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Within the engineering domain, safety issues are often related to engineering design and typically exclude factors such as epidemics, famine, and disease. This article provides a perspective on the reciprocal relationship and interaction between a natural hazard and a simultaneous pandemic outbreak and discusses how a catastrophic dam break, combined with the ongoing COVID-19 pandemic, poses a risk to human life. The paper uses grey- and peer-reviewed literature to support the discussion and reviews fundamentals of dam safety management, potential loss of life due to a dam break, and the recent evolution in dam risk analysis to account for the COVID-19 outbreak. Conventional risk reduction recommendations, such as quick evacuation and sheltering in communal centers, are revisited in the presence of a pandemic when social distancing is recommended. This perspective manuscript aims to provide insight into the multi-hazard risk problem resulting from a concurring natural hazard and global pandemic.
Mohammad Hariri-Ardebili; Upmanu Lall. Superposed Natural Hazards and Pandemics: Breaking Dams, Floods, and COVID-19. Sustainability 2021, 13, 8713 .
AMA StyleMohammad Hariri-Ardebili, Upmanu Lall. Superposed Natural Hazards and Pandemics: Breaking Dams, Floods, and COVID-19. Sustainability. 2021; 13 (16):8713.
Chicago/Turabian StyleMohammad Hariri-Ardebili; Upmanu Lall. 2021. "Superposed Natural Hazards and Pandemics: Breaking Dams, Floods, and COVID-19." Sustainability 13, no. 16: 8713.
Investigating the economic consequences of the natural hazards on the infrastructures is a crucial task in any risk-based decision-making process. Life-cycle cost (LCC) analysis is an important tool in both design (to choose the most economic configuration) and analysis (to estimate the future cost of ownership) stages. The pile-supported wharves, as one of the main parts in a marine harbor system, may experience significant deterioration (i.e. strength reduction) and seismic shaking during their life time. In this paper, damage cost in multiple limit states of a deteriorated pile-supported wharf due to chloride corrosion is calculated. A precise finite element model is developed to account for structural aging and the simultaneous seismic shaking. Aging-dependent seismic fragility functions are first developed using incremental dynamic analysis. Next, the LCC analysis is conducted considering the crane damage, as well as, inspection and maintenance costs. The results are calculated for three seismic hazard levels. The findings confirm significance of corrosion on LCC of pile-supported wharf. It is observed that corrosion may increase the LCC of the structure and system (i.e. structure and cranes) by 15% and 8%, respectively. Finally, a set of analytical formulations are proposed for performance index as a function of age, damage state, and seismic hazard level.
Hamid Mirzaeefard; Masoud Mirtaheri; Mohammad Amin Hariri-Ardebili. Life-cycle cost analysis of pile-supported wharves under multi-hazard condition: aging and shaking. Structure and Infrastructure Engineering 2021, 1 -21.
AMA StyleHamid Mirzaeefard, Masoud Mirtaheri, Mohammad Amin Hariri-Ardebili. Life-cycle cost analysis of pile-supported wharves under multi-hazard condition: aging and shaking. Structure and Infrastructure Engineering. 2021; ():1-21.
Chicago/Turabian StyleHamid Mirzaeefard; Masoud Mirtaheri; Mohammad Amin Hariri-Ardebili. 2021. "Life-cycle cost analysis of pile-supported wharves under multi-hazard condition: aging and shaking." Structure and Infrastructure Engineering , no. : 1-21.
