This page has only limited features, please log in for full access.
Resilience is defined as the ability of a system to withstand and recover to a desired level of performance after the occurrence of a hazard. Community resilience has a significant socioeconomic implication for any disaster. Therefore, attempting to quantify resilience after a disaster is of utmost importance, particularly for planners, designers, and decision makers. Modern society depends on various infrastructure system networks to ensure functionality, and these infrastructure systems perform on their own and also perform interdependently with other infrastructure networks during natural hazards. For quantifying resilience, the interdependency between infrastructure systems plays a significant role; for instance, in the event of building damage, the state of damage to the roadways network is also crucial for the recovery process and ultimately in resilience. As a result, large-scale disruption of any infrastructure network increases significantly because of interdependency. In this work, an integrated geographic information system (GIS) and Bayesian belief network (BBN) framework is developed to study the resilience and effects in functionality due to interdependency among building and roadways infrastructure systems in a community. GIS is used for data collection, and BBN is adopted for computing the posterior probabilities of resilience. The framework is then implemented in a study area of Barak Valley in North-East India, and resilience is evaluated for the considered building-roadways network. Sensitivity analysis of system resilience to the critical components is performed to facilitate decision making under uncertainty. Finally, some general recommendations are given for improving flood resilience for future disasters.
Mrinal Kanti Sen; Subhrajit Dutta; Amir H. Gandomi; Chandrasekhar Putcha. Case Study for Quantifying Flood Resilience of Interdependent Building–Roadway Infrastructure Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2021, 7, 04021005 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Amir H. Gandomi, Chandrasekhar Putcha. Case Study for Quantifying Flood Resilience of Interdependent Building–Roadway Infrastructure Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2021; 7 (2):04021005.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Amir H. Gandomi; Chandrasekhar Putcha. 2021. "Case Study for Quantifying Flood Resilience of Interdependent Building–Roadway Infrastructure Systems." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7, no. 2: 04021005.
Resilience is defined as the capacity of a system to withstand a natural hazard and to regain desirable performance after the occurrence of such disasters. Natural hazards, such as floods, earthquakes, hurricanes, and tsunamis, have devastating effects on infrastructure systems. Such high-consequence events create the need for building resilient infrastructure for sustainable development. However, resilience-based infrastructure design is a challenging task, primarily due to factors such as lack of appropriate data for quantifying infrastructure resilience, and robustness of resilience models. Hence, there is a definite need to build resilience models based on realistic data and to validate such models. This paper developed a hierarchical Bayesian network (BN) model for flood resilience of housing infrastructure, and used the variable elimination (VE) method to quantify flood resilience. A study area in Barak Valley of Northeast India was selected because frequent high consequence flood events have occurred in this region. Relevant data were collected by performing an extensive field survey in various places of the valley, and were used to quantify two major factors—reliability and recovery—on which housing infrastructure resilience quantification depends. The main advantages of the proposed resilience model are that (1) it gives a realistic scenario of the infrastructure system robustness and its restoration after damage, (2) the proposed BN-based data-driven resilience model can be updated as and when more data are available, and (3) it helps planners, designers, policymakers, and stakeholders to make resilience-based decisions for sustainable communities.
Mrinal Kanti Sen; Subhrajit Dutta; Jahir Iqbal Laskar. A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2021, 7, 04020060 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Jahir Iqbal Laskar. A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2021; 7 (1):04020060.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Jahir Iqbal Laskar. 2021. "A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7, no. 1: 04020060.
Resilience is the capability of a system to resist any hazard and revive to a desirable performance. The consequences of such hazards require the development of resilient infrastructure to ensure community safety and sustainability. However, resilience-based housing infrastructure design is a challenging task due to a lack of appropriate post-disaster datasets and the non-availability of resilience models for housing infrastructure. Hence, it is necessary to build a resilience model for housing infrastructure based on a realistic dataset. In this work, a Bayesian belief network (BBN) model was developed for housing infrastructure resilience. The proposed model was tested in a real community in Northeast India and the reliability, recovery, and resilience of housing infrastructure against flood hazards for that community were quantified. The required data for resilience quantification were collected by conducting a field survey and from public reports and documents. Lastly, a sensitivity analysis was performed to observe the critical parameters of the proposed BBN model, which can be used to inform designers, policymakers, and stakeholders in making resilience-based decisions.
