ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Site investigation data can play an important role in not only site characterization but also the improvement of engineering design. It is tempting to predict the potential benefit of a site investigation before it is actually conducted. In this study, a Bayesian framework to assess the effectiveness of a site investigation program is proposed. The expected reduced variance (ERV) is employed to measure the uncertainty reduction owing to site investigation data. The uncertain outcomes of all possible site investigation data are considered through their probabilistic distributions. Then a framework to calculate the ERV is established. Monte Carlo simulation is adopted and the statistical uncertainty can be analyzed through the bootstrap method. Two examples of a shallow foundation on sand and an undrained slope are illustrated in this paper. Based on the examples studied, the optimal sampling layout is also discussed. The proposed method is promising to be applied in optimization of the sampling layout for various geotechnical practices.

The active-learning Kriging (ALK)–based time-variant system reliability analysis has been widely concentrated. Unfortunately, the current time-variant system reliability methods are mostly focused on the series system, parallel system, or series-parallel system; thus, they cannot efficiently deal with the time-variant reliability problem of a complex system such as bridge system, network system, and so on. In view of this issue, the paper proposes an efficient time-variant reliability method for a complex system by introducing the structure function into the ALK-based time-variant reliability analysis. Firstly, similar to the ALK-based time-variant system reliability method, some extreme values corresponding to the initial input samples are optimized, and thus the initial extremum response surface is constructed based on the Kriging model. Then, considering the epistemic uncertainty of the Kriging predictions, the predicted response of system structure function under a particular input sample is viewed as a random variable, and its mean and variance are computed based on the minimal cut sets of a complex system. Lastly, considering the aleatory uncertainty between the different candidate samples, the point corresponding to the maximum prediction variance is selected, the most important component is decided by introducing the structure importance, and its extreme value is correspondingly optimized to update the initial extremum response surface. The stopping criterion is also provided in this paper and the effectiveness of the proposed method is illustrated by several examples.

Seismic vulnerability assessment of structures is a major research area and the output, i.e., fragility curves (FCs), within a probabilistic framework, devises effective seismic disaster mitigation. The present study proposes a novel methodology for obtaining the structural component-level FC through two new approaches with respect to the assessment of seismic demand and the computation of fragility. The present study evolves an existing nonlinear static method by a few steps to be able to assess the probabilistic demands and develops a numerical computation technique that exploits the actual profiles of the evaluated distributions of the structural capacity and demand data in a study to obtain the fragility magnitudes. The substitute methodology is implemented to derive the FCs of a class of integral abutment bridges while assessing the demands in a simple manner and avoiding the complexities of dynamic analysis and yet with reliable precision as well as averting the misjudgment of fragilities stemming from the lognormal or normal fit on the data, which are practically very rough fit or misfit to it, as imposed in the traditional fragility formulation.

Tree failures under extreme gusts could exacerbate storm damages to critical infrastructures, including the power delivery system, transportation network, or residential buildings. Nevertheless, the effects of tree damages have largely been excluded in existing community resilience assessment frameworks. To this end, presently, a model integrating tree failure consequences with community-level resilience evaluation is proposed for improved damage and recovery projections, considering the interconnected transportation, power delivery, and residential building systems. Tree failure probabilities are estimated based fragility analysis of the tree structural response under gusts. Proximity analysis is conducted to quantify the threat of trees striking critical infrastructures. The tree and wind risk assessments are integrated with fragility analysis of the power distribution system timber utility poles and the vulnerability analysis of low-rise wooden residential buildings. The methodology is carried out on a case study of a community in the northeastern US. The results indicate utility pole failures and roadway debris accumulations and blockages initiate when wind speeds reach around $40\mathrm{m}/\mathrm{s}$ ($90\mathrm{mi}/\mathrm{h}$). Although the inclusion of tree failures increase pole failure probability failure, tree-induced damages to residential buildings were found minimal due to the relatively lower heights of the sparser trees in the studied suburban region. Storm impacts are linked to the household level, including structural damages and their restoration times, increases in travel time to the main road, and probability of losing power. Vegetation management strategies are found to effectively reduce the building and powerline damages and debris buildup on roads.