Generating low-rank approximations of kernel matrices that arise in nonlinear machine learning techniques holds the potential to significantly alleviate the memory and computational burdens. A compelling approach centers on finding a concise set of exemplars or landmarks to reduce the number of similarity measure evaluations from quadratic to linear concerning the data size. However, a key challenge is to regulate tradeoffs between the quality of landmarks and resource consumption. Despite the volume of research in this area, current understanding is limited regarding the performance of landmark selection techniques in the presence of class-imbalanced data sets that are becoming increasingly prevalent in many applications. Hence, this paper provides a comprehensive empirical investigation using several real-world imbalanced data sets, including scientific data, by evaluating the quality of approximate low-rank decompositions and examining their influence on the accuracy of downstream tasks. Furthermore, we present a new landmark selection technique called Distance-based Importance Sampling and Clustering (DISC), in which the relative importance scores are computed for improving accuracy-efficiency tradeoffs compared to existing works that range from probabilistic sampling to clustering methods. The proposed landmark selection method follows a coarse-to-fine strategy to capture the intrinsic structure of complex data sets, allowing us to substantially reduce the computational complexity and memory footprint with minimal loss in accuracy.
Parisa Hajibabaee; Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili. Kernel Matrix Approximation on Class-Imbalanced Data With an Application to Scientific Simulation. IEEE Access 2021, 9, 1 -1.
AMA StyleParisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili. Kernel Matrix Approximation on Class-Imbalanced Data With an Application to Scientific Simulation. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleParisa Hajibabaee; Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili. 2021. "Kernel Matrix Approximation on Class-Imbalanced Data With an Application to Scientific Simulation." IEEE Access 9, no. : 1-1.
Time-dependent reliability and sensitivity analysis, in which the nature of demand, capacity and the limit state function varies over the life cycle of the structural system, is a challenging task. Meta-models are a suitable tool to construct inexpensive-to-evaluate and accurate surrogates in analysis of modern engineering problems. The paper’s contribution is threefold: First, the superiority of polynomial chaos Kriging (PCK) meta-model in terms of efficiency and accuracy is depicted in comparison to two other state-of-the-art methods in an explicit representation of a pilot gravity dam. Second, the problem of aging dams is analytically studied. Adaptive reliability approaches which benefit from Kriging and PCK meta-models are also investigated in a comparative analysis with classical reliability methods. Third, the importance of considering nonlinear dependency between random variables by copula theory is investigated under the reliability and sensitivity concepts. The results show that PCK meta-model can be used as an effective technique in uncertainty quantification (UQ) of dams. Furthermore, Kriging and PCK-assisted reliability methods can establish fairly accurate meta-models to perform reliability analysis in structural UQ with low computational efforts. Finally, the concept of UQ is propagated to ”dam class” in which the dam shape and its age are assumed to be variable. A generalized program is developed to assist dam owners and decision-makers in approximate failure probability estimation of gravity dams. This paper paves the road for global risk assessment of dams.
A. Amini; A. Abdollahi; M.A. Hariri-Ardebili; U. Lall. Copula-based reliability and sensitivity analysis of aging dams: Adaptive Kriging and polynomial chaos Kriging methods. Applied Soft Computing 2021, 109, 107524 .
AMA StyleA. Amini, A. Abdollahi, M.A. Hariri-Ardebili, U. Lall. Copula-based reliability and sensitivity analysis of aging dams: Adaptive Kriging and polynomial chaos Kriging methods. Applied Soft Computing. 2021; 109 ():107524.
Chicago/Turabian StyleA. Amini; A. Abdollahi; M.A. Hariri-Ardebili; U. Lall. 2021. "Copula-based reliability and sensitivity analysis of aging dams: Adaptive Kriging and polynomial chaos Kriging methods." Applied Soft Computing 109, no. : 107524.
Engineers are often confronted with the challenge of performing a rigorous safety assessment of concrete structures affected by alkali silica reaction (ASR). Such an endeavor is often accompanied by accelerated expansion tests to simulate the aging process. This paper proposes an integrative framework to link test results with finite element analyses in order to predict future response. This hybrid approach is further enhanced through a probabilistic framework. As a vehicle for this approach, tests performed on large reinforced concrete panels subjected to accelerated ASR expansion are used, and predictive response made through an uncertainty quantification paradigm. Finally, safety is quantified through developed fragility functions. The proposed methodology could be expanded as a prognosis tool to other applications where future response of ASR affected structures is sought.