Mrinal Kanti Sen; Subhrajit Dutta; Golam Kabir. Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network. Sustainability 2021, 13, 1026 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Golam Kabir. Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network. Sustainability. 2021; 13 (3):1026.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Golam Kabir. 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network." Sustainability 13, no. 3: 1026.
Housing infrastructure is a basic human need and thus should be resilient against natural disasters. Therefore, an effective resilience-based framework for housing infrastructure is required to enhance the endurance capacity of housing and for effective recovery. Involving an expert opinion is also required for the representation of the nonlinear, complex relationship between the flood resilience parameters for housing infrastructure. In this work, initially, a hierarchical flood resilience model for housing infrastructure is developed using the decision-making trial and evaluation laboratory (DEMATEL) and the interpretive structure modeling (ISM) methods. The relationships among various resilience parameters related to flood hazards are obtained using integrated DEMATEL and ISM following the data received from expert opinions. Next, a flood resilience-based decision-making framework is developed integrating DEMATEL, ISM, and Bayesian Network (BN) methods. A field survey collects all the postdisaster data to feed in the BN model, capturing the interrelationship among the resilience parameters. Finally, the developed framework is implemented for a community in northeast India for flood resilience assessment and risk-informed decision making. It has been observed from the result that the resilience of the housing infrastructure for most of the surveyed places are extremely low, which means houses of those areas should be immediately strengthened. The evaluated resilience values provide the vulnerable scenarios of housing infrastructure of that community against flood hazards, which help the public authority of that community. Additionally, the identification of the most sensitive parameter/s among all considered parameters helps in decision making for future hazards.
Mrinal Kanti Sen; Subhrajit Dutta; Golam Kabir; Nikil N. Pujari; Shamim Ahmed Laskar. An integrated approach for modelling and quantifying housing infrastructure resilience against flood hazard. Journal of Cleaner Production 2020, 288, 125526 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Golam Kabir, Nikil N. Pujari, Shamim Ahmed Laskar. An integrated approach for modelling and quantifying housing infrastructure resilience against flood hazard. Journal of Cleaner Production. 2020; 288 ():125526.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Golam Kabir; Nikil N. Pujari; Shamim Ahmed Laskar. 2020. "An integrated approach for modelling and quantifying housing infrastructure resilience against flood hazard." Journal of Cleaner Production 288, no. : 125526.
Infrastructure resilience is defined as the ability of a system to withstand and recover from the effects of natural or man-made hazards. For any community, quantifying its sociophysical infrastructure resilience during and after any disruptive event is important for planners, designers, and decision-makers. However, a global approach for resilience quantification becomes challenging due to the fact that infrastructure systems’ performance varies from location to location and the recovery process is also complex and region-specific. In this work, an integrated Geographic Information System (GIS)-Bayesian Belief Network (BBN) framework is developed to model and quantify the resilience (vulnerability and recovery) of network infrastructure systems against flood hazards. To this end, a simple case study is demonstrated for quantifying flood resilience of a roadway network in a community in northeast India. Data collection is done using a GIS platform and a probabilistic graphical model (BBN model) is used to model uncertainties in resilience quantification based on the available data and judgments. The main contributions of the proposed resilience model are: (1) the model can provide more accurate and realistic estimates based on beliefs; (2) the model can be updated as and when more data is available; and (3) sensitivity analysis of the validated road network resilience model to facilitate risk-informed decision-making against future flood disaster.
Mrinal Kanti Sen; Subhrajit Dutta. An Integrated GIS-BBN Approach to Quantify Resilience of Roadways Network Infrastructure System against Flood Hazard. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2020, 6, 04020045 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta. An Integrated GIS-BBN Approach to Quantify Resilience of Roadways Network Infrastructure System against Flood Hazard. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2020; 6 (4):04020045.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta. 2020. "An Integrated GIS-BBN Approach to Quantify Resilience of Roadways Network Infrastructure System against Flood Hazard." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 6, no. 4: 04020045.
