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

In the last years, it is evident that cycling is becoming an alternative transportation mode to driving and has gained more popularity among all age groups, particularly in metropolitan cities, due to COVID-19. Although cycling is beneficial to individuals and urban cities (i.e., reduction of traffic congestion and promotion of a healthy lifestyle), it could also expose cyclists to risky situations, resulting in serious consequences. Therefore, this research aims at conducting a comprehensive analysis of the key contributory factors by using data derived from cycling accident and literature reports. More specifically, the accident data are first used to prioritize contributory factors contributing to a high level of cycling risk, and then the results guide the development of the literature review. The literature review analysis emphasized the characteristics, relationships, and control measures against different selected contributory factors identified from cycling accident reports. The in-depth analysis aids to figure out and better understand what the characteristics and relationships of these factors are, how they affect the safety of cyclists individually and jointly, and what to do to control their negative effects. The findings will not only provide practical insights for transport authorities to control contributory factors influencing cycling safety, but also engage more research for the improvement of cycling popularity, prevention of cycling risks, and enhancement of cycling safety in future.

In most engineering problems, because of a lack of complete information about the structure of the performance function, selection of the optimal approach for efficient reliability analysis is in essence a decision under uncertainty. This issue is investigated in this paper and, by representing reliability methods as search algorithms, no-free-lunch theorems (NFL) of search and optimization are used to propose similar NFL for reliability analysis. Using NFL, this study aims to answer some basic questions about the existence and selection of optimal reliability methods for black- and gray-box problems and proposes a mathematical framework for the application of detection theory in structural reliability. Black- and gray-box problems in this context refer to structural reliability problems with, respectively, no and partial information on the topology of the limit state function. Then, by employing Dempster-Shafer theory of evidence as a generalized Bayesian decision-making theorem, a practical experts-in-the-loop approach for the selection of an optimal reliability method in uncertain conditions is proposed. To meet this aim, providing a step-by-step solution of some selection problem examples, it is shown that knowledge of several experts can be fused into one all-encompassing knowledge representation to reduce the probability of making an error in the selection of an optimal approach for efficient reliability analysis.

Properties of geomaterials often exhibit stratification and anisotropy due to various influencing factors such as weathering and sedimentation. However, the measurement of anisotropy is a difficult task since anisotropy not only depends on the direction but also varies with scale. In the current study, hydraulic conductivity is considered a typical geomaterial property and simulated by random field theory. A novel method based on two-dimensional and three-dimensional analytical expressions is proposed to estimate the apparent hydraulic conductivity ($k$) in different directions and determine the corresponding anisotropic ratios. A series of simulation tests on specimens with various dimensions from one strong anisotropy site are also performed via the finite element method. The analytical solutions of the proposed method are verified by numerical results. Results indicate that the anisotropic ratio shows a substantial sensitivity to the sample scale. A decrease in sample scale can result in the reduction of the anisotropic ratio; as a result, $k$ gradually approaches to a point level’s value, and the effect of anisotropy decreases. This work not only sheds light on the gap between the laboratory results and the field’s inherent properties but also provides guidelines on upscaling small-scale (e.g., laboratory scale) results to field-scale applications.

Piers are usually the most vulnerable components in a bridge structure and generally undergo excessive deformation, which will lead to damage and even whole structural collapse. This paper investigates the probabilistic seismic deformation capacities of reinforced concrete piers under different limit states for two engineering demand parameters, i.e., the drift ratio and displacement ductility. Based on sample data from the UW-PEER database, a penalized generalized additive model is used for predictor variable selections and to determine whether the mechanism of each predictor on the seismic capacity is linear or nonlinear. The influence of a predictor that illustrated a nonlinear pattern is modeled by a Gaussian process, and Bayesian semiparametric regression is conducted in the R environment to obtain posteriori estimations of the capacity measures. The results indicate that the ratios of the model predictions to the experimental observations are all around 1.0, which proves the unbiasedness of the models. Compared with previous seismic capacity models, the prediction of seismic capacity measures shows higher accuracy, lower dispersion, and better portrayal of uncertainties. The proposed model based on Bayesian semiparametric regression provides a performance improvement in the seismic capacity evaluation of the bridge structures, which can be used for the subsequent bridge seismic fragility and risk assessment.

