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

Environmental pollution risk caused by urban transport emissions is becoming increasingly serious and is an urgent issue in the process of establishing sustainable transport systems. In particular, the development of accurate microscopic models about motor vehicle emissions and the quantitative evaluation of the impact of various traffic management measures on traffic emissions are crucial. This study established and calibrated a simulation for exhaust emission analysis based on high-precision onboard emission test data. Using the simulation models, this study analyzed the emissions ratios of CO, HC, and ${\mathrm{NO}}_x$ of mixed traffic (including light and heavy vehicles) at typical urban intersections. The results reveal the emission patterns of mixed traffic and show that heavy vehicles contribute to over 70% of CO, 65% of HC, and 90% of ${\mathrm{NO}}_x$ at urban intersections even though the number of heavy vehicles is merely 20% of overall traffic. The results indicate that the storage length of lanes at an intersection does not have significant effects on traffic emissions. The results provide the scientific basis for formulating effective energy-saving, emission-reducing traffic management measures and establishing a green transport system.

Current design codes for long-span bridges rely on the conservative live load models developed based on the assumed severe traffic congestion scenarios. To properly consider the site-specific traffic environment in designs of long-span bridges, this study proposes a new live load model. First, traffic environments of long-span bridges are investigated to propose classifications according to the percentage of heavy vehicles and the occurrence frequency of traffic congestion. For multilane bridges, characteristic lanes (car, middle, and truck) are defined depending on their locations, and congested traffic flows are generated through microsimulation models to consider changes in vehicle speed and driving lanes. The traffic simulation produces live load samples, which provide a basis for a design lane load model and multiple presence factors for long-span bridges. As numerical examples, the live load effects of an actual bridge were estimated by the proposed live load model and compared with those in the current bridge design code of South Korea. The results show that the live load model of the existing code is overly conservative, whereas the proposed live load model reflects the actual traffic environments of long-span bridges effectively.

Accurate elimination of environmental variability on bridge modal frequency is a prerequisite for high-quality structural performance evaluation. However, the non-Gaussian and nonlinear characteristics of data distribution associated with variable environments restrict the application of anomaly warning methods with inaccurate or unreliable detection results. Consequently, an early warning method in abnormal modal frequency based on the localized principal component differences model through integrating the slow feature analysis (SFA) and $k$-nearest neighbor rule is proposed in this paper. SFA is first used to extract the measured slowly features of modal frequency for dimensionality reduction and redundant information elimination. Second, the localized modal set of each sample can be automatically searched from the training database based on the Euclidean distance metrics. Third, the estimated slowly features of modal frequency can be calculated using the mean vector of this set. Finally, the environmental variability can be suppressed through the principal component differences between measured and estimated slowly features. After this analysis, an early warning index of modal abnormality (i.e., Mahalanobis distance) is defined for enlarging slight changes in abnormal frequency. The warning results of Z24 bridge indicate that the proposed method discards the environment-induced modal variability without environmental measurements by fully considering both the nonlinearity between modal variables and the non-Gaussianity of data distribution, and the detectability of frequency anomalies outperforms conventional methods under various modal order combinations.

Due to the rapid development in the number of bridges worldwide, risk assessment has gradually been an important step to evaluate the safety of bridge construction. However, the assessment results are usually affected by the subjectivity and individual preference of the bridge experts. In this paper, an innovative bridge construction risk assessment algorithm considering multi-information fusion is proposed based on cluster analysis, set-valued statistic, and entropy method. First, the weight of each individual expert is obtained by adopting cluster analysis to reduce the influence of individual bias from experts. Second, the index weight considering importance-based and condition-based information fusion is achieved based on the minimum cross-entropy principle. The importance-based weight of each index is achieved by the fall-shadow method based on the set-valued statistic. The condition-based weight of each index is computed using the entropy method with respect to the evaluation results of the indexes. Finally, the fuzzy comprehensive evaluation process is utilized to conduct the assessment from the bottom layer to the target layer inside the hierarchical risk index system; the risk level of the target bridge construction safety and corresponding control principle are achieved based on the risk evaluation criteria. An example risk assessment of the construction stage for a long-span suspension bridge is applied to illustrate the validity of the proposed approach.

Abstract

Uncertainties are predominant in hydropower development that lead to risks of time and cost overruns. The overruns’ assessment requires quantitative data that is difficult to obtain and sometimes partially or completely unavailable. Accordingly, this study proposes an expert fuzzy-based system for assessing the impact of uncertainties on time and cost overrun. A Bayesian network analysis is used wherein the experts’ opinions are incorporated through an analytic hierarchy process for the uncertainty’s likelihood and impact. A fuzzy inference system is used to incorporate the decision maker’s optimistic and pessimistic approaches. The proposed methodology is illustrated through a $3\times 42\mathrm{MW}$ run-of-river Myntdu Leshka Hydroelectric project in India. The project’s initial likely completion time was five years with a cost of ₹3,630 million at the 1999 price level. The results state that the project’s time and cost overruns lie between 158% and 196% and between 235% and 276%, respectively, under optimistic and pessimistic conditions. Such a methodology has not been applied by past studies for hydropower projects, which is the study’s main contribution. The methodology can be easily applied by construction project developers globally in different fields by varying the uncertainties, their likelihood, and impact scales as per different risk perceptions and regional conditions.