Housing infrastructure is the basic need of living, and due to disaster, many houses got damaged. Therefore, assessing resilience for housing infrastructure against a flood hazard is an important task for any community as it gives the real scenario of the capability to resist and recover from the disaster after the occurrence of the hazard. The process of resilience quantification requires a different type of information from different sources, and due to which uncertainty and incomplete data may get involved. There is significantly limited literature available focusing on housing infrastructure resilience; however, the available literature has not incorporated such uncertainty and incomplete information. Therefore, in this work, a resilience assessment framework for housing infrastructure is proposed using a combination approach of Best Worst Method and a Hierarchical Evidential Reasoning based on the Dempster-Shafer theory against flood hazard. The proposed framework is then implemented in Barak valley North-East India to quantify that valley’s resilience and evaluate the model. Initially, different resilience attributes are selected, and based on experts’ opinion, the Best Worst Method rates the criteria to find the weightage. After finding the weightage, flood resilience is evaluated by using the Dempster-Shafer rule of combination. Lastly, sensitivity analysis is also performed to investigate the sensitivity of all the attributes of the proposed hierarchical housing infrastructure resilience model. The proposed flood resilience assessment model generates satisfactory results which indicate the condition state of resilience along with the unassigned degree of belief or uncertainty.
Mrinal Kanti Sen; Subhrajit Dutta; Golam Kabir. Development of flood resilience framework for housing infrastructure system: Integration of best-worst method with evidence theory. Journal of Cleaner Production 2020, 290, 125197 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Golam Kabir. Development of flood resilience framework for housing infrastructure system: Integration of best-worst method with evidence theory. Journal of Cleaner Production. 2020; 290 ():125197.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Golam Kabir. 2020. "Development of flood resilience framework for housing infrastructure system: Integration of best-worst method with evidence theory." Journal of Cleaner Production 290, no. : 125197.
Optimization under uncertainty (OUU) is a robust framework to obtain optimal designs for real engineering problems considering uncertainties. The numerical solution for large-scale problems involving millions of degrees-of-freedom is typically computation-intensive in nature. Also, OUU problems constitutes an uncertainty analysis, involving a computation-intensive numerical solver for large-scale systems. Hence, the solution of OUU problems are computationally demanding in nature. In this study, a bilevel data-driven modeling framework is proposed using proper orthogonal decomposition (POD) and polynomial chaos expansion (PCE) metamodels. A heuristic particle swarm optimization (PSO) technique is used for optimization. The effectiveness of the POD-PCE metamodel combined with PSO is demonstrated for two practical large-scale structural optimizations under uncertainty problems. From the case studies, it has been observed that the proposed method gives solutions that are almost hundreds and thousands of times faster as compared to the crude Monte Carlo simulation.
Subhrajit Dutta; Amir H. Gandomi. Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems. Journal of Structural Engineering 2020, 146, 04020245 .
AMA StyleSubhrajit Dutta, Amir H. Gandomi. Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems. Journal of Structural Engineering. 2020; 146 (11):04020245.
Chicago/Turabian StyleSubhrajit Dutta; Amir H. Gandomi. 2020. "Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems." Journal of Structural Engineering 146, no. 11: 04020245.
Engineering optimization problems are challenging to solve mainly due to their numerical modeling and analysis complexities. This chapter deals with the efficient use of surrogate model-driven evolutionary algorithms, built hierarchically for the solution of large-scale computation intensive optimization problems. In most optimization problems, the majority of computation is involved in repetitive function calls to evaluate the system response/bahaviour under consideration. The quality solutions depends on the system response estimation, and in most cases high fidelity models are used to get accurate results. Conventional evolutionary algorithms require a great number of such high fidelity function calls. Here, we use low cost surrogate models or metamodels, which approximate the original model mathematically, but significantly reduce the computation cost for a desired accuracy level. The surrogate model training requires a small amount of evaluations of the original model at support points. The hierarchical surrogate model-based PSO algorithms we propose are tested on a range of large-scale design optimization problems and compared with other well-known surrogate modeling techniques.
Subhrajit Dutta; Amir H. Gandomi. Surrogate Model-Driven Evolutionary Algorithms: Theory and Applications. Genetic Programming Theory and Practice XIII 2020, 435 -451.
AMA StyleSubhrajit Dutta, Amir H. Gandomi. Surrogate Model-Driven Evolutionary Algorithms: Theory and Applications. Genetic Programming Theory and Practice XIII. 2020; ():435-451.
Chicago/Turabian StyleSubhrajit Dutta; Amir H. Gandomi. 2020. "Surrogate Model-Driven Evolutionary Algorithms: Theory and Applications." Genetic Programming Theory and Practice XIII , no. : 435-451.