The interval discrete Fourier transform (DFT) algorithm can propagate signals carrying interval uncertainty. By addressing the repeated variables problem, the interval DFT algorithm provides exact theoretical bounds on the Fourier amplitude and estimates of the power spectral density (PSD) function while running in polynomial time. Thus, the algorithm can be used to assess the worst-case scenario in terms of maximum or minimum power, and provide insights into the amplitude spectrum bands of the transformed signal. To propagate signals with missing data, an upper and lower value for the missing data present in the signal must be assumed, such that the uncertainty in the spectrum bands can also be interpreted as an indicator of the quality of the reconstructed signal. For missing data reconstruction, there are a number of techniques available that can be used to obtain reliable bounds in the time domain, such as Kriging regressors and interval predictor models. Alternative heuristic strategies based on variable—as opposed to fixed—bounds can also be explored. This work aims to investigate the sensitivity of the algorithm against interval uncertainty in the time signal. The investigation is conducted in different case studies using signals of different lengths generated from the Kanai-Tajimi PSD function, representing earthquakes, and the Joint North Sea Wave Observation Project (JONSWAP) PSD function, representing sea waves as a narrowband PSD model.

Hurricanes are one of the costliest and deadliest weather and climate disasters that impact the United States, largely affecting the residential sector. Residential structures are essential for the health and safety of the community during a hurricane event. However, homeowners typically have to rely on general information and instructions to understand their level of risks. The knowledge of structural vulnerability to hurricanes at the individual residential building level is often limited, which hinders personalized disaster preparation and mitigation decision-making. To this end, a vulnerability assessment method is proposed for individual homeowners to create customized finite-element models that quantify wind damage of low-rise wood-frame residential structures under various scenarios using ANSYS 2020 R1 parametric design language. A case study was conducted to test the proposed methodology and investigate prevailing failure modes under different structural characteristics and wind scenarios. Specifically, the impacts of roof framing features and previous exposure to environmental conditions on the potential damage of residential buildings under hurricane wind loads were studied. Analysis results, including the percentage of failed sheathing, nailed connections and framing members, are used to inform personalized mitigation recommendations. The proposed customized vulnerability assessment may increase homeowners’ situational awareness of disasters and promote rational retrofitting decisions.

During long-term bridge monitoring, model updating is necessary because it provides the basis for accurate condition assessment and damage detection. In this study, an adaptive sampling-based Bayesian model updating method for bridges is developed considering a substructure approach. First, the substructuring method is considered to solve eigenvalue problems. By reducing the size of the characteristic equations, the substructure approach overcomes poor algorithm performance, nonconvergence of results, and inefficient model updating caused by the large number of updated parameters when updating a large-scale system. Then Bayesian model updating is applied to quantify the uncertainty existing in bridge model updating and to obtain the posterior probability density function (PDF) of updating parameters that can be further used in different fields of engineering. By introducing the affine-invariant ensemble sampler (AIES) to replace the traditional Metropolis-Hastings (MH) sampler, an adaptive transitional Markov chain Monte Carlo algorithm is proposed to obtain the posterior probability of parameters with high efficiency. Application to a bridge structure demonstrates that the proposed method is efficient and useful in engineering problems.

Stability analysis of rock structures with limited data of uncertain rock properties possessing mutual dependencies currently is an unexplored domain. This study used a Bayesian multimodel inference-based probabilistic methodology to analyze the stability of rock structures with limited data of copula-dependent uncertain inputs. The methodology propagates the epistemic uncertainties in the model types and parameters of the marginals and copula of inputs emanating due to limited data to the outputs of interest. A stratified Bayesian multimodel inference is employed initially to characterize the uncertainties in the input marginals by preparing the model sets using a reweighting approach. These model sets are employed in the second stage to characterize the uncertainties in their copula dependence. These input uncertainties, represented via an ensemble of multivariate candidate densities, are propagated to estimate the uncertainties in the outputs. The proposed methodology was demonstrated for a tunnel under consideration in the Karnataka state of India. The methodology was found to be effective because it propagates the mutual dependencies and epistemic uncertainties of the inputs to the outputs (probability of failures) and estimates their confidence intervals instead of their fixed-point estimates. These interval estimates include the point estimates of the outputs along with their upper and lower bounds within which the point estimates may vary, making users more informed about the response of structures. Additional analyses showed that the epistemic uncertainties of inputs and their dependencies significantly affect the outputs with epistemic uncertainties playing the dominating role.

