paper proposes the first surrogate-based framework for VoI analysis. Instead of modeling the limit state functions describing events of interest for decision making, which is commonly pursued in surrogate model-based reliability methods, the proposed framework models system responses. This approach affords sharing equality-type information from observations among surrogate models to update likelihoods of multiple events of interest. Moreover, two knowledge sharing schemes called model and training points sharing are proposed to most effectively take advantage of the knowledge offered by costly model evaluations. Both schemes are integrated with an error rate-based adaptive training approach to efficiently generate accurate Kriging surrogate models. The proposed VoI analysis framework is applied for an optimal decision-making problem involving load testing of a truss bridge. While state-of-the-art methods based on importance sampling and adaptive Kriging Monte Carlo simulation are unable to solve this problem, the proposed method is shown to offer accurate and robust estimates of VoI with a limited number of model evaluations. Therefore, the proposed method facilitates the application of VoI for complex decision problems.

ACS Style

Chi Zhang; Zeyu Wang; Abdollah Shafieezadeh. Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging. IEEE Access 2020, 8, 51021 -51034.

AMA Style

Chi Zhang, Zeyu Wang, Abdollah Shafieezadeh. Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging. IEEE Access. 2020; 8 (99):51021-51034.

Chicago/Turabian Style

Chi Zhang; Zeyu Wang; Abdollah Shafieezadeh. 2020. "Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging." IEEE Access 8, no. 99: 51021-51034.

Journal article
Published: 01 March 2020 in International Journal of Geomechanics
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For geotechnical systems involving complex behaviors and significant uncertainties, metamodeling-based reliability methods based on fixed designs of experiment (passive metamodeling methods) are frequently applied. However, because of the passive nature of these methods, they do not guarantee accurate probability estimates. This issue also persists in estimates obtained from active metamodeling methods (metamodeling methods that update the design of experiment iteratively) if a global stopping criterion or a nonoptimal learning function is used for the type of problem. This study highlights the importance of addressing these issues by investigating an example of failure analysis of soil slopes and concludes that the stopping criteria for development and refinement of metamodels need to be objective and locally defined with respect to the limit state function (LSF) and with a direct link to the accuracy of the reliability estimates. To address these challenges, this study established an effective sampling region that optimally ignores candidate design samples with weak probability densities. This enabled the development of an effective learning function that facilitates the active learning process. Moreover, an analytical upper bound for the error in failure probability estimation, proposed by the authors, was adopted here to arrive at a local and objective stopping criterion. This method was applied on two soil slopes, the stability of which was evaluated by the strength reduction method (SRM) in FLAC3D. The results highlight that passive and active methods with global stopping criteria cannot guarantee an accurate estimate of the failure probability. Furthermore, the developed method can considerably reduce the computational demands while achieving accurate estimates of failure probabilities of soil slopes.

ACS Style

Mehrzad Rahimi; Zeyu Wang; Abdollah Shafieezadeh; Dylan Wood; Ethan J. Kubatko. Exploring Passive and Active Metamodeling-Based Reliability Analysis Methods for Soil Slopes: A New Approach to Active Training. International Journal of Geomechanics 2020, 20, 04020009 .

AMA Style

Mehrzad Rahimi, Zeyu Wang, Abdollah Shafieezadeh, Dylan Wood, Ethan J. Kubatko. Exploring Passive and Active Metamodeling-Based Reliability Analysis Methods for Soil Slopes: A New Approach to Active Training. International Journal of Geomechanics. 2020; 20 (3):04020009.

Chicago/Turabian Style

Mehrzad Rahimi; Zeyu Wang; Abdollah Shafieezadeh; Dylan Wood; Ethan J. Kubatko. 2020. "Exploring Passive and Active Metamodeling-Based Reliability Analysis Methods for Soil Slopes: A New Approach to Active Training." International Journal of Geomechanics 20, no. 3: 04020009.