During their life cycle, engineering systems typically suffer from deterioration due to aging and exposure to extreme events and harsh environmental conditions. As a result, regular or exceptional recovery strategies can be required to restore the system to a target structural performance level. This paper proposes a novel stochastic formulation for evaluating the sustainability of engineering systems as a function of their structural performance over a fixed time horizon. The life-cycle sustainability of the system is evaluated in terms of its cost and environmental impact, which includes the impact of the construction and possible recovery strategies. The formulation integrates state-dependent stochastic models that sequentially capture the effects of gradual and shock deterioration processes and the subsequent recovery processes on the system’s sustainability. Moreover, the formulation accounts for the relevant uncertainties, such as those in the external conditions (e.g., environmental exposure and potential hazards) and those in the environmental emissions associated with the materials and energy used throughout the system life cycle. The formulation uses the Renewal Theory-based Life-Cycle Analysis (RTLCA) formulation for a more computationally efficient evaluation of sustainability measures. As an illustration, the proposed analysis was used to evaluate the life-cycle sustainability of a typical reinforced concrete (RC) bridge.

Abstract

Value-at-risk (VAR) is an indispensable part of a disaster risk assessment of historical buildings. Indicator-based value framework can provide a consistent and comparative method to assess the VAR of historical buildings. However, the existing value frameworks and their indicators are diverse, greatly due to the multidimensional characteristic of the value of historical buildings. Besides, there are various value indicators that existing frameworks do not encompass. As such, a holistic framework to assess the VAR of historical buildings is still needed. Establishing a holistic VAR framework requires a detailed review of existing value frameworks and indicators to understand their similarities and differences. Therefore, a review of existing value frameworks and indicators is presented, covering frameworks and indicators applicable to three categories of built cultural heritage (historical buildings, monuments, and archaeological sites) reported in literature, as well as guidelines and reports. The review summarizes 53 value indicators and 12 frameworks, and after integration, reorganization, and optimization, a novel holistic VAR framework, with 6 first-level indicators and 42 second-level indicators, has been proposed for VAR assessment of historical buildings using the analytic hierarchy process based on expert elicitation. Further, the proposed VAR framework has been verified by performing a text mining analysis of the definitions/descriptions of the value indicators. Two historical buildings in China were analyzed to demonstrate the universality and comprehensiveness of the proposed VAR framework. The VAR assessment results can provide reference on safety measures allocation for risk reduction of historical buildings.

Practical Applications

Historical buildings are vulnerable to disasters such as fires. Thus, a disaster risk assessment is of practical significance for safety measure allocation and risk reduction of historical buildings. When performing a disaster risk assessment of historical buildings, a VAR assessment is an indispensable part. At present, the VAR of modern buildings is normally solely determined in monetary terms; however, historical buildings have multidimensional values that are difficult, even impossible, to quantify, and there is currently no practical approach to assess the VAR. An indicator-based VAR assessment framework can provide a consistent and comparative way to assess the VAR of historical buildings. By using the proposed VAR assessment framework, the multidimensional value indicators of historical buildings can be assessed relatively. This relative VAR assessment method allows a consistent and well-regulated assessment of the VARs of different historical buildings and horizontal comparison among the VARs. The VAR assessment results can be incorporated into disaster risk assessment to provide guidance on safety measures allocation for risk reduction of historical buildings.

Seismic probabilistic risk assessment requires explicit consideration and quantification of all sources of uncertainty. Although uncertainties in earthquakes and basic material properties, such as nominal concrete compressive strength, strain at maximum stress, and modulus of elasticity of concrete, have been considered in some studies, uncertainties in parameters used to model the nonlinear behavior of concrete are often not considered. Recent recommendations in design standards have incorporated performance-based limit states in the design and risk assessment of nuclear power plants. These recommendations have implicitly mandated that the uncertainty in the nonlinear behavior of concrete needs to be characterized and quantified in accordance with different performance limit states. This study focused on nonlinear model parameters in the concrete damage plasticity model (CDPM) and their characterization in terms of material strength parameters. Furthermore, a performance-based characterization and quantification of uncertainties in nonlinear model parameters was also conducted. Data from existing experimental studies were used to study the uncertainties in CDPM and were used as a basis for the proposed quantification.