Practical Applications

The methodology shown in the current study can be applied by hydropower and other construction project developers globally. The likelihood and impact scales, fuzzy optimistic and pessimistic rule base, and number and type of uncertainties can be changed easily based on the developers’ perceptions. In the case of hydropower projects, this method should be applied once the detailed project report (DPR) is prepared and prior to its submission to the approval authority.

The use of porous concrete for paving urban pathways and reducing water pollution is particularly important. Pumice, scoria, zeolite, travertine, and metal sulfide nanoparticles can reduce water pollution. In this study, the effect of the volume percentage of several adsorbents, such as pumice, scoria, zeolite, travertine, and iron sulfide nanoparticles, as alternatives to concrete aggregates, was investigated with regard to the physical properties of concrete specimens, including compressive strength, porosity, and permeability coefficient. In order to study the effect of the adsorbents, a surrogate model trained by metamodel methods was used. Kriging, polynomial response surface method (PRSM), and radial basis function (RBF) methods were used as metamodel approaches. Kriging was found to have the least error in estimating compressive strength, porosity, and permeability coefficients. The failure probabilities of the performance functions defined by the kriging, PRSM, and RBF methods were estimated to be 0.0081, 0.0080, and 0.0019, respectively.

Abstract

The risk of thermostructural instability of thin-walled tanks in petrochemical storage farms due to adjacent fires is an engineering design concern. Thermal buckling of thin-walled tanks could potentially result in disastrous consequences such as large premature deformation and probable cracking of the shell, leading to flammable liquid leakage or spilling and additional pool fire spreading. Various parameters, such as the pool fire’s diameter, stand-off distance, and exposure to multiple surrounding pool fires, are among the factors that can produce different thermal loads on the target structure that empirical or simplified analytical models often fail to accurately predict. Examining these parameters illuminates their influence on thermal loading on cylindrical tanks and their mechanical behavior. In this study, a combined thermostructural approach is exploited in the form of large eddy simulation (LES) to investigate the pool fire geometrical parameters and nonlinear structural thermal instability analysis of thin-walled tanks with fixed roofs. The results reveal that by increasing the fire stand-off distance, the flux received by the target tank reduces at a rate slightly less than the inverse of distance squared, and the risk of a structure’s instability is tangibly postponed. Analyzing pool fire diameter shows that there is a maximum critical temperature for the empty tanks. Also, it is found that the risk of tanks’ thermostructural instability is a function of multiple pool fires arrangement and their geometry, and there is no simple relationship between the number of pool fires and critical destabilizing temperature.

Practical Applications

Fire is one of the major risks that threaten the performance of thin-walled structures such as petrochemical storage in tank farms. Fire would induce heat flux by radiation and convection methods on the tank surfaces and, by irregular temperature distribution, the shell could deform in an unstable critical manner. This study covers the effect of pool fire dimension and its stand-off distance on the structural behavior of thin-walled tanks. The pool fire and the tank are simulated by sophisticated numerical methods. The buckling mode shapes and critical temperatures at which the shell experiences severe and sudden deformation are investigated. Moreover, the effect of fire incidents with multiple pools on storage tanks has been scrutinized. The results would be beneficial for the tank’s design, risk, and uncertainty assessment and also would guide the determination of the optimal position of reservoirs in tank farms.

The present highway geometric design is governed by design speed and considers all other variables such as terrain, roadway, and driver characteristics as deterministic. The behavior of road users can be impulsive, and there is always an element of uncertainty; it requires the introduction of reliability ideas into highway design. Reliability analysis is one method that explains the uncertainty in the geometric design process and assesses the risk related to a particular design feature. The study selected 38 curves with variable geometry located along National Highway 953 (NH-953) in India’s hilly terrains. A total of 13,680 samples of speed data (360 data points for one curve) were collected using the radar gun at the curve midpoint. The first-order reliability method was used to calculate the reliability index ($\beta$) and probability of noncompliance ($P_{\mathrm{nc}}$). The results showed that the $\beta$ of a car is less than heavy commercial vehicles (HCV), and the corresponding $P_{\mathrm{nc}}$ was higher for a car than HCV. Since HCV is moving slower than a car, the safety margin (difference between design and vehicle mean speeds) is more, and $P_{\mathrm{nc}}$ is less. Further, the vehicle mean speed increases, the safety margin decreases, and the resultant $\beta$ of the curves decreases ($P_{\mathrm{nc}}$ increases). Therefore, the chances of crashes are increased. Also, cars have 30% to 32% higher $P_{\mathrm{nc}}$ than HCV. Additionally, the Tobit regression approach was used to develop a $\beta$ model. The results revealed that deflection angle is positively correlated while curve length, tangent length, $P_{\mathrm{nc}}$, and total crashes negatively correlate with $\beta$. Lastly, the developed safety warrants are helpful for the highway authority to identify the unreliable highway segment and provide safety measures to increase the reliability of the segment and reduce the likelihood of crashes.