Mohammad Amin Hariri-Ardebili; Victor E. Saouma; Nolan W. Hayes. A hybrid FE-based predictive framework for ASR-affected structures coupled with accelerated experiments. Engineering Structures 2021, 234, 111709 .
AMA StyleMohammad Amin Hariri-Ardebili, Victor E. Saouma, Nolan W. Hayes. A hybrid FE-based predictive framework for ASR-affected structures coupled with accelerated experiments. Engineering Structures. 2021; 234 ():111709.
Chicago/Turabian StyleMohammad Amin Hariri-Ardebili; Victor E. Saouma; Nolan W. Hayes. 2021. "A hybrid FE-based predictive framework for ASR-affected structures coupled with accelerated experiments." Engineering Structures 234, no. : 111709.
Quantification of structural vibration characteristics is an essential task prior to perform any dynamic health monitoring and system identification. Anatomy of vibration in concrete arch dams (especially tall dams with un-symmetry shape) is very complicated and requires special techniques to solve the eigenvalue problem. The situation becomes even more complicated if the material distribution is assumed to be heterogeneous within the dam body (as opposed to conventional isotropic homogeneous relationship). This paper proposes a hybrid Random Field (RF)–Polynomial Chaos Expansion (PCE) surrogate model for uncertainty quantification and sensitivity assessment of dams. For different vibration modes, the most sensitive spatial locations within dam body are identified using both Sobol’s indices and correlation rank methods. Results of the proposed hybrid model is further validated using the classical random forest regression method. The outcome of this study can improve the results of system identification and dynamic analysis by properly determining the vibration characteristics.
Mohammad Hariri-Ardebili; Golsa Mahdavi; Azam Abdollahi; Ali Amini. An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams. Water 2021, 13, 302 .
AMA StyleMohammad Hariri-Ardebili, Golsa Mahdavi, Azam Abdollahi, Ali Amini. An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams. Water. 2021; 13 (3):302.
Chicago/Turabian StyleMohammad Hariri-Ardebili; Golsa Mahdavi; Azam Abdollahi; Ali Amini. 2021. "An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams." Water 13, no. 3: 302.
Uncertainty quantification in complex engineering problems is challenging because of necessitating large numbers of expensive model evaluations. This paper proposes a two-stage framework for developing accurate machine learning-based surrogate models in structural engineering. The studied numerical model considers aleatory and epistemic uncertainties, i.e., ground motion features and material properties. Our framework’s first step trains classification algorithms on the collected data from our numerical model with a disproportionate ratio of observations from two categories, i.e., failed and safe simulations. We investigate the performance of imbalanced learning strategies along with artificial neural networks to achieve high classification accuracy. The second step of our framework aims to estimate three quantities of interest using the same network architecture, comparing our approach with regularized linear regression models. Moreover, we present a new approach to reducing the number of numerical simulations for developing machine learning-based surrogate models with limited training data. This approach employs Gaussian processes as a powerful probabilistic technique, providing an inherent uncertainty measure to determine the quality of estimated response values. Extensive numerical experiments demonstrate the superior performance of neural networks with three hidden layers compared to traditional machine learning algorithms for both classification and regression tasks. Also, empirical investigations corroborate that Gaussian processes enable us to predict the values of missing simulations for reducing the computational cost associated with numerical models. To conclude this work, we present several applications and future research directions.
Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili. Neural Networks and Imbalanced Learning for Data-Driven Scientific Computing With Uncertainties. IEEE Access 2021, 9, 15334 -15350.
AMA StyleFarhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili. Neural Networks and Imbalanced Learning for Data-Driven Scientific Computing With Uncertainties. IEEE Access. 2021; 9 ():15334-15350.