Infrastructure systems are the backbones of the socioeconomic development of a community. However, after installation, these engineered systems undergo deterioration, leading to a degradation in their condition while in operation. In this work, a generalized modeling framework is proposed and validated for the diagnosis and prognosis of infrastructure systems based on real-time data. A data-driven modeling scheme, dynamic mode decomposition (DMD), is used for prognosis. The novelty of the proposed framework lies in the fact that the developed prognostic model is data-driven and physics informed, and the model works better on problems with unknown/implicit governing equations and boundary conditions. The developed prognostic model provides more accurate predictions based on real-time data and identification of dominant spatiotemporal modes, as evident from the application of mortar cube crack prediction under compressive testing. This framework can be recommended to researchers/practitioners for predicting the remaining useful life of infrastructure components and systems before their maintenance or failure. Such robust predictions of the future condition of existing infrastructure will be beneficial to stakeholders for sustainable development.
Sandeep Das; Subhrajit Dutta; Chandrasekhar Putcha; Shubhankar Majumdar; Dibyendu Adak. A Data-Driven Physics-Informed Method for Prognosis of Infrastructure Systems: Theory and Application to Crack Prediction. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2020, 6, 04020013 .
AMA StyleSandeep Das, Subhrajit Dutta, Chandrasekhar Putcha, Shubhankar Majumdar, Dibyendu Adak. A Data-Driven Physics-Informed Method for Prognosis of Infrastructure Systems: Theory and Application to Crack Prediction. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2020; 6 (2):04020013.
Chicago/Turabian StyleSandeep Das; Subhrajit Dutta; Chandrasekhar Putcha; Shubhankar Majumdar; Dibyendu Adak. 2020. "A Data-Driven Physics-Informed Method for Prognosis of Infrastructure Systems: Theory and Application to Crack Prediction." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 6, no. 2: 04020013.
Tensile membrane structures are gaining popularity due to their efficiency, lightweight properties and aesthetic appeal. The focus on more efficient, sustainable and environment-friendly forms of construction makes these structures a suitable choice. The limited guidelines and a general lack of experience, however, slow down the adoption of such structures. Its design process consists of form finding, analysis and patterning, which are not familiar to all designers. This paper presents an overview of the design of tensile membrane structures, their characteristics and other challenges. The central concept of form finding and its different approaches are described here, along with a brief review of load analysis and patterning. The future scope of improvement in the adoption and design of tensile membrane structures are presented by highlighting the different challenges faced in this field.
Allan L. Marbaniang; Subhrajit Dutta; Siddhartha Ghosh. Tensile Membrane Structures: An Overview. Lecture Notes in Civil Engineering 2020, 29 -40.
AMA StyleAllan L. Marbaniang, Subhrajit Dutta, Siddhartha Ghosh. Tensile Membrane Structures: An Overview. Lecture Notes in Civil Engineering. 2020; ():29-40.
Chicago/Turabian StyleAllan L. Marbaniang; Subhrajit Dutta; Siddhartha Ghosh. 2020. "Tensile Membrane Structures: An Overview." Lecture Notes in Civil Engineering , no. : 29-40.
Additive printing allows the “single step” production of virtually any complex mechanical component. However, the manufacturing process involves a layer-by-layer deposition of material, which leads to an anisotropic mechanical behavior of the whole component. This would then entail a very fine 3D model to simulate the mechanical performance accurately. This simulation would also need to be integrated within an iterative design process in order to obtain the most efficient design. Both reasons explain the prohibitive number of calculations needed, which is currently beyond the capacities of existing software and computers. Recent research papers have opened promising pathways for integrating model reduction techniques within the overall topology optimization process. However, these approaches still present challenges such as choosing the minimum number and appropriate selection of the snapshots required to get accurate simulations. In this work, we present a methodology in the combined field of reduced-order modeling and topology optimization. The key idea consists of projecting the higher dimensional system of equations onto a lower dimensional space with the reduced basis vectors constructed using Proper Orthogonal Decomposition (POD). This reduced basis is updated in an incremental “on-the-fly” manner using alternatively costly high-fidelity and more rapid lower fidelity simulation snapshots. The variable-fidelity resolutions of successive approximations to the global system of equations are then integrated into the topology optimization process. The approaches are tested and computational savings and precision are compared, using both minimum compliance and compliant mechanism design benchmark problems.
