The nonlinear mechanical behaviour of pipeline joints influences the seismic response of water supply pipelines. This study presents an experimental investigation of the tensile behaviour of push-on joints of ductile iron (DI) pipelines, subjected to axial tensile forces and internal water pressure. The axial performance and damage states of joints are determined for push-on joints with different diameters. A statistical analysis is then conducted to determine the correlation between tensile strength and joint opening. An empirical equation for estimating the tensile strength of pipeline joints is proposed, along with a normalized failure criterion for joint opening considering water leakage. Moreover, a numerical model for buried pipelines considering nonlinear soil-pipe interaction is developed. Incremental dynamic analysis (IDA) is performed on DI pipelines with explicit consideration of the uncertainty of joint mechanical properties. Seismic fragility curves are developed based on the IDA results. The effect of mechanical parameter uncertainty of pipeline joints on seismic risk assessment of segmented pipelines is quantitatively evaluated. The numerical results indicated that the failure probability of the pipeline considering the uncertainty of joint mechanical properties is approximately 1.5 to 2 times larger than that predicted by a deterministic model.

API-RP2EQ (2021) has recommended annual probabilities of failure for Jacket-Type Offshore Platforms (JTOPs) against earthquake events and has specified that catastrophic failure modes that can lead to environmental damage or loss of structural integrity shall not occur during an Abnormal Level Earthquake (ALE). In this study some structural and non-structural limit states are proposed for the seismic evaluation of JTOPs and a comprehensive methodology is used for evaluating the probabilities of reaching relevant limit states, considering the nonlinear dynamic behavior of JTOPs, soil-pile interaction, pipeline risers, and relevant uncertainties. Incremental Dynamic Analysis (IDA) has been carried out on finite element models of platforms, and record-to- record and epistemic uncertainties have been considered in deriving fragility curves. Results show that the slope-based limit states derived from nonlinear static pushover curves provide a fairly good estimate of the target annual probability of structural failure. They also show that a non-structural limit state associated with containment leakage of pipeline risers should also be considered in the analysis. The research provides valuable insights into probabilistic performance-based seismic assessment of steel jacket-type offshore platforms and indicates that the reserve strength coefficients recommended in the relevant standard may be too conservative.

Ground motion, geotechnical materials, and structural materials are three primary uncertainty sources in the seismic design and assessment of metro station structures. However, the effects of the latter two have often been ignored, which brings doubts about the rationality of the design and evaluation results. In this paper, based on the probability density evolution theory, the non-linear stochastic seismic analyses and reliability analyses under multi-source uncertainty conditions were carried out for a metro station structure in soft soils, and the effects of three primary uncertainty sources, i.e., ground motion, geotechnical materials, and structural materials, were explored. Random variable models were established to quantify the involved uncertainties of non-linear materials. The results showed that for underground structures, the uncertainty of geotechnical materials is nonnegligible, because it not only changes the soil-structure relative stiffness, but also changes the deformation mode of strata, resulting in both an increase in the elastic reliability and a decrease in the elastic-plastic reliability. The uncertainty source of structural materials changes the soil-structure relative stiffness, but has little effect on the lateral deformation response and elastic-plastic reliability of the station structure.

Seismic fragility analysis is an effective method to evaluate the seismic performance of retrofitted wharf systems affected by the uncertainty of soil-cement strength. Nevertheless, fragility analysis usually consumes a large consumption of computational power. In this study, seismic fragility analysis using the artificial neural network (ANN) for the retrofitted wharf, considering the aleatory uncertainty of soil-cement strength and the epistemic uncertainty of the ANN, is carried out; On this basis, the fragility surface for two types of damage limit states considering the uncertainty of soil-cement strength is obtained. It was found that: (1) overall, the soil-cement strengthening strategy is effective for improving the seismic safety of wharf systems, however, the strengthening effect is limited, especially under strong earthquakes, will be further weakened; (2) ANN can effectively predict the maximum seismic response of retrofitted pile-supported wharves, so as to quickly carry out seismic fragility analysis. Examples show that the prediction method has good generalization; and (3) the fragility surface model considers the aleatory uncertainty of soil-cement strength and the epistemic uncertainty of the ANN, which makes the performance-based evaluation of retrofitted pile-supported wharves more comprehensive.