Chicago/Turabian StyleFarhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili. 2021. "Neural Networks and Imbalanced Learning for Data-Driven Scientific Computing With Uncertainties." IEEE Access 9, no. : 15334-15350.
This discussion is based on the paper by Pang et al. (2020) (hereafter identified as “the original authors” and “the original paper” or “the reference paper”). The original authors employed the endurance time analysis (ETA) and incremental dynamic analysis (IDA) techniques to investigate the probabilistic response of a deep water bridge in a reservoir. They derived multiple fragility curves using both techniques. Derivation of ETA-based fragility functions in the original paper is not fully consistent with the nature of seismic fragility curves. The underpinning concept of fragility curves is tied with ground motion record-to-record (RTR) variability, while the conventional ETA does not offer such an ability. We discussed several underlying assumptions in the ETA method, as well as different sources of ground motion RTR variability (from IDA technique) which may affect the logarithmic dispersion in a fragility curve (and it is unseen in conventional ETA method).
Mohammad Amin Hariri-Ardebili; Siamak Sattar. Myths and realities of endurance time analysis: Discussion/Comments Regarding “Seismic assessment of deep water bridges in reservoir considering hydrodynamic effects using endurance time analysis” by Yutao Pang, Li Cai, Wei He, and Li Wu; Ocean Engineering, 2020, 198:106846. Ocean Engineering 2021, 221, 108499 .
AMA StyleMohammad Amin Hariri-Ardebili, Siamak Sattar. Myths and realities of endurance time analysis: Discussion/Comments Regarding “Seismic assessment of deep water bridges in reservoir considering hydrodynamic effects using endurance time analysis” by Yutao Pang, Li Cai, Wei He, and Li Wu; Ocean Engineering, 2020, 198:106846. Ocean Engineering. 2021; 221 ():108499.
Chicago/Turabian StyleMohammad Amin Hariri-Ardebili; Siamak Sattar. 2021. "Myths and realities of endurance time analysis: Discussion/Comments Regarding “Seismic assessment of deep water bridges in reservoir considering hydrodynamic effects using endurance time analysis” by Yutao Pang, Li Cai, Wei He, and Li Wu; Ocean Engineering, 2020, 198:106846." Ocean Engineering 221, no. : 108499.
Pile-supported wharves, as one of the main components in marine harbor systems, are subjected to multiple hazards such as earthquakes and corrosion during their lifetime. In this paper, the corrosion initiation time due to chloride ion diffusion (using both the deterministic and probabilistic approaches), temperature and humidity variations are calculated for various steel materials in the typical pile-supported wharf of port Los Angeles. Various deterioration sources such as reduction in the ultimate strength and strain of prestressed strands and concrete compressive strength are simulated. The updating limit state functions are identified by nonlinear static analysis. Finally, the time-dependent fragility curves are developed using incremental dynamic analysis. Results show that corrosion considerably reduces the seismic performance due to both structural strength and ductility over the life cycle of the wharf. It also causes brittle fracture, a sudden and complete failure of the corroded piles towards the end of the structure’s life cycle. Finally, a set of analytical models is proposed to estimate the median of the aging fragility functions with a nearly constant dispersion.
Hamid Mirzaeefard; Mohammad Amin Hariri-Ardebili; Masoud Mirtaheri. Time-dependent seismic fragility analysis of corroded pile-supported wharves with updating limit states. Soil Dynamics and Earthquake Engineering 2021, 142, 106551 .
AMA StyleHamid Mirzaeefard, Mohammad Amin Hariri-Ardebili, Masoud Mirtaheri. Time-dependent seismic fragility analysis of corroded pile-supported wharves with updating limit states. Soil Dynamics and Earthquake Engineering. 2021; 142 ():106551.
Chicago/Turabian StyleHamid Mirzaeefard; Mohammad Amin Hariri-Ardebili; Masoud Mirtaheri. 2021. "Time-dependent seismic fragility analysis of corroded pile-supported wharves with updating limit states." Soil Dynamics and Earthquake Engineering 142, no. : 106551.
