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

Vertical formwork in construction accounts for many fatal falls and injuries, among all other activities. Experts believe that an effective risk assessment could enhance safety performance by preventing accidents and injuries at the site. Despite the fact that little research has focused on quantifying the overall safety risks of formwork operations, vertical formwork activities that pose the risk of falls have been uncharted. This study aimed to assess the activity-based fall risks associated with vertical formwork. First, the formwork activities that pose the risk of falls were identified through observations, and industry professionals validated the descriptions of the resulting activities. Next, the Delphi method was employed with a panel of 13 construction experts. The identified activities were presented to experts to quantify each formwork activity based on probability and severity scales developed in prior research. Results indicated that the formwork panel stripping and erection have the highest risk of falls during vertical formwork operations. The findings of this study could help contracting companies target high-risk activities associated with vertical formwork for fall prevention safety programs.

The long-term performance of reinforced concrete (RC) structures is influenced by several environmental factors along with various loading conditions. Structures such as RC bridges exposed to the marine environment, essentially in chloride-contaminated environments, are highly susceptible to chloride-induced corrosion. In recent years, with the exponential increase of traffic intensity on highways, RC bridges have essentially experienced fatigue damages. High frequencies of cyclic loading due to traffic in combination with corrosion may expedite the deterioration mechanism and reduce the structure’s service life significantly. This paper presents a novel framework to calculate the service life of a structure subjected to chloride-induced corrosion and fatigue due to traffic loads. The presented model takes care of the following phenomena: corrosion initiation due to chloride ingress, reduction of steel reinforcement area due to corrosion of reinforcement, concrete cracking due to the accumulation of corrosion products and nucleation and the propagation of cracks in reinforcing bars due to fatigue loads. The framework comprises the calculation of three significant deterioration stages: corrosion initiation time, pit-to-crack transition time, and time until structural failure. The novelty of the proposed approach resides in its ability to model pitting corrosion as a realistic corrosion deterioration mechanism rather than the traditional uniform corrosion process. This model takes into account the nonlinearity of the corrosion rate after the initial cracking of concrete to calculate the corrosion-fatigue lifetime. Comparisons are drawn for general and pitting corrosion. The application of the model is done to evaluate the service life of an RC bridge girder subjected to cyclic loading of varying frequencies subjected to three different chloride-contaminated environments. The results indicate that the total lifetime of a structure is strongly affected by environmental aggressiveness. Under lower frequencies of traffic loading, an increase in environmental aggressiveness can result in reductions of lifetime ranging between 30% and 35%, but under the higher frequency of cyclic loading, the total reduction in a lifetime could go up to 70%. Parametric investigations of the structural parameters revealed an increase in the lifetime performance of the structure under a lesser water binder ratio and higher cover thickness.

For the nonlinear analyses of concrete structures, the complete compressive stress-strain curve of concrete is the key basis. Spatial variability is one of the significant characteristics of the mechanical properties of concrete. It may affect the possibly partially damaged area and failure mode of concrete components. However, there are few investigations on the spatial variability of the complete compressive stress-strain curve of concrete. To this end, a plain concrete slab of $2.8\times 4.8\times 0.2\mathrm{m}$ was cast. In order to study the spatial variability of the rebound number, an intensive rebound hammer test was performed on this slab with spacing of 40 mm. Then a core test was carried out with partitioned uniform sampling. In order to obtain more information about the spatial variability of random field, the positions of cores were determined by three two-dimensional circular number theoretical nets. The test of the complete compressive stress-strain curve was performed on the obtained cores and a four-variate random field of the compressive constitutive parameters of concrete was obtained. Then the geostatistical method was adopted to study the spatial variability of the obtained random fields. The test results show that there is strong spatial correlation in the four compressive constitutive parameters of concrete. In addition, the size, shape, and failure mode of the concrete block have great influence on its complete compressive stress-strain curves. For the random field of the rebound number, there is obvious spatial correlation, but the spatial variability is dominated by randomness.

In the present study, a one-dimensional experimental velocity profile measured in seepage channels is evaluated by Tsallis entropy theory considering time-averaged velocity as a random variable. The velocity data was observed by conducting the experiments in the laboratory flume over the flat sand bed for no seepage and seepage discharge. The proposed velocity profile based on the Tsallis entropy model is tested with laboratory observations. Various efficiency criteria, root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) coefficient, are used to verify the validity and accuracy of the proposed model. The Tsallis entropy-based 1D velocity distributions produced by various shape parameters agreed satisfactorily with experimental data and compared well. According to observations, the shape parameter $\mathrm{a}=0.2$ leads to the best fit of the cumulative distribution function and velocity for the centre line of the channel. It is shown that the Tsallis entropy can be used to make accurate velocity predictions in active seepage channels.