Probabilistic analysis and time variation model of concrete compressive strength in existing buildings were conducted based on relevant site testing data. The data were collected from rebound hammer (RH) method and core test method. There were 73,840 of RH test data and 4,149 of core test data. First, the data were normalized through dividing by the cube strength, to eliminate the concrete grade effect. The probability distribution analysis showed that the normal distribution is more suitable than the log-normal distribution. Moreover, it was found that the mean value of the core test data was higher than that of the RH data. Further, regression analysis on the data was carried out, and two quadratic functions were adopted to capture the time variation models of the compressive strength based on the RH data, and the core test data, respectively. Furthermore, some more data were obtained by the Genetic algorithm-back propagation (GA-BP) neural network method, and the time variation models were accordingly updated including the new data. Finally, the relationship between the RH model and core test model was discussed, and two approximately linear curves existed from 0 to 30 years and 31 to 60 years. Hopefully, this probabilistic analysis and the time variation model can be helpful in the assessment of the bearing capacity and evaluation of structure reliability in concrete structures.

Base-isolated structures face a risk of damage under the action of earthquakes, and base isolation devices may be susceptible to damage during severe earthquakes. This study proposed an approach based on the response surface method to assess the base-isolated structures’ seismic fragility and investigated the reliability and effectiveness of the base-isolation technique. Three performance levels and corresponding performance criteria for general base-isolated reinforced concrete structures were specified. The proposed seismic fragility assessment approach fully considered the uncertainty of the base-isolated structures and ground motions. Uncertainty parameters of the superstructure and base isolators were characterized, and parametric sensitivity analysis of the base-isolated structure was carried out. Moreover, the seismic fragility evaluation method of base-isolation systems was studied, and a simplified evaluation equation was introduced. The proposed response surface-based method’s accuracy and efficiency were also compared with the Monte Carlo method. An actual project was taken as an example to illustrate the proposed approach. The failure probabilities of the base-isolated structure under various seismic intensity levels were calculated, and the seismic fragility curves were plotted. It indicated that the base-isolated structure could achieve the performance objectives of maintaining operation under minor earthquakes, guaranteeing life safety under moderate earthquakes, and preventing collapse under severe earthquakes. However, the seismic isolation bearings could still have large displacements under severe earthquakes. Furthermore, the seismic fragility analysis of the base-isolated and nonisolated structures was compared. It showed that the base isolation technique significantly reduced the damage probability of the superstructure under various intensity levels and effectively prevented the superstructure from entering a serious damage state. The proposed seismic fragility analysis approach and a large amount of computational data could provide a significant engineering application value for similar reinforced concrete structures and a reference value for the design of base-isolated structures.

The purpose of this paper is to capture the direct and indirect effects of COVID-19 emerging risks on construction projects’ success in developing countries from the contractors’ perspective. To achieve this, we collected data from Iraqi construction industry and conducted the following multistage research methodology: (1) preliminary investigation with four construction contractors to identify the success indicators of construction projects; (2) focus group session with 11 experts to identify the COVID-19 emerging risks for the construction industry; (3) semistructured interviews with seven construction experts to develop several hypotheses on the effect of COVID-19 emerging risks on project success; (4) survey data collection from 99 construction contractors; and (5) development of a structural equation model (SEM) to analyze the effects of COVID-19 emerging risks on project success. The results of the SEM analysis show that financial market (FM)–related risks, supply chain operations (SCO)–related risks, health and safety of construction workforce (HSCW)–related risks, organizational implications (OI)–related risks, and contractual implications (CI)–related risks have a significant impact on construction projects success. CI-related risks have the strongest total effects on project success, followed by OI-related risks, FM-related risks, HSCW-related risks, and SCO-related risks, respectively. The results also emphasis the significant mediation role of CI between COVID-19 emerging risks and project success. This study contributes to the body of knowledge and contracting companies by helping researchers and construction contractors to better understand how the key risk factors emerging from extreme conditions like the COVID-19 pandemic affect construction projects’ success and may serve as a guideline for developing effective response strategies.