Majority of the existing dam deformation monitoring models focus on the prediction of individual displacement, and ignore the spatial correlation of data. In this study, we propose a method dealing with multi-target prediction called the Maximum Correlated Stacking of Single-Target. The proposed method can provide reliable predictions of multi-target simultaneously, while fully exploiting the internal relationships between target variables via the strategy of targets stacking. Moreover, it can be coupled with different existing baseline models for the prediction and anomaly detection of arch dam deformation. Jinping–I arch dam is taken as a case study, where the monitoring displacement of 23 different points are analyzed and modeled simultaneously. Three kernel-based machine learning algorithms (i.e., support vector machine, relevance vector machine, and kernel extreme learning machine) and the partial least squares regression are adopted as baseline models for multi-target regression methods. Compared with the single-target regression and two state-of-the-art multi-target regression methods, the simulated results reveal the higher accuracy of the proposed method. Furthermore, model performance is validated in terms of anomaly detection capability, where two progressive anomalous scenarios (i.e., anomalies of single or multiple points) are investigated. The proposed method can be adapted for the health monitoring of other infrastructures in which multiple responses (e.g., displacement, temperature, or stress) need to be predicted simultaneously.
Siyu Chen; Chongshi Gu; Chaoning Lin; Mohammad Amin Hariri-Ardebili. Prediction of arch dam deformation via correlated multi-target stacking. Applied Mathematical Modelling 2020, 91, 1175 -1193.
AMA StyleSiyu Chen, Chongshi Gu, Chaoning Lin, Mohammad Amin Hariri-Ardebili. Prediction of arch dam deformation via correlated multi-target stacking. Applied Mathematical Modelling. 2020; 91 ():1175-1193.
Chicago/Turabian StyleSiyu Chen; Chongshi Gu; Chaoning Lin; Mohammad Amin Hariri-Ardebili. 2020. "Prediction of arch dam deformation via correlated multi-target stacking." Applied Mathematical Modelling 91, no. : 1175-1193.
The AAR model of the author is an uncoupled one, that is the constitutive model is in no way affected by the AAR which itself is considered to be an initial strain (akin of temperature), which grafts itself on the mechanical one. It is implemented in [1], and a complete “validation” of the code with the RILEM benchmark is separately published [2]. This section will describe first the AAR model yielding to the expression of the AAR strain tensor which is accounted for.
Victor Saouma; M. Amin Hariri-Ardebili. Benchmark Study Results: Merlin/Colorado. Bio-aggregates Based Building Materials 2020, 461 -490.
AMA StyleVictor Saouma, M. Amin Hariri-Ardebili. Benchmark Study Results: Merlin/Colorado. Bio-aggregates Based Building Materials. 2020; ():461-490.
Chicago/Turabian StyleVictor Saouma; M. Amin Hariri-Ardebili. 2020. "Benchmark Study Results: Merlin/Colorado." Bio-aggregates Based Building Materials , no. : 461-490.
A number of structures worldwide are known to (or will) suffer from chemically induced expansion of the concrete. This includes not only the traditional alkali aggregate reaction (also known as alkali silica reaction) but increasingly delayed ettringite formation (DEF)
Victor Saouma; Alain Sellier; Stéphane Multon; Yann Le Pape. Benchmark Problems for AAR FEA Code Validation. Bio-aggregates Based Building Materials 2020, 381 -410.
AMA StyleVictor Saouma, Alain Sellier, Stéphane Multon, Yann Le Pape. Benchmark Problems for AAR FEA Code Validation. Bio-aggregates Based Building Materials. 2020; ():381-410.
Chicago/Turabian StyleVictor Saouma; Alain Sellier; Stéphane Multon; Yann Le Pape. 2020. "Benchmark Problems for AAR FEA Code Validation." Bio-aggregates Based Building Materials , no. : 381-410.