Existing tunnels are inevitably disturbed by adjacent engineering projects, with ground surface surcharge accounting for the largest proportion. Meanwhile, the influence of spatial variability of soil parameters on tunnel deformation cannot be ignored, especially for the cross-correlation when a multivariate random field is modeled. Unfortunately, studies about the coupled effect of multivariate cross-correlated random fields and surcharge on longitudinal deformation of tunnels in three-dimensional (3D) space are quite scarce. Thus, this study firstly investigated the coupled impact of the multivariate cross-correlation of soil parameters and ground surface surcharge on the maximum longitudinal deformation of an existing tunnel in 3D space by using the random FEM (RFEM) combining the parallel technology integrated within ABAQUS. The Young’s modulus, Poisson’s ratio, and friction angle were discretized and highlighted by isotropic cross-correlated random fields. The results showed that the surcharge has quite a significant impact on the deformation of an operating tunnel especially in spatially variable soil. The calculation result obtained by random variable theory (RVT) was smaller than that of random field theory (RFT) because the former ignored the structural characteristics of soil parameters. Conventional single-parameter random fields (SRF) and multivariate independent random fields (IRF) may overestimate the tunnel deformation compared with multivariate cross-correlation random fields (CRF). Further parameter studies found that a larger cross-correlation coefficient between soil parameters will result in a smaller tunnel deformation in the CRF model.

The heterogeneous character of concrete results in spatial variation of its material properties. Random field models are often used to account for this effect. Due to the large scatter on the correlation lengths suggested in literature, tests could be performed to determine the most appropriate correlation model and corresponding correlation length. Subsequently, different techniques can be employed to fit an analytical model to the experimental semivariogram, resulting in the most appropriate correlation model and corresponding correlation length. However, the resulting correlation lengths can largely depend on the experimental design. In this work, the effect of several parameters and choices to be made by an engineer in deriving the correlation model based on experimental data from destructive tests has been investigated. It was found that the curve-fitting method generally leads to better estimates of the scale of fluctuation compared to the maximum likelihood method. Moreover, there is a clear benefit of applying a bootstrapping procedure to the experimental data to estimate the covariance matrix adopted in the fitting procedures as well as to estimate the uncertainty related to the estimated parameters. When a measurement error is suspected to be present and cannot be neglected, the nugget should be estimated together with the variance and the scale of fluctuation. Furthermore, the Gaussian correlation model was found to be the most robust choice, even if the actual correlation model is not Gaussian. The latter was confirmed for actual experimental data on the material properties of concrete, in which a linear model was found to fit the data best but the Gaussian model provided comparable results.

This work first analyzes the time-dependent property of average concrete permeability by the relative decline ratio and proposes an analysis method for the randomness of time-dependent variables. Then, based on exposure test results of 10 different concretes in a natural tidal environment, the average time-dependent processes, statistical parameters, and probability distributions of the instantaneous chloride diffusion coefficient ($D_{\mathrm{ins}}$), water permeability coefficient ($K_w$), and intrinsic gas permeability coefficient ($K_g$) were all studied. Similarities among the randomness of three concrete permeabilities were analyzed by the Kullback-Leibler (K-L) divergence. Results indicate that decreasing processes of three permeability coefficients with increasing exposure time show similarities. The decreases are all faster at the early exposure stage and gradually stabilize at the later stage. Specifically, the decline rate of average $K_w$ is the greatest, followed by that of $K_g$, and the decline rate of $D_{\mathrm{ins}}$ is the smallest. When ignoring the influence of material components, generally, both $D_{\mathrm{ins}}$ and $K_w$ follow a normal distribution, and $K_g$ follows a lognormal distribution. The randomness of $K_w$ is the greatest, and that of $D_{\mathrm{ins}}$ is the smallest. The randomness of both $K_w$ and chloride diffusion coefficient of concrete without admixture are both greater than those of concrete with admixture. The randomness of intrinsic gas permeability of concrete with admixture is greater than that of concrete without admixture. Results show good similarities among the three permeability coefficients of concrete, and $D_{\mathrm{ins}}$ is more similar with $K_w$ in random characteristics. Similarities among the randomness of three permeabilities in concrete without admixture are more obvious than those of concrete with admixture.

In maritime studies, seafarer fatigue has been proven to be an important risk factor that affects human performance and leads to human errors. However, in the development of human reliability analysis (HRA) for the maritime industry, few studies have taken into account fatigue as a human performance-shaping factor (PSF) and systematically evaluate seafarer fatigue using HRA methods. In this paper, 52 ship masters and officers from six shipping companies are invited to select 15 leading influential factors as sub-PSFs from the 43 fatigue relevant factors within five categories (general PSFs) published by the International Maritime Organization (IMO). Next, the potential risk levels of the 15 sub-PSFs are evaluated and prioritized through a new failure modes and effects analysis (FMEA) method combining a belief-based Bayesian network (BBN) and an evidence reasoning (ER) algorithm. Finally, an axiomatic analysis is used to verify the robustness and applicability of the new FMEA method within the context of seafarer fatigue evaluation. It is found that the top five sub-PSFs include sleep quality, emotion, non-day shift, shift length, and noise, and it is suggested that these sub-PSFs should be integrated into maritime HRA analysis. The results of this study can not only promote navigation safety but also facilitate the development of fatigue risk management.