Conference paper
Published: 21 February 2020 in Geo-Congress 2020
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Due to the complexity of wave propagation in soil media and the uncertainty in geotechnical material properties, predicted surface motions using theoretical wave propagation models often deviate from recorded surface ground motions. This gap highlights the need for more representative seismic wave propagation models. Seismic wave propagation based upon Kjartansson's constant-Q concept is one such model, in which the energy loss during each cycle is considered to be independent of frequency. A fundamental feature of the constant-Q model is that the soil stress-strain relationship involves time derivatives of fractional order (called the FD model here). The complete identification of the FD model requires determining three parameters, including the soil density, shear wave velocity at a reference frequency, and dissipation factor. This study performs a probabilistic calibration based on Bayes’ theorem to arrive at most likely estimates of the parameters of the FD model. In the calibration process, we develop theoretical transfer functions by solving the Kjartansson's constant-Q model in the frequency domain. The theoretical and empirical surface-downhole transfer functions are computed for a Kiban-Kyoshin network (KiK-net) site and subsequently used in the likelihood function of Bayesian calibration approach. In this investigation, we present posterior probabilistic models for the parameters of the FD model, which facilitate probabilistic linear and equivalent linear site response analysis for cases where the FD model is used to define soil behavior. Moreover, results of this study show that the constant-Q model offers an accurate representation of seismic wave propagation in soil media.

ACS Style

Nariman L. Dehghani; Mehrzad Rahimi; Abdollah Shafieezadeh; Jamie E. Padgett. Parameter Estimation of a Fractional Order Soil Constitutive Model Using KiK-Net Downhole Array Data: A Bayesian Updating Approach. Geo-Congress 2020 2020, 1 .

AMA Style

Nariman L. Dehghani, Mehrzad Rahimi, Abdollah Shafieezadeh, Jamie E. Padgett. Parameter Estimation of a Fractional Order Soil Constitutive Model Using KiK-Net Downhole Array Data: A Bayesian Updating Approach. Geo-Congress 2020. 2020; ():1.

Chicago/Turabian Style

Nariman L. Dehghani; Mehrzad Rahimi; Abdollah Shafieezadeh; Jamie E. Padgett. 2020. "Parameter Estimation of a Fractional Order Soil Constitutive Model Using KiK-Net Downhole Array Data: A Bayesian Updating Approach." Geo-Congress 2020 , no. : 1.

Research article
Published: 11 February 2020 in Earthquake Spectra
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This study investigates the influence of intensity measure (IM) selection on simulation-based regional seismic risk assessment (RSRA) of spatially distributed structural portfolios. First, a co-simulation method for general spectral averaging vector IMs is derived. Then a portfolio-level surrogate demand modeling approach, which incorporates the seismic demand estimation of the non-collapse and collapse states, is proposed. The derived IM co-simulation method enables the first comparative study of different IMs, including the conventional IMs and some more advanced scalar and vector IMs, in the context of RSRA. The influence of IM selection on the predictive performance of the portfolio-level surrogate demand models, as well as on the regional seismic risk estimates, is explored based on a virtual spatially distributed structural portfolio subjected to a scenario earthquake. The results of this study provide pertinent insights in surrogate demand modeling, IM co-simulation and selection, which can facilitate more accurate and reliable regional seismic risk estimates.

ACS Style

Ao Du; Jamie E Padgett; Abdollah Shafieezadeh. Influence of intensity measure selection on simulation-based regional seismic risk assessment. Earthquake Spectra 2020, 36, 647 -672.

AMA Style

Ao Du, Jamie E Padgett, Abdollah Shafieezadeh. Influence of intensity measure selection on simulation-based regional seismic risk assessment. Earthquake Spectra. 2020; 36 (2):647-672.

Chicago/Turabian Style

Ao Du; Jamie E Padgett; Abdollah Shafieezadeh. 2020. "Influence of intensity measure selection on simulation-based regional seismic risk assessment." Earthquake Spectra 36, no. 2: 647-672.