Various levels of uncertainties exist in the structural mechanics problems which need to be properly quantified. In the case of the brittle materials, the uncertainties might be addressed in the multi-scale level using random variables (RV), random fields (RF), and their correlation. This paper presents the results of a study that considers the spatial distribution of several mass concrete parameters in the context of the macro-scale RF theory. Three-dimensional slice finite element model of a dam is prepared as case study. The modulus of elasticity, tensile strength, and mass density are assumed to be either homogeneous or heterogeneous within the body. Appropriate RF realizations are generated based on the mid-point discretization technique, and covariance matrix decomposition. First, a deterministic reference coupled system model is analyzed, and the damage is verified according to the literature. Next, several stochastic finite element simulations are performed to understand the progressive failure of the heterogeneous and homogeneous mass concrete in the macro-scale. The impact of correlation length, and multiple RVs are also investigated. The uncertainty and dispersion of the intensifying dynamic responses, as well as the failure modes are quantified. It is observed that concrete heterogeneity affects the progressive failure analysis, and should be included in any detailed risk assessment framework.
Mohammad Amin Hariri-Ardebili. Uncertainty quantification of heterogeneous mass concrete in macro-scale. Soil Dynamics and Earthquake Engineering 2020, 137, 106137 .
AMA StyleMohammad Amin Hariri-Ardebili. Uncertainty quantification of heterogeneous mass concrete in macro-scale. Soil Dynamics and Earthquake Engineering. 2020; 137 ():106137.
Chicago/Turabian StyleMohammad Amin Hariri-Ardebili. 2020. "Uncertainty quantification of heterogeneous mass concrete in macro-scale." Soil Dynamics and Earthquake Engineering 137, no. : 106137.
Humans are living in an uncertain world, with daily risks confronting them from various low to high hazard events, and the COVID-19 pandemic has created its own set of unique risks. Not only has it caused a significant number of fatalities, but in combination with other hazard sources, it may pose a considerably higher multi-risk. In this paper, three hazardous events are studied through the lens of a concurring pandemic. Several low-probability high-risk scenarios are developed by the combination of a pandemic situation with a natural hazard (e.g., earthquakes or floods) or a complex emergency situation (e.g., mass protests or military movements). The hybrid impacts of these multi-hazard situations are then qualitatively studied on the healthcare systems, and their functionality loss. The paper also discusses the impact of pandemic’s (long-term) temporal effects on the type and recovery duration from these adverse events. Finally, the concept of escape from a hazard, evacuation, sheltering and their potential conflict during a pandemic and a natural hazard is briefly reviewed. The findings show the cascading effects of these multi-hazard scenarios, which are unseen nearly in all risk legislation. This paper is an attempt to urge funding agencies to provide additional grants for multi-hazard risk research.
Mohammad Amin Hariri-Ardebili. Living in a Multi-Risk Chaotic Condition: Pandemic, Natural Hazards and Complex Emergencies. International Journal of Environmental Research and Public Health 2020, 17, 5635 .
AMA StyleMohammad Amin Hariri-Ardebili. Living in a Multi-Risk Chaotic Condition: Pandemic, Natural Hazards and Complex Emergencies. International Journal of Environmental Research and Public Health. 2020; 17 (16):5635.
Chicago/Turabian StyleMohammad Amin Hariri-Ardebili. 2020. "Living in a Multi-Risk Chaotic Condition: Pandemic, Natural Hazards and Complex Emergencies." International Journal of Environmental Research and Public Health 17, no. 16: 5635.