As the concept and methods of maintenance of infrastructure systems are receiving increasing attention, the optimal maintenance strategy for a group of structures is a promising research topic. Among the factors affecting the decision-making of optimal maintenance strategy for civil infrastructure systems, the target service life and user cost estimation approach are significant. Currently, user cost estimation at the project level is widely adopted, whereas the user cost estimation at the network level is in the development stage. This paper investigates the impact of several factors on the risk-informed optimal maintenance strategy for a bridge network under corrosion, including target service life, user cost estimation approach, and correlation among bridge safety margins. The novelty of this paper consists of considering the effects of these factors on the optimal maintenance strategy of bridge networks. These effects, which are crucial for adopting the optimal maintenance strategy, were not investigated in a detailed manner previously. Two different maintenance strategies are considered for the maintenance of corroded steel girders. The former is to conduct replacement with new carbon steel girders, and the latter is to use A709-50CR steel, where CR stands for corrosion resistant, a new type of steel with a chromium content similar to that of martensitic stainless steel, in replacement actions. Using an existing bridge network under a low life-cycle risk threshold, it is shown that replacement based on A709-50CR girders results in a reduced life-cycle network maintenance cost than replacement based on carbon steel girders. Also, the project-level approach to estimate user cost can lead to a substantial increase in the life-cycle network maintenance cost compared with the network-level approach.

Inherent uncertainties associated with masonry structures result in large scatter in experimentally or analytically predicted behavior. Rigorous investigation of uncertainties in the structural behavior of masonry structures is of paramount importance to lay down the basis for reliable structural design. In this study, the probabilistic behavior of reinforced masonry walls under out-of-plane (OOP) loading was investigated. Uncertainties in material and geometric properties were incorporated in finite-element (FE) models for probabilistic structural analysis. The individual and combined effect of different uncertain input parameters on the overall probabilistic behavior was evaluated. Furthermore, the relative importance of uncertain variables to the load and deformation capacities was assessed using variance-based sensitivity analysis. The model uncertainty in FE-predicted load capacity was quantified to characterize the model error. The results indicate that model uncertainty contributes to the variance in lateral load capacity more than all the other uncertainties in material and geometric properties.

Benchmarking is very valuable for evaluating and comparing methodologies. Here, Gaussian process regression using multiple Gaussian random fields (GPR-MR) is applied to benchmarking data for spatial variability problems. The benchmarking data used were from the literature and included four types of virtual ground models (VG1 to VG4) and one real ground measurement data set. The spatial variability of geological properties is often divided into a trend component and a random component. In GPR-MR, the trend component is expressed by a random field with a large scale of fluctuation (SOF), leading to a smooth (slow) variability, whereas the random component is expressed by one with a small SOF, leading to a rapidly changing variability. The SOF and the standard deviation of random fields were estimated using the maximum likelihood method based on the measured data provided in the benchmarking data. GPR-MR was used to estimate the spatial variabilities of all cases, and its performance was evaluated. For the real ground measured data, model selection was also performed with respect to the autocorrelation function of the random component in terms of information criteria, whereas the Markovian autocorrelation function was used for the virtual ground data without the model selection. Based on the results, the Whittle-Matérn (WM) model was selected for the random component. GPR-MR was used to estimate the spatial variability, and its performance with the WM model was evaluated.