Geomechanical parameters of intact metamorphic rocks determined from laboratory testing remain highly uncertain because of the great intrinsic variability associated with the degrees of metamorphism. The aim of this paper is to develop a proper methodology to analyze the uncertainties of geomechanical characteristics by focusing on three domains, i.e. data treatment process, schistosity angle, and mineralogy. First, the variabilities of the geomechanical laboratory data of Westwood Mine (Quebec, Canada) were examined statistically by applying different data treatment techniques, through which the most suitable outlier methods were selected for each parameter using multiple decision-making criteria and engineering judgment. Results indicated that some methods exhibited better performance in identifying the possible outliers, although several others were unsuccessful because of their limitation in large sample size. The well-known boxplot method might not be the best outlier method for most geomechanical parameters because its calculated confidence range was not acceptable according to engineering judgment. However, several approaches, including adjusted boxplot, 2MADe, and 2SD, worked very well in the detection of true outliers. Also, the statistical tests indicate that the best-fitting probability distribution function for geomechanical intact parameters might not be the normal distribution, unlike what is assumed in most geomechanical studies. Moreover, the negative effects of schistosity angle on the uniaxial compressive strength (UCS) variabilities were reduced by excluding the samples within a specific angle range where the UCS data present the highest variation. Finally, a petrographic analysis was conducted to assess the associated uncertainties such that a logical link was found between the dispersion and the variabilities of hard and soft minerals. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/ by/4.0/).

Recent studies have suggested that brown carbon (BrC), an absorbing component in organic aerosol, has strong absorption in the near-ultraviolet wavelengths, and contributes to regional and global radiative forcing (RF). However, the inclusion of BrC in global climate models leads to significant uncertainties in estimated RF, mainly attributed to uncertain BrC properties and relevant BrC parameters assigned in the model. In this study, we modified the bulk aerosol optical scheme (BAOS) in Community Atmospheric Model version 5.3 by including BrC absorption and evaluated the performance of the modified BAOS by comparing the simulated aerosol absorption with 2-year surface observational data in two Asian cities, Kanpur, India and Nanjing, China. The mean relative errors in the simulated total aerosol absorption (B-abs) and absorption Angstrom exponent in modified BAOS are around 35% in Kanpur and even below 20% in Nanjing. Our results show that the inclusion of BrC remedies the underestimated total aerosol absorption by 20% and 14% on average at Kanpur and Nanjing, respectively, exhibiting a better agreement with ground-based observations of aerosol absorption at both sites. We also conducted a series of sensitivity experiments to quantify the uncertainties caused by varying parameters related to BrC. The model simulations suggest that the imaginary refractive index of BrC is the most significant factor contributing to the uncertainties in aerosol optical properties calculated in BAOS at the Kanpur site. While in the Nanjing site, both particle size distribution and mixing state have dominant impacts on the calculated aerosol optical properties.

Uncertainties in the climate response to a doubling of atmospheric CO2 concentrations are quantified in a perturbed land surface parameter experiment. The ensemble of 108 members is constructed by systematically perturbing five poorly constrained land surface parameters of global climate model individually and in all possible combinations. The land surface parameters induce small uncertainties at global scale, substantial uncertainties at regional and seasonal scale and very large uncertainties in the tails of the distribution, the climate extremes. Climate sensitivity varies across the ensemble mainly due to the perturbation of the snow albedo parameterization, which controls the snow albedo feedback strength. The uncertainty range in the global response is small relative to perturbed physics experiments focusing on atmospheric parameters. However, land surface parameters are revealed to control the response not only of the mean but also of the variability of temperature. Major uncertainties are identified in the response of climate extremes to a doubling of CO2. During winter the response both of temperature mean and daily variability relates to fractional snow cover. Cold extremes over high latitudes warm disproportionately in ensemble members with strong snow albedo feedback and large snow cover reduction. Reduced snow cover leads to more winter warming and stronger variability decrease. As a result uncertainties in mean and variability response line up, with some members showing weak and others very strong warming of the cold tail of the distribution, depending on the snow albedo parametrization. The uncertainty across the ensemble regionally exceeds the CMIP3 multi-model range. Regarding summer hot extremes, the uncertainties are larger than for mean summer warming but smaller than in multi-model experiments. The summer precipitation response to a doubling of CO2 is not robust over many regions. Land surface parameter perturbations and natural variability alter the sign of the response even over subtropical regions.