Traditional seismic fragility analysis defines the structural damage measure (DM) as a single parameter based on either strength, displacement, damage, or energy to evaluate the postearthquake performance of the structure, thus it is difficult to fully catch the behavior characteristics of the structures, e.g., both the maximum deformation capacity and the repairability of the structure. In this paper, we developed a multiparameter-based DM $(m\mathrm{DM})$ index (here actually two parameters) for the probabilistic seismic analysis of reinforced concrete (RC) structures to capture a more comprehensive view of structural performance. Two parameters (i.e., the maximum interstory drift ratio ${\theta }_{\mathit{max}}$ and the maximum residual interstory drift ratio ${\theta }_{\mathrm{r},\max }$) are combined to represent the overall structural performance, and different combination rules are investigated. To verify the superiority of the proposed $m\mathrm{DM}$, the fragility analysis of three different structures is conducted, i.e., ordinary RC structure, aging RC structure, and new self-centering structure. The fragility curves derived from single DM and $m\mathrm{DM}$ are compared, and it is found that fragility curves with $m\mathrm{DM}$ can reflect both the maximum displacement and residual displacement features of the structure, thus $m\mathrm{DM}$ gives a more comprehensive and synthesis assessment of the postearthquake performance of the structure, which is beneficial for informed decision-making.

Precast concrete construction (PCC) is one of the most innovative and efficient methods in construction, but the separate processes involved in this method are one of its disadvantages. Therefore, the project owners considered quality management of PCC based on the Building information modelling (BIM) technique as a key factor to achieving desired tolerances, combining the separated process, and decreasing errors. This study was conducted to modify BIM processes based on dimensionless indices of PCC. The Fuzzy-decision-making trial and evaluation laboratory (DEMATEL) method was used to obtain the weight of variables. Genetic expression programming (GEP) was then used to estimate the modified BIM index ($I_{MBIM}$) in the PCC according to other important indicators as input data. The statistical results comparing the predicted and calculated values of $I_{MBIM}$ show that there is a low root-mean-square-error (RMSE) of 0.027, low MAE of 0.019, and high ${\mathit{R}}^2$ of 0.95 in the testing process. Thus, there is good agreement between the relationship of $I_{MBIM}$ and the calculated values that the proposed algorithm in this study can predict $I_{MBIM}$ with high accuracy. The sensitivity analysis illustrates that the index of system quality ($I_{sq}$) has the most significant effect on $I_{MBIM}$ in comparison with other indices.

Abstract

A bimodal distribution function with statistical parameters obtained using fuzzy regression is presented for predicting random truckload patterns that include all load ranges including overloads. Overload trucks often appear as a sizable portion of truck populations on highways. In applications when damage estimation of transportation facilities such as pavements and bridges is desired, theoretical models providing a reasonable representation of truckload populations including overloads will be useful. Load populations mostly exhibit inconsistent patterns, often with two or more distinct peaks. This is because of a combination of loaded and empty trucks as well as overloads in the population. As such, a mixed distribution model instead of a simple statistical distribution is used to portray a realistic representation of truckload populations. In this paper, using the bimodal model, theoretical distributions are developed to (1) represent the entire truckload population with weigh-in-motion (WIM) data, and (2) predict the population with limited data. If no truckload population is available, the bimodal distribution model can still be used, with certain assumptions, based on fuzzy regression using certain parameters pertaining to the traffic data such as the average daily truck traffic and an overall estimate of the percentage of overload data in the population. This paper represents new directions in modeling truckload patterns with capabilities to offer the continuity as well as the bimodal feature of the limited load data.

Practical Applications

Overload trucks, which refers to truck weights in excess of the 356 kN (80 kips), in the US often appear as a sizable portion of truck populations on highways. As truck populations grow, there is a potential for increase in the frequency of overloads as well. In applications when damage estimation of transportation facilities such as pavements and bridges are desired, theoretical models providing a reasonable representation of truck load populations including overloads will be useful. Load populations mostly exhibit an inconsistent pattern, often with two or more distinct peaks. The reason is attributed to the variety of loads in the population, and in most cases, it can be because of a combination of loaded and empty trucks as well as overloads appearing in the data. Mixed probability distribution models consisting of two functions (bimodal models) have been tried in the past and appear to offer a better solution for truck load populations. In this study, using the bimodal model, theoretical distributions are developed to (1) represent the entire truck load population with WIM data, and (2) predict the population with certain assumptions based on fuzzy regression using limited traffic pattern information available for the roadway.