Manyu Xiao; Dongcheng Lu; Piotr Breitkopf; Balaji Raghavan; Weihong Zhang; Subhrajit Dutta. Multi-grid reduced-order topology optimization. Structural and Multidisciplinary Optimization 2020, 61, 1 -23.
AMA StyleManyu Xiao, Dongcheng Lu, Piotr Breitkopf, Balaji Raghavan, Weihong Zhang, Subhrajit Dutta. Multi-grid reduced-order topology optimization. Structural and Multidisciplinary Optimization. 2020; 61 (6):1-23.
Chicago/Turabian StyleManyu Xiao; Dongcheng Lu; Piotr Breitkopf; Balaji Raghavan; Weihong Zhang; Subhrajit Dutta. 2020. "Multi-grid reduced-order topology optimization." Structural and Multidisciplinary Optimization 61, no. 6: 1-23.
Chandrasekhar Putcha; Subhrajit Dutta; Jessica Rodriguez. Risk Priority Number for Bridge Failures. Practice Periodical on Structural Design and Construction 2020, 25, 04020010 .
AMA StyleChandrasekhar Putcha, Subhrajit Dutta, Jessica Rodriguez. Risk Priority Number for Bridge Failures. Practice Periodical on Structural Design and Construction. 2020; 25 (2):04020010.
Chicago/Turabian StyleChandrasekhar Putcha; Subhrajit Dutta; Jessica Rodriguez. 2020. "Risk Priority Number for Bridge Failures." Practice Periodical on Structural Design and Construction 25, no. 2: 04020010.
Optimization under uncertainty (OUU) provides robust optimal design solutions for real engineering problems considering uncertainties. These OUU problems involves a costly inner loop uncertainty quantification, involving a computation-intensive numerical solver for large-scale real systems with significantly higher degrees of freedom. The current work is aimed at reducing this cost of computation in OUU. To this end, a sequential polynomial chaos expansion (PCE) and kriging based metamodel is used. This metamodel is later adopted to substitute the actual expensive true numerical model solver in the uncertainty analysis computation phase. Particle swarm optimization (PSO) is used for optimization, leveraging on the properties of stochastic search. The effectiveness of PCE-kriging metamodel combined with PSO is demonstrated for optimization of two transmission towers. It has been observed that the proposed metamodel-based approach for OUU of a 244 member large-scale tower provides significantly faster and accurate solutions.
Subhrajit Dutta. A sequential metamodel-based method for structural optimization under uncertainty. Structures 2020, 26, 54 -65.
AMA StyleSubhrajit Dutta. A sequential metamodel-based method for structural optimization under uncertainty. Structures. 2020; 26 ():54-65.
Chicago/Turabian StyleSubhrajit Dutta. 2020. "A sequential metamodel-based method for structural optimization under uncertainty." Structures 26, no. : 54-65.
Despite a solid theoretical foundation and straightforward application to structural design problems, 3D topology optimization still suffers from a prohibitively high computational effort that hinders its widespread use in industrial design. One major contributor to this problem is the cost of solving the finite element equations during each iteration of the optimization loop. To alleviate this cost in large-scale topology optimization, the authors propose a projection-based reduced-order modeling approach using proper orthogonal decomposition for the construction of a reduced basis for the FE solution during the optimization, using a small number of previously obtained and stored solutions. This basis is then adaptively enriched and updated on-the-fly according to an error residual, until convergence of the main optimization loop. The method of moving asymptotes is used for the optimization. The techniques are validated using established 3D benchmark problems. The numerical results demonstrate the advantages and the improved performance of our proposed approach.
Manyu Xiao; Dongcheng Lu; Piotr Breitkopf; Balaji Raghavan; Subhrajit Dutta; Weihong Zhang. On-the-fly model reduction for large-scale structural topology optimization using principal components analysis. Structural and Multidisciplinary Optimization 2020, 62, 209 -230.
AMA StyleManyu Xiao, Dongcheng Lu, Piotr Breitkopf, Balaji Raghavan, Subhrajit Dutta, Weihong Zhang. On-the-fly model reduction for large-scale structural topology optimization using principal components analysis. Structural and Multidisciplinary Optimization. 2020; 62 (1):209-230.