The magnitude of leakage in the dam body and its foundation can be used as an important indicator in dam risk management. This study presents a data mining and monitoring framework for safety control of the dam leakage flow. First, the influencing factors in dam leakage flow are investigated. Second, a kernel extreme learning machine (KELM) is trained to predict dam leakage, where the parameters are optimized adaptively by parallel multi-population Jaya algorithm. Finally, a novel global sensitivity analysis is proposed to evaluate the relative importance of each input variable based on the KELM. Monitoring data of leakage flow from the concrete face rockfill dam in a pumped-storage power station is used as a vehicle for post-possessing. The simulated results of the case study reveal that KELM achieves a satisfactory prediction of the leakage flow. It is found that the water level fluctuation and rainfall have a significant impact on leakage magnitude. The sensitivity analysis provides a useful qualitative metric of dam leakage, which is of great value for dam safety monitoring and operation.
Siyu Chen; Chongshi Gu; Chaoning Lin; Yao Wang; Mohammad Amin Hariri-Ardebili. Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine. Measurement 2020, 166, 108161 .
AMA StyleSiyu Chen, Chongshi Gu, Chaoning Lin, Yao Wang, Mohammad Amin Hariri-Ardebili. Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine. Measurement. 2020; 166 ():108161.
Chicago/Turabian StyleSiyu Chen; Chongshi Gu; Chaoning Lin; Yao Wang; Mohammad Amin Hariri-Ardebili. 2020. "Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine." Measurement 166, no. : 108161.
Application of fiber reinforced concrete (FRC) has been increased in the past decade due to its enhanced structural performance. Therefore, it is important to fully characterize its static and dynamic behavior under different loading scenarios. In this paper, the effects of low-percentage steel fibers with four different fiber volume is investigated on the dynamic responses of concrete slabs. First, a series of experimental tests are conducted. Next, the results are used to validate a nonlinear finite element model under impact loading. Finally, a large dataset of numerical simulations are developed using appropriate design of experiments, and four soft computing techniques are used to predict the target responses. The findings show that application of low-percentages fiber increases the acceleration response of the concrete slabs subjected to impact load, on the average of 102%. The slab’s stiffness increases with an increase in fiber percentage. The dominant frequency, the damping ratio, as well as the slab’s cracking pattern, and the failure modes indicate that FRC slabs are stiffer than conventional RC specimen. The numerical simulations reasonably estimate the maximum acceleration. Outcome of the predictive meta-models can be used to estimate the dynamic behavior of similar slabs with no additional experimental and numerical costs.
Kambiz Daneshvar; Mohammad Javad Moradi; Morteza Amooie; Siyu Chen; Golsa Mahdavi; Mohammad Amin Hariri-Ardebili. Response of low-percentage FRC slabs under impact loading: Experimental, numerical, and soft computing methods. Structures 2020, 27, 975 -988.
AMA StyleKambiz Daneshvar, Mohammad Javad Moradi, Morteza Amooie, Siyu Chen, Golsa Mahdavi, Mohammad Amin Hariri-Ardebili. Response of low-percentage FRC slabs under impact loading: Experimental, numerical, and soft computing methods. Structures. 2020; 27 ():975-988.
Chicago/Turabian StyleKambiz Daneshvar; Mohammad Javad Moradi; Morteza Amooie; Siyu Chen; Golsa Mahdavi; Mohammad Amin Hariri-Ardebili. 2020. "Response of low-percentage FRC slabs under impact loading: Experimental, numerical, and soft computing methods." Structures 27, no. : 975-988.
It is well accepted that risk analyses of nuclear facilities should be interpreted in the context of probabilistic methods. Nearly all of the current applications only focus on the simulation-based random variable (RV) uncertainty quantification, while the impacts of random fields (RF) are ignored. Since the concrete is a heterogeneous material in different length scales, this paper presents the results of a study that considers the spatial distribution of several concrete parameters in the context of the macro-scale RF theory. A series of probabilistic analyses are designed based on typical structural components of a nuclear facility, i.e., beams, columns, shear walls, and the containment building. Both the strength and vibration characteristics are quantified. The impacts of single/multiple RV(s), correlation length, and construction technique are discussed. The uncertainty and dispersion of the capacity points are determined in each component and compared with each other. Moreover, a model framework is proposed for calibrating large heterogeneous structures, and RF-dependent reliability analysis.
