Heating temperature prediction of concrete structure damaged by fire using a Bayesian approach

A fire that occurs in a reinforced concrete (RC) structure accompanies a heating temperature, and this negatively affects the concrete material properties, such as the compressive strength, the bond between cement paste and aggregate, and the cracking and spalling of concrete. To appropriately measure the reduced structural performance and durability of fire-damaged RC structures, it is important to accurately estimate the heating temperature of the structure. However, studies in the literature on RC structures damaged by fire have focused mostly on structural member tests at elevated temperatures to ensure the fire resistance or fire protection material development; studies on estimating the heating temperature are very limited except for the very few existing models. Therefore, in this study, a heating temperature estimation model for a reinforced concrete (RC) structure damaged by fire was developed using a statistical Bayesian parameter estimation approach. For the model development, a total of 77 concrete test specimens were utilized; based on them, a statistically highly accurate model has been developed. The usage of the proposed method in the framework of the 500 ◦C isotherm method in Eurocode 2 has been illustrated through an RC column resistance estimation application

Structural safety inspection of reinforced concrete structures considering failure probabilities of structural members

Regular safety inspections of existing reinforced concrete (RC) structures are required according to the regulations and criteria set by each country. In South Korea, the safety inspection regulations provided by the Korea Infrastructure Safety and Technology Corporation (KISTEC) are followed. These regulations were developed based on fuzzy theory to avoid subjective decisions, and provide standardized deterioration grades for member types, floors, and the entire structure. However, the safety inspection regulation by the KISTEC often provides unconservative evaluation results. In particular, as the importance factors of beam and slab members are set lower than those of other members, there are cases in which deteriorations occurring in beams and slabs are not properly reflected in the floor level evaluation. In this study, to overcome such limitations, case studies were carried out and modified importance factors for structural member types were proposed considering the failure probabilities of each member type based on the reliability theory. The importance modification factor was calculated based on the strength ratio of structural members so that the more dangerous the members are, the more impact they give on the evaluation. Overall, compared to the KISTEC method, the proposed method provided conservative but practical assessment results, and it was found that the proposed importance factors can be very useful to properly reflect the effects of damaged members on the deterioration status evaluation of the floors and the entire structure

An investigation of machine learning techniques to estimate minimum horizontal stress magnitude from borehole breakout

Borehole breakout is a widely utilised phenomenon in horizontal stress orientation determination, and breakout geometrical parameters, such as width and depth, have been used to estimate both horizontal stress magnitudes. However, the accuracy of minimum horizontal stress estimation from borehole breakout remains relatively low in comparison to maximum horizontal stress estimation. This paper aims to compare and improve the minimum horizontal stress estimation via a number of machine learning (ML) regression techniques, including parametric and non-parametric models, which have rarely been explored. ML models were trained based on 79 laboratory data from published literature and validated against 23 field data. A systematic bias was observed in the prediction for the validation dataset whenever the horizontal stress value exceeded the maximum value in the training data. Nevertheless, the pattern was captured, and the removal of systematic bias showed that the artificial neural network is capable of predicting the minimum horizontal stress with an average error rate of 10.16% and a root mean square error of 3.87 MPa when compared to actual values obtained through conventional in-situ measurement techniques. This is a meaningful improvement considering the importance of in-situ stress knowledge for underground operations and the availability of borehole breakout data

Seismic performance assessment of interdependent lifeline networks using logical expansion of recursive decomposition algorithm

Lifeline networks are fundamental to our quality of life, to economic growth, and to the environment, but their components are intensely vulnerable to natural and man-made hazards. To minimize these hazard effects and to prevent functional damage propagation, it is essential to prepare effective pre-disaster hazard mitigation strategies based on rapid system reliability assessment. In this paper, the Logical Expansion of Recursive Decomposition Algorithm (LE-RDA), a non-simulation-based network reliability analysis method, is utilized to efficiently estimate the reliability of lifeline networks whose failure is defined by the complex failures of multiple node pairs. LE-RDA is demonstrated by recent lifeline network examples to estimate the seismic performance, functional degradation, and interaction effect, all of which are defined by a complex mixture of events. A new convergence-based efficient framework for LE-RDA calculation is introduced as well. LE-RDA can be applied to the node pair connectivity analysis of any general lifeline networks for decision support in hazard mitigation planning and disaster prevention management

Logical expansion of the recursive decomposition algorithm for infrastructure interdependency analysis

This article deals with the network reliability analysis of interdependent lifeline networks whose survival is defined by complex general system events with multiple source and demand node pairs. A new method, the Logical Expansion of the Recursive Decomposition Algorithm (LE-RDA), is introduced to handle complex events by successively applying a generalized decomposition process for two logical functions (intersection and union). The method is applied to an electric power grid and a water network in Memphis/Shelby County to estimate the seismic performance, functional degradation, interaction effect, and the contribution of network component groups to water flow distributions, all of which are defined by a complex mixture of correlated events with shared components. The significance of the interdependency effect is addressed and the network component groups’ priorities are identified for various earthquake magnitudes and interdependency levels

Post-disaster damage detection for pipeline networks by Matrix-based System Reliability analysis