Chicago/Turabian StyleManyu Xiao; Dongcheng Lu; Piotr Breitkopf; Balaji Raghavan; Subhrajit Dutta; Weihong Zhang. 2020. "On-the-fly model reduction for large-scale structural topology optimization using principal components analysis." Structural and Multidisciplinary Optimization 62, no. 1: 209-230.
In the past decade, uncertainty quantification (UQ) has received much attention, particularly in the research areas of reliability and risk analysis, sensitivity analysis, and optimization under uncertainty, to mention a few. In the context of UQ, one of the major challenges is the computational demand of the numerical (finite element) model that is used to analyze the large-scale engineering systems under consideration. Metamodels or surrogate models are often used as substitutes to those high-fidelity numerical models to overcome this issue. Polynomial chaos expansion (PCE) has been considered as one of the promising metamodeling methods. To build a PCE metamodel, design of experiments (DoEs) are carried out, i.e., determining the design points (in the input space) where the original (high-fidelity) computational model needs to be evaluated. The accuracy level of the metamodel depends on the DoE over the input design space. This chapter will introduce some state-of-the-art DoEs used for uncertainty quantification problems. A comparative study is performed to show the efficiency and limitations of the various experimental designs in uncertainty quantification of engineered systems with varying input dimensionality and computational complexity.
Subhrajit Dutta; Amir Gandomi. Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels. Handbook of Probabilistic Models 2019, 369 -381.
AMA StyleSubhrajit Dutta, Amir Gandomi. Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels. Handbook of Probabilistic Models. 2019; ():369-381.
Chicago/Turabian StyleSubhrajit Dutta; Amir Gandomi. 2019. "Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels." Handbook of Probabilistic Models , no. : 369-381.
Optimal design of engineering systems considering uncertainties is broadly dealt in the area of reliability-based design optimization (RBDO). These RBDO problems involve a costly inner loop reliability estimate involving a computation-intensive true model solver. The current work is aimed at reducing this cost of computation in RBDO. To this end, a polynomial chaos expansion (PCE) metamodel is used. This PCE metamodel is later used to substitute the actual expensive true model in the reliability computation phase. A stochastic optimizer—particle swarm optimization (PSO) is used for the outer optimization loop. The effectiveness of PCE metamodel combined with PSO is demonstrated for a large-scale truss structural system in reducing the overall computation cost involved in RBDO.
Subhrajit Dutta; Chandrasekhar Putcha. Reliability-Based Design Optimization of a Large-Scale Truss Structure Using Polynomial Chaos Expansion Metamodel. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) 2019, 481 -488.
AMA StyleSubhrajit Dutta, Chandrasekhar Putcha. Reliability-Based Design Optimization of a Large-Scale Truss Structure Using Polynomial Chaos Expansion Metamodel. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). 2019; ():481-488.
Chicago/Turabian StyleSubhrajit Dutta; Chandrasekhar Putcha. 2019. "Reliability-Based Design Optimization of a Large-Scale Truss Structure Using Polynomial Chaos Expansion Metamodel." Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) , no. : 481-488.
Tensile membrane structures (TMS) are increasingly in demand due to their ability to span large distances with elegance and structural efficiency. Because TMS is comparatively new to the structural engineering world, there are relatively limited resources available explaining the behavior of such structures. Most countries do not have documented guidelines for the analysis and design of TMS. This paper presents the existing challenges and provides some recommendations on the analysis and design of membrane structures. Emphasis is placed on form-finding, which is the initial equilibrium analysis of membranes. Attention is also given to understanding the behavior of TMS subjected to environmental forces, especially because of their inherent flexibility that imparts a complex interaction between the form and the applied forces.
Subhrajit Dutta; Siddhartha Ghosh. Analysis and Design of Tensile Membrane Structures: Challenges and Recommendations. Practice Periodical on Structural Design and Construction 2019, 24, 04019009 .
AMA StyleSubhrajit Dutta, Siddhartha Ghosh. Analysis and Design of Tensile Membrane Structures: Challenges and Recommendations. Practice Periodical on Structural Design and Construction. 2019; 24 (3):04019009.
Chicago/Turabian StyleSubhrajit Dutta; Siddhartha Ghosh. 2019. "Analysis and Design of Tensile Membrane Structures: Challenges and Recommendations." Practice Periodical on Structural Design and Construction 24, no. 3: 04019009.