The hinge joint is an important and fragile component of assembled hollow-slab bridges. Therefore, it is necessary to regularly identify hinge joint damage for guaranteeing the safety of assembled hollow-slab bridges. However, conventional hinge joint damage identification methods are time consuming and expensive. Therefore, this study proposes a data-driven hinge joint damage identification method under moving vehicle excitation to quantitatively identify hinge joint damage conveniently. First, we established a refined finite-element model of a hollow-slab bridge with damaged hinge joints and analyze the dynamic response of the bridge under vehicle loads. The Pearson correlation coefficient between the acceleration time history of the adjacent slabs was proposed as the damage index. Further, an ensemble learning algorithm called gradient boosted regression trees (GBRT) was employed to develop a model for identifying hinged joint damage. Finally, the performance of the model was thoroughly compared with commonly utilized machine-learning algorithms and the auto-encoder-based method. The results show that the proposed model exhibits the highest accuracy. Under different signal-to-noise ratio conditions, the model’s coefficient of determination ($R^2$) is always above 0.85, the mean absolute error (MAE) is below 4.40 cm, and the root mean squared error (RMSE) is below 7.91 cm. This confirms the feasibility of the model for quantitative and convenient identification of the damage height of hinged joints.

Recently, bridge weigh-in-motion (B-WIM) technology has played an essential part in bridge management and maintenance. To systematically investigate the performance of B-WIM methods considering the spatial variation of material characteristics, a great number of parametric analyses were conducted. First, the random fields are represented through the stochastic harmonic function (SHF) representation method to consider the spatial variability of the mass density and elastic modulus. Then, various scenarios, including sample size, vehicle types, correlation length (CL), and coefficient of variation (COV), are designed in parametric analyses. Finally, on the basis of a vehicle-bridge coupling (VBC) system, numerical simulations are conducted to explore the performance of typical B-WIM methods such as the Moses algorithm and strain area method. The results showed that the performances of two typical B-WIM methods are affected by the spatial variability of material properties, but the maximal discrepancy of recognition accuracy does not exceed 5%, which is acceptable in actual engineering.

Interval treatments of uncertainty are plagued by dependency issues in their implementation. Ignoring the dependency among interval values can lead to a significant overestimation of the bounds on calculated uncertain responses. However, the clever implementation of operation order and other dependency effects in interval computations can result in models for the uncertainty that provide sharp bounds for multidomain uncertainty dependency in physical systems. In this paper, we explore an interval analysis of structural systems—specifically, systems where one or more groups of structural elements are subject to a global uncertainty in a value of a property and, concomitantly, each element has an additional intragroup uncertainty. Consider a typical scenario where the steel for structural elements is from a single batch (has unknown but identical modulus and strength) while each element has uncertainty in its stiffness from both modulus and geometric variations. To address the relationship between overall group uncertainty and local uncertainty, a product of interval variables is employed. The dependency for element level uncertainty (i.e., area) is addressed by the element-by-element methods of Muhanna and Mullen, while the group dependency is addressed by a new group-by-group method where a single interval value multiplies the entire substructure matrix. Example calculations are presented to compare the results of the new multilevel fixed-point method with conventional interval finite element calculations and with all combinations of endpoints, sensitivity-based methods, and two optimization methods. In general, only the fixed-point methods provide a guaranteed enclosure. Both solution accuracy, as well as computational requirements of the methods, will be assessed.

In this study, the reliability analysis of magnetorheological (MR) dampers in semiactive control of structural responses under uncertainty in seismic excitation is investigated. Uncertainties in the input earthquake exerted to the base of the structure are evaluated in two cases by considering the stationary and nonstationary natures of the earthquake amplitude and frequency content. A clipped optimal control algorithm with a linear quadratic regulator control strategy is used for the semiactive control system. In this regard, an unconstrained optimization problem is proposed to minimize the maximum structural responses in order to optimize the control system’s performance of the damper. In reliability analysis, two limit state functions (LSFs) are defined based on structural safety and human comfort criteria. By using the standard Monte Carlo simulation method, the structure’s probability of failure for each LSF is calculated, and the system’s reliability in controlling the structure’s seismic responses is determined. The results show that the proposed optimization method results in better performance of dampers in reducing the structural seismic responses. Furthermore, by deriving the reliability graphs, the system’s reliability for any desired values of controlled responses can be obtained, which helps to design a reliable and safe smart structure.