Mohammad Amin Hariri-Ardebili. Safety and reliability assessment of heterogeneous concrete components in nuclear structures. Reliability Engineering & System Safety 2020, 203, 107104 .
AMA StyleMohammad Amin Hariri-Ardebili. Safety and reliability assessment of heterogeneous concrete components in nuclear structures. Reliability Engineering & System Safety. 2020; 203 ():107104.
Chicago/Turabian StyleMohammad Amin Hariri-Ardebili. 2020. "Safety and reliability assessment of heterogeneous concrete components in nuclear structures." Reliability Engineering & System Safety 203, no. : 107104.
Mohammad Amin Hariri-Ardebili; Leandro Sanchez; Roozbeh Rezakhani. Aging of Concrete Structures and Infrastructures: Causes, Consequences, and Cures (C3). Advances in Materials Science and Engineering 2020, 2020, 1 -3.
AMA StyleMohammad Amin Hariri-Ardebili, Leandro Sanchez, Roozbeh Rezakhani. Aging of Concrete Structures and Infrastructures: Causes, Consequences, and Cures (C3). Advances in Materials Science and Engineering. 2020; 2020 ():1-3.
Chicago/Turabian StyleMohammad Amin Hariri-Ardebili; Leandro Sanchez; Roozbeh Rezakhani. 2020. "Aging of Concrete Structures and Infrastructures: Causes, Consequences, and Cures (C3)." Advances in Materials Science and Engineering 2020, no. : 1-3.
The expansion of water resources is the key factor in the socio-economic development of all countries
Mohammad Amin Hariri-Ardebili; Jerzy Salamon; Guido Mazza; Hasan Tosun; Bin Xu. Advances in Dam Engineering. Infrastructures 2020, 5, 39 .
AMA StyleMohammad Amin Hariri-Ardebili, Jerzy Salamon, Guido Mazza, Hasan Tosun, Bin Xu. Advances in Dam Engineering. Infrastructures. 2020; 5 (5):39.
Chicago/Turabian StyleMohammad Amin Hariri-Ardebili; Jerzy Salamon; Guido Mazza; Hasan Tosun; Bin Xu. 2020. "Advances in Dam Engineering." Infrastructures 5, no. 5: 39.
Following a comprehensive literature survey, this paper will first address the theoretical underpinnings of stochastic modeling. An algorithm to model arch dam inhomogeneity in terms of characteristic length is presented next and is applied to assess the impact of concrete's elastic modulus and volumetric AAR expansion. Results are contrasted with both a single deterministic analysis, and a series of classical Monte Carlo simulations in which non-Gaussian stochastically varying properties are used. The impact of randomness in material properties on displacements, joint openings, and stresses are investigated. It is found that whereas mean values of these responses are for the most part little impacted, standard deviations exhibit a greater variation vis a vis simple Monte Carlo simulations. Furthermore, the safety is assessed through fragility surfaces, and meta-modeling. This study determined that whereas randomness may affect local results e.g. stresses, their impact may be neglected for globally averaged responses e.g. displacements.
Victor E. Saouma; Mohammad Amin Hariri-Ardebili; Lori Graham-Brady. Stochastic analysis of concrete dams with alkali aggregate reaction. Cement and Concrete Research 2020, 132, 106032 .
AMA StyleVictor E. Saouma, Mohammad Amin Hariri-Ardebili, Lori Graham-Brady. Stochastic analysis of concrete dams with alkali aggregate reaction. Cement and Concrete Research. 2020; 132 ():106032.
Chicago/Turabian StyleVictor E. Saouma; Mohammad Amin Hariri-Ardebili; Lori Graham-Brady. 2020. "Stochastic analysis of concrete dams with alkali aggregate reaction." Cement and Concrete Research 132, no. : 106032.