Prototype bioreactor studies are becoming an increasingly common approach to assessing municipal solid waste degradation in landfills, owing to their greater potential for monitoring and control. Though reports of several bioreactor studies have appeared in the literature, the performance of a laboratory-scale bioreactor in the presence of a gas collection system (GCS) has not been investigated anywhere. Because the properties of waste in a landfill or a bioreactor can vary temporally and spatially, it is essential to account for these variabilities while designing a GCS. In this study, a GCS for a prototype bioreactor was designed using kriging surrogate models to account for the variability in the waste properties. The numerical models required to calibrate the kriging models were implemented through TOUGH3 EOS7CA (three-dimensional simulations). The radius of influence (ROI) of a gas extraction well (a critical parameter that controls the design of a GCS) was estimated by selecting methane generation rate ($M_{GR}$), suction pressure ($S_p$), absolute permeability ($k$), and depth of the extraction well ($D$) as the input parameters. The ROI and gas pressure distributions inside the bioreactor were determined accounting for the variabilities in the input parameters through Monte Carlo simulations on the kriging model. Gas pressures inside the prototype bioreactor with a single gas extraction well (GEW) system indicated that the bioreactor was unsafe—values far higher than atmospheric pressures were observed. A sensitivity analysis was conducted to achieve efficiency and optimize the design, revealing that $S_p$ and $M_{GR}$ were the critical parameters that controlled the ROI. Hence, the GCS was designed with two GEWs, and the safety of the bioreactor was assessed by varying only the critical parameters with another developed kriging model. Observance of gas pressures in the range of atmospheric pressure indicated that the design was safe. A relation was developed between $S_p$ and $M_{GR}$, which will help enable a 90% methane recovery at the GEWs. The probability of failure of the prototype bioreactor in the presence of the developed relation was also estimated. The low probability of failure ($1.23\times {10}^{-4}$) indicated that the design was safe.

The paper presents the performance analysis of a well-designed truck parking terminal, which is planned for regulating truck traffic over a commercial port. The designed truck parking terminal is modeled using microscopic traffic simulation, which is validated based on the movement of vehicles to the parking bays. After validation, various scenarios were created to evaluate parking terminal performance by varying the parking volumes and number of operational parking bays. The operational efficiency of the parking terminal for design scenarios was evaluated using parking performance measures that included parking load, average parking duration, parking turnover, and load-to-capacity ratio (parking index). For the design peak load of $\mathrm{4,200}\mathrm{vehicles}/\mathrm{day}$ with a uniform arrival rate, the operational efficiency was found to be about 73%. Interestingly, it was observed that with an increase in the number of operational parking bays, the parking efficiency decreased for the given volume level. Considering this phenomenon, a methodology was developed to identify the optimum number of parking bays under varying demand-supply scenarios. The developed methodology can help identify the optimum number of parking bays for existing and future (expansion) conditions. Furthermore, this study highlights the importance of using simulation in evaluating operational and design aspects of transportation facilities, where the need for repeated empirical observations is eliminated. As such, this study should be of interest to traffic engineers and practitioners interested in the efficient operation of parking terminals.

Abstract

Improving the evacuation efficiency of fire stairways is conducive to ensuring the evacuation safety of underground spaces. Personnel fatigue is the main issue during ascending movement, and it is related to vertical height. During ascending evacuation, using stairway handrails is a typical behavior characteristic that may influence the fatigue and speed of pedestrians, thereby changing evacuation efficiency. Therefore, this study aims to explore the specific effect mechanism of using handrails at different heights on individual fatigue and evacuation speed during ascending evacuation. The within-subject control experiment was completed by recruiting 16 volunteers (8 males and 8 females) in a high-rise hotel building of a university in Chongqing, China. Participants’ features of using handrails, relative heart rate, evacuation time, and evacuation speed data within a range of 10 floors (33.6 m) were obtained through an observation experiment on single-person ascending evacuation. Then, the effect mechanism was analyzed through statistical methods. This study found that participants’ proportion of using handrails surged when the ascending vertical height exceeded 6.72 m and reached saturation at 20.16 m. Additionally, participants’ proportion of using handrails and handrail usage frequency showed similar changing trends and nodes throughout the whole evacuation process. Furthermore, handrails can effectively alleviate the physical fatigue of individuals after ascending over 23.52 m. In addition, the evacuation speed of using handrails during the entire ascending evacuation process was significantly improved; especially, speed attenuation slowed down in the later period. This study can help deepen the understanding of handrail usage behavior during ascending evacuation and promote the sustainable development of underground spaces.

Practical Applications

This study is a representative study within the interdisciplinary scope of safety science and architecture and provides a reference for the safety design of fire stairways in underground spaces. In the current underground space regulations, key parameters such as the setting principle, height, detail parameters, and surface characteristics of stairway handrails are still based on empirical research data or models developed in the ground spaces. With the large-depth and large-scale development of underground spaces and infrastructures, the inadequate applicability of the current underground regulations of fire stairway handrails may threaten the efficiency and safety of evacuation and hide potential threats. Therefore, the vertical layered design of the stair handrail should be proposed. The current standard stipulates that the number of handrails is mainly determined according to the clear width of the fire stairways. The design of fire stairway handrails should also be varied at different vertical heights based on the handrail effect mechanism, as well as the functional characteristics, human density, and evacuation difficulty of underground spaces with various depths. In addition, this study contributes to the construction of a database of individual evacuation behaviors, which provides a new perspective and support for the simulation of microscopic behavior.