The available thunderstorm wind records with subsecond sampling intervals is scarce for a given site; stochastic models that can be used to sample multivariate nonstationary non-Gaussian thunderstorm winds at multiple points or tricomponent thunderstorm winds at a point are lacking. We propose the use of the dual-tree complex wavelet packet transform (DT-$\mathcal{C}\mathrm{WPT}$) within the framework of the iterative power and amplitude correction (IPAC) algorithm to generate multivariate nonstationary non-Gaussian thunderstorm wind records. This is a data-driven or seed-record-based approach, and the use of the IPAC algorithm ensures the matching of the marginal cumulative probability distribution function. The DT-$\mathcal{C}\mathrm{WPT}$ is used to gain computational efficiency because it is a redundant transform with a low redundancy factor, and it provides phase information. The statistics of the time-frequency power spectral density of the sampled records and the seed record were compared to show the adequacy and effectiveness of the proposed approach. The results also show that the use of the DT-$\mathcal{C}\mathrm{WPT}$ instead of the (discretized) continuous wavelet transform and S-transform significantly reduces the computational time.

Residential buildings in coastal areas often suffer from dramatic cyclone-induced damages. Resilience analysis is a powerful tool to quantify the object’s (e.g., a building portfolio’s) ability to withstand disruptive events. The key aspects include (1) the modeling of the recovery process in the aftermath of a cyclone event, which is intrinsically complex and is conditional on multiple factors (e.g., the available resources, the damage state immediately after a hazardous event), and (2) a reasonable index for resilience quantification that is capable of reflecting the asset owner/decision-maker’s requirement for the recovery process. In this paper, a new resilience measure is proposed that incorporates a requirement of anticipated recovery time (i.e., whether the recovery can be completed before the anticipated time) for the object. A novel framework for resilience quantification of residential building portfolios is also developed, which involves an optimal resource allocation strategy to maximize the resilience of building portfolios. The applicability of the proposed framework is illustrated through examining the resilience of a virtual community subjected to cyclone winds. The impacts of resourcefulness, wind field uncertainty, and correlation between building capacities on the building portfolio resilience are investigated.

The lateral load–carrying capacity of highway bridges is adversely affected by corrosion deterioration of reinforced concrete (RC) bridge piers during earthquakes. Recent studies reveal that increased global warming due to climate change causes changes in temperature and humidity that further exacerbate the corrosion deterioration of RC bridge piers. This climate change–induced corrosion deterioration may further impair the performance of bridges when located in regions of moderate to high seismic zones. Consequently, this study provides a probabilistic framework for considering the joint impact of corrosion deterioration, earthquakes, and climate change on the lifetime vulnerability of highway bridges. The framework is demonstrated using a case study of a nonseismically designed highway bridge located close to marine sources within seismic active region of Gujarat, India. An improved corrosion deterioration model is utilized that incorporates climate change and concrete cracking effects for estimating time-dependent corrosion of the RC bridge pier. A robust, experimentally validated finite-element model is developed that can capture the varied failure modes of the bridge pier. A set of recorded ground motions that represents regional seismicity is selected to perform nonlinear time-history analyses. Time-varying damage state thresholds and probabilistic seismic demand models are used to develop seismic fragility curves for the bridge while also accounting for the climate change effects. Results reveal that consideration of climate change effects significantly increases the seismic fragility of the deteriorated bridge up to 53%. Lastly, the developed methodology is demonstrated for seismically designed bridge to evaluate the impact of modern codes for ductile detailing and durability provisions on bridge vulnerability incorporating climate change effects.

Compared with ocean transportation, inland waterway transportation (IWT) has issues such as a low configuration standard of navigation equipment, insufficient crew knowledge and skills, and the relatively more complex hydrographic environment of inland waterways. To recognize and quantify the risk of IWT, this study proposes a novel risk assessment method. Firstly, text mining by Python is applied to recognize the risk influential factors (RIFs) from marine accident investigation reports (MAIRs), and a risk evaluation hierarchy system is established. Secondly, a risk assessment model which integrates failure mode and effects analysis (FMEA), a belief rule-based Bayesian network (BRBN) and evidential reasoning (ER) is proposed to quantify the risk level of influential factors. Finally, a case study of the Songhua River was carried out to verify the feasibility and practicality of the established risk evaluation index system and research methods. The targeted preventive measures are proposed to improve the safety of IWT. This study shows that misobservation and poor safety awareness are the most important human factors affecting the safety of IWT, whereas the organizational factors have relatively low risk priority. It is suggested that stakeholders should strengthen the assessment of crew members and improve their ability to recognize hazards.