In this paper, two data-drive site characterization methods, the sparse Bayesian learning (SBL) method and the Gaussian process regression (GPR) method, are benchmarked by a set of virtual ground examples and a real ground example of cone penetration test (CPT) data. The two methods both assume a zero-mean prior Gaussian random field model for the spatial trend, but the strategies of maintaining model simplicity are different. The SBL method produces a simple trend model by adopting sparse basis functions, whereas the GPR method produces a simple trend model by adopting a kernel function governed by few hyperparameters. The accuracy of the two methods in predicting the cone tip resistance ($q_t$) of CPT was quantified by the root-mean square prediction error (RMSE), whereas the accuracy in identifying soil layers was quantified by the identification rate (IR). It was found that the GPR method in general outperforms the SBL method. Further accuracy improvement for the GPR method can be obtained if a clustering analysis based on the Robertson’s soil behavior index ($I_c$) is conducted.

Abstract

The present study proposes vector-valued intensity measures (IMs), consisting of two IM parameters, for improved and efficient seismic risk assessment of liquid storage tanks base isolated with laminated rubber bearing (LRB) and lead rubber bearing or New Zealand base-isolation system (NZ system). To assess the seismic risk of the base-isolated liquid storage tanks, their seismic fragility was obtained using stripe analysis. The joint hazard of the selected IMs was obtained using vector-valued probabilistic seismic hazard analysis. It is shown that the vector-valued intensity measure consisting of peak ground acceleration (PGA) and spectral acceleration ($S_a$) at the isolation period ($T_b$) is efficient for predicting the peak base shear demand in the liquid storage tanks base isolated with LRB. Similarly, the vector-valued intensity measure consisting of PGA and peak ground velocity (PGV) is efficient for predicting the peak base shear in the liquid storage tanks base isolated with the NZ system. This study also presents parametric investigations on the effect of thicknesses of the tank wall ($t_w$) and the slenderness ratio ($S$) of the tank on the seismic risk of the base-isolated liquid storage tanks.

Practical Applications

The societal and industrial importance of liquid storage tanks is well understood, and it is necessary to ensure their safety during a strong earthquake for conducting post-earthquake response operations as well as to avoid any cascading hazardous event. To mitigate the seismic risk to a tank structure, the use of base-isolation technique, which minimizes the transmission of seismic force to the structure from the ground, is increasing worldwide. However, one can implement appropriate mitigation measures only when the associated seismic risk is quantified after understanding the prevailing seismic hazard and response of the structure under the expected level of earthquake. In the context of performance-based earthquake engineering, the seismic risk to a structure is assessed using key parameters such as IMs and engineering demand parameters (EDPs). Nevertheless, the choice of efficient IMs is always a challenge to estimate the probable seismic risk to a structure. This study proposes efficient vector-valued IMs for seismic risk assessment of base-isolated liquid storage tanks. The presented results can be used for efficient assessment of seismic risk for existing, and during the designing phase of newly proposed, base-isolated liquid storage tanks.

Explicit multihazard analysis of structures, especially bridges, has attracted significant attention from researchers and practitioners. Depending on the number of hazards considered and the length of the time interval, such analysis can be time-consuming and cumbersome for practical purposes. Therefore it would be beneficial to determine under what conditions such analysis is necessary. This paper explored the damage scenarios and performance criteria under which typical overpass bridges in the US require an explicit multihazard analysis. The paper concentrated on three common hazards (in addition to gravity loads): seismic, scour, and corrosion. Throughout the analysis, seismic hazard was assumed to be dominant. To explore this question, the paper used a probabilistic methodology to model time-dependent damage processes in RC. The dominant uncertainties in the modeling process are those associated with earthquake occurrence and intensity, corrosion initiation time and rate, and stream flow intensity. To quantify structural damage, a Park–Ang damage index is used. The damage index evolves with the age of the simulated structure and the combined effect of multiple hazards. It was found after examining several realistic bridge designs that the combined effect of multiple hazards throughout the life of the structure becomes important when evaluating low-probability damage events (such as collapse).