After a natural or man-made hazard occurs, it is essential to detect damaged components in lifeline networks to enable rapid recovery of the utility service in the impacted areas. However, inspections of individual network components such as buried pipes are often impractical due to exceedingly large costs and time. This paper aims to develop new system reliability methods for identifying network components with higher conditional probabilities of damage given post-disaster network flow monitoring data. First, the matrix-based system reliability (MSR) method (Song & Kang 2009, Lee et al. 2010) is further developed for quantifying the uncertainties in the flow quantities of a lifeline network with a large number of component damage scenarios. In order to overcome the computational challenge in using the MSR method for large system problems, this paper introduces a new procedure to construct the vectors of the system state probabilities efficiently by selectively searching elements that correspond to the system states with higher likelihoods. Using the convenient matrix-based framework, one can obtain the probability distributions and statistical parameters of network flow quantities efficiently. Second, a Bayesian method is proposed to compute the conditional probability that a component is damaged given post-disaster network flow monitoring data. This method achieves an optimal matrix-based representation of the problem for efficient damage detection. The developed methods are demonstrated by a water pipeline network consisting of 15 pipelines. The uncertainty in the outflow at a location is quantified using the MSR method with a selective expansion scheme. The conditional probabilities of damage in 15 pipelines given post-disaster network flow observations are obtained by the Bayesian method for damage detection purpose. The results of the methods are compared to those by Monte Carlo simulations and by the MSR method without selective expansion scheme in order to demonstrate the accuracy and efficiency of the proposed methods

Network reliability analysis of complex systems using a non-simulation-based method

Civil infrastructures such as transportation, water supply, sewers, telecommunications, and electrical and gas networks often establish highly complex networks, due to their multiple source and distribution nodes, complex topology, and functional interdependence between network components. To understand the reliability of such complex network system under catastrophic events such as earthquakes and to provide proper emergency management actions under such situation, efficient and accurate reliability analysis methods are necessary. In this paper, a non-simulation-based network reliability analysis method is developed based on the Recursive Decomposition Algorithm (RDA) for risk assessment of generic networks whose operation is defined by the connections of multiple initial and terminal node pairs. The proposed method has two separate decomposition processes for two logical functions, intersection and union, and combinations of these processes are used for the decomposition of any general system event with multiple node pairs. The proposed method is illustrated through numerical network examples with a variety of system definitions, and is applied to a benchmark gas transmission pipe network in Memphis TN to estimate the seismic performance and functional degradation of the network under a set of earthquake scenarios

Development of statistical design models for concrete sandwich panels with continuous glass-fiber-reinforced polymer shear connectors

This article proposes a statistical framework for the development of design models for concrete sandwich panels with glass-fiber-reinforced polymer shear grids. The framework is developed by integrating the Bayesian parameter estimation method and the Eurocode-based capacity reduction factor calibration method. In the first part of the framework, probabilistic and deterministic shear flow prediction models are proposed based on 32 experimental data. It is seen that the contribution of glass-fiber-reinforced polymer grids is dominant, although parameters on the geometrical and material properties of insulation and concrete wythes also contribute. Different constant terms for bias correction of the proposed models are proposed according to the insulation type, and the prediction error of the developed model was reduced. In the second part of the framework, the capacity reduction factor for the proposed deterministic formulas is calculated for design purposes. Statistical calibrations for capacity factors are carried out to meet a target reliability level, and the value is estimated to be approximately 0.75 for all proposed models. Further data collection will improve the applicability of the proposed models and clarify quantification of the contribution of parameters

A rapid reliability estimation method for directed acyclic lifeline networks with statistically dependent components

Lifeline networks, such as transportation, water supply, sewers, telecommunications, and electrical and gas networks, are essential elements for the economic and societal functions of urban areas, but their components are highly susceptible to natural or man-made hazards. In this context, it is essential to provide effective pre-disaster hazard mitigation strategies and prompt post-disaster risk management efforts based on rapid system reliability assessment. This paper proposes a rapid reliability estimation method for node-pair connectivity analysis of lifeline networks especially when the network components are statistically correlated. Recursive procedures are proposed to compound all network nodes until they become a single super node representing the connectivity between the origin and destination nodes. The proposed method is applied to numerical network examples and benchmark interconnected power and water networks in Memphis, Shelby County. The connectivity analysis results show the proposed method's reasonable accuracy and remarkable efficiency as compared to the Monte Carlo simulations

Reliability-based flexural design models for concrete sandwich wall panels with continuous GFRP shear connectors

This paper proposes design models for insulated concrete sandwich wall panels (SWPs) with GFRP grids against a flexural failure. The design models are developed by considering both ultimate and serviceability limit states. First, mean-prediction models for evaluating ultimate moments and cracking moments of SWPs are proposed, and second, they are further developed into design models by adding capacity factors (or safety factors). The capacity factors are statistically determined using the method provided in Eurocode 1990: 2002 [1]; this method considers the random distribution of resistance defined by evaluating both modeling and parametric uncertainties. Two capacity factors are calibrated for an ultimate limit state function and a serviceability limit state function. For a more convenient design process, a unified capacity factor is determined by combining both factors into a function of a nominal ultimate moment. The unified factor can be applied to achieve the ultimate limit state requirement, and at the same time it automatically achieves the serviceability requirement