Tensile membrane structures (TMS) are light-weight flexible structures that are designed to span long distances with structural efficiency. The stability of a TMS is jeopardised under heavy wind forces due to its inherent flexibility and inability to carry out-of-plane moment and shear. A stable TMS under uncertain wind loads (without any tearing failure) can only be achieved by a proper choice of the initial prestress. In this work, a double-loop reliability-based design optimisation (RBDO) of TMS under uncertain wind load is proposed. Using a sequential polynomial chaos expansion (PCE) and kriging based metamodel, this RBDO reduces the cost of inner-loop reliability analysis involving an intensive finite element solver. The proposed general approach is applied to the RBDO of two benchmark TMS and its computational efficiency is demonstrated through these case studies. The method developed here is suggested for RBDO of large and complex engineering systems requiring costly numerical solution.
Subhrajit Dutta; Siddhartha Ghosh; Mandar M. Inamdar. Optimisation of tensile membrane structures under uncertain wind loads using PCE and kriging based metamodels. Structural and Multidisciplinary Optimization 2017, 57, 1149 -1161.
AMA StyleSubhrajit Dutta, Siddhartha Ghosh, Mandar M. Inamdar. Optimisation of tensile membrane structures under uncertain wind loads using PCE and kriging based metamodels. Structural and Multidisciplinary Optimization. 2017; 57 (3):1149-1161.
Chicago/Turabian StyleSubhrajit Dutta; Siddhartha Ghosh; Mandar M. Inamdar. 2017. "Optimisation of tensile membrane structures under uncertain wind loads using PCE and kriging based metamodels." Structural and Multidisciplinary Optimization 57, no. 3: 1149-1161.
Due to the inherent flexibility of tensile membrane structures (TMS), they need to remain in a stable equilibrium condition in the presence of gusty winds as well as in their absence. This paper is aimed at the reliability-based optimization of frame-supported tensile membrane structures subjected to uncertain wind loads. The transient membrane displacement is minimized under this random loading constrained to a stable TMS form and a maximum failure probability against membrane tearing. A particle swarm optimization algorithm is used, combined with Latin hypercube sampling and response surface approach, for obtaining the optimum initial prestress required. These algorithms balance the computationally demanding dynamic relaxation method required for the membrane structural analysis. The proposed methodology is demonstrated through the example of a frame-supported conic membrane structure. The results show that the proposed method can effectively optimize the TMS performance under random wind forces, within manageable computation time.
Subhrajit Dutta; Siddhartha Ghosh; Mandar M. Inamdar. Reliability-Based Design Optimization of Frame-Supported Tensile Membrane Structures. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2017, 3, 1 .
AMA StyleSubhrajit Dutta, Siddhartha Ghosh, Mandar M. Inamdar. Reliability-Based Design Optimization of Frame-Supported Tensile Membrane Structures. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2017; 3 (2):1.
Chicago/Turabian StyleSubhrajit Dutta; Siddhartha Ghosh; Mandar M. Inamdar. 2017. "Reliability-Based Design Optimization of Frame-Supported Tensile Membrane Structures." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 3, no. 2: 1.
Subhrajit Dutta; Siddhartha Ghosh; Mandar M. Inamdar; Manolis Papadrakakis. STOCHASTIC OPTIMISATION OF THE INITIAL PRESTRESS FOR A TENSILE MEMBRANE STRUCTURE SUBJECTED TO UNCERTAIN WIND FORCES. Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2015) 2015, 297 -304.
AMA StyleSubhrajit Dutta, Siddhartha Ghosh, Mandar M. Inamdar, Manolis Papadrakakis. STOCHASTIC OPTIMISATION OF THE INITIAL PRESTRESS FOR A TENSILE MEMBRANE STRUCTURE SUBJECTED TO UNCERTAIN WIND FORCES. Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2015). 2015; ():297-304.
Chicago/Turabian StyleSubhrajit Dutta; Siddhartha Ghosh; Mandar M. Inamdar; Manolis Papadrakakis. 2015. "STOCHASTIC OPTIMISATION OF THE INITIAL PRESTRESS FOR A TENSILE MEMBRANE STRUCTURE SUBJECTED TO UNCERTAIN WIND FORCES." Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2015) , no. : 297-304.