Existing efforts for analyzing the overflow fragility of inland levees have focused primarily on breach and incorporated simplified definitions of breach for that purpose. The intermediate events that are precursors of a breach have not been analyzed, and several key performance measures including the duration of overflow, discharge rate, and time to the intermediate and breach events, which can inform decisions on evacuation and recovery planning, have not been investigated. This study, for the first time, provides a set of two-dimensional fragility models with an initial water level before surge and peak surge elevation as intensity measures. Fragility models are developed for the class of homogenous levees with noncohesive sediments. Breach formation is modeled through the Dam/Levee Breach model, and failure probabilities are derived using Monte Carlo Simulation (MCS). Unlike existing fragility models, the new class of fragility models accurately represents key performance failure events in levees including initiation of a breach, local geotechnical instabilities, and breach development. These models can be used in risk analysis frameworks to probabilistically account for local geotechnical failures and breach scenarios. Furthermore, probabilistic models for the time of peak discharge rate, breach peak outflow, and volume of water outflow are developed based on stochastic simulations of the flood performance of levees. A power model is investigated for representing breach peak outflow and is compared with previous deterministic models. The proposed model for breach peak outflow can be integrated with hydraulic inundation modeling to probabilistically estimate the inundated area downstream of levees.

This paper presents a framework for time-variant system reliability analysis of concrete sewer pipes considering multiple modes of failure. Sewer pipelines are among critical infrastructure systems, designed to collect and transport wastewater away from communities. Such pipes are typically subjected to corrosion and subsequent damage because of continuous contact with corrosive materials. Corrosion is here modeled in terms of the reduction in the pipe wall thickness. In the proposed framework, prevailing uncertainties in the characteristics of wastewater fluid, geometric properties of fluid cross section, and corrosion-related parameters of concrete are quantified as random variables. Next, various modes of failure under bending, shear, excessive cracking, and cover loss are formulated as multiple limit-state functions. This leads to a general system reliability problem defined as a series system of parallel subsystems, i.e., cut sets. Further, by introducing the corrosion model into the limit-state functions, a time-variant system reliability problem is established in which the integration domain is conditioned to the corrosion level at each point in time. By integrating over the conditional probabilities through a rejection sampling method, the failure probability is computed given the corrosion level over the desired time span. The results show that the failure probability increases sharply with time and corrosion level. For instance, the failure probability exceeds 80% for pipes over 50 years and at corrosion levels of over 40%. Moreover, the sensitivity analysis shows that the acid-consumption capacity of the pipe material, the pH-related factor, and the proportion of ${\mathrm{S}}^{2-}$ dissolved highly influence the failure probability. The maximum effects of these parameters occur within 0%–10% corrosion. The results of this study can be used to evaluate the failure probability of sewer pipes given their age and current corrosion level.

The safe operation of natural gas long-distance pipeline stations is the critical link for its sustainable transmission. Therefore, station operation risk assessment and space-time expression in high consequence areas have become a critical problem that must be urgently addressed. The conventional way to assess the probability of gas pipeline failure cannot meet the requirements of the natural gas operating companies for the safety assessment of natural gas stations. Taking the example of the China–Myanmar natural gas pipeline branch station, based on the fuzzy fault tree model (FFT), a comprehensive pipeline safety evaluation method (CPSE-FFT) has been introduced, which evaluates the risk factor of pipeline operation in the station area and the social security risk brought by it. In this case study, the method is applied to a branch station of the China–Myanmar long-distance pipeline. In the results, the probability of pipeline accidents in the area of the station is ${5.79\times 10}^{-4}$ times/year, and six buildings with higher risk levels among the 37 buildings are also exposed on the risk distribution map. The overall social risk level is also expressed. CPSE-FFT is a practical, reasonable, and accurate assessment method that can quantitatively reflect the risks posed by the safe operation of natural gas stations. This method is suitable for most natural gas station risk assessments. The results of the risk assessment provide a reference for managers and decision-makers to reduce the risk of natural gas stations.