Exceedance Probability Research Articles

Statistical downscaling of precipitation in northwestern Iran using a hybrid model of discrete wavelet transform, artificial neural networks, and quantile mapping

Downscaling of daily precipitation from Global Circulation Models (GCMs)-predictors at a station level, especially in arid and semi-arid regions, has remained a formidable challenge yet. The current study aims at proposing a coupled model of Discrete Wavelet Transform (DWT), Artificial Neural Networks (ANNs), and Quantile Mapping (QM) for statistical downscaling of daily precipitation. Given the historic (1978–2005) and future (2006–2100) predictors of eight-selected GCMs under Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5, a viable DWT-ANN model was developed for each station. Subsequently, we linked QM to DWT-ANN for bias correction and drizzle effect postprocessing of the DWT-ANN-historic/future projected precipitation. The skill of DWT-ANN-QM was demonstrated using various evaluation metrics, including Taylor diagram, Quantile–Quantile plot, Empirical Cumulative Distribution Function, and wet/dry spell analysis. We appraise the efficacy of the coupled model at 12 weather stations over the Gharehsoo River Basin (GRB) in northwestern Iran. Compared to the observed wet/dry spells, the dry-spells were better simulated via DWT-ANN-QM rather than the wet-spells wherein length and exceedance probability of the spells were overestimated. Results indicated that the future precipitation across the GRB will rise, on average, from 10 to 17% depending on weather station. Seasonal spatial distribution of the middle future (2041–2070) precipitation illustrated that an increase for fall and winter, especially, is expected, whereas the amount of the future spring and summer precipitation is projected to be declined. Having been developed and tested in a semi-arid basin, the efficacy of the coupled model should be further assessed in more humid climates.

Numerical Simulation on Aftershock Fragility of Low-Ductility RC Frames under Different Mainshock-Induced Damage Conditions

Aftershocks typically occur multiple times following major earthquakes, potentially inflicting enhanced damage. It is crucial to quantify the impact of aftershocks on the seismic performance of low-ductility reinforced concrete (RC) frames with different mainshock-induced damage states. For this purpose, this study investigated the aftershock fragility of low-ductility RC frames with different damage states induced by mainshocks via the case study of a six-story RC frame without a seismic design. The models of the low-ductility RC frames with different damage states were established via OpenSees software 2.5.0. Incremental dynamic analysis (IDA) was carried out to establish damage states induced by a mainshock. Then sixty-five real aftershocks were inputted to analyze the structures with different main-induced damage states. Aftershock fragility curves of low-ductility RC frame structures with different damage states were obtained. The results show that the exceedance probability of the low-ductility RC frame with mainshock-induced damage is higher than that of the intact low-ductility structure, corresponding to each limit state. The severity of the mainshock-induced damage directly amplifies the demand for a low-ductility RC frame in the subsequent aftershocks. As the mainshock-induced damage increases, the exceedance probability of the low-ductility RC frame undergoing more severe damage under aftershocks significantly increases.

Site-specific probabilistic seismic hazard analysis in Nuweiba, Gulf of Aqaba, Egypt: Combining area source model and active faults

Gulf of Aqaba in Eastern Egypt is characterized by significant seismic activity and high deformation rate, making it a major source of destructive earthquakes in the region. While previous studies have produced probabilistic seismic hazard analysis (PSHA) maps for the region, they have not adequately accounted for active faults or site response in their calculations. This study introduces a newly developed seismic source model for the Gulf of Aqaba, considering area source, active fault source, and a combination of both models to improve seismic hazard assessment for the region. The study incorporates the previously measured average shear-wave velocity for the upper 30 m (Vs30), fundamental frequency (F0), and amplification factor (A0) at some selected sites in Nuweiba city. Accordingly, site-specific PSHA is carried out for the bedrock condition (Vs30 = 800 m/s, according to the Egyptian Building Code) and for the surface of soft sediments using F0, A0, and Vs30. Uncertainties in slip rate, maximum magnitude, and recurrence model are accounted during PSHA computation. Ground motion parameters such as peak ground acceleration (PGA) and spectral acceleration (SA) for various probabilities of exceedance in 50-year lifetime are calculated for both rock site conditions and soft sediments. The maximum calculated PGA and SA are 0.65 g and 1.25 g, respectively, for a 10% probability of exceedance over 50 years at bedrock conditions.

Influence of geological uncertainty on longitudinal deformation of tunnel based on improved coupled Markov chain

The complex geological condition encountered during excavation is the key challenge for tunneling projects. Therefore, tunnel design and construction require a comprehensive understanding of the subsurface geology. This study aims to reveal the effect of geological uncertainty on tunnel longitudinal deformation based on a reconstruction geological model using the improved coupled Markov chain method. The improved coupled Markov chain method makes the predicted results more suitable for the possible stratigraphic distribution by optimizing the transition probability matrix, and breaks through the limitations of non-coaxial predicted cell updating in traditional methods. A mapping method was proposed to establish a matching relationship between the irregular grids of the geological model and the numerical tunnel model. Then, a long tunnel in Shenzhen was selected as a case study to demonstrate the effectiveness of the proposed framework. The correlation coefficient was used to verify the relationship between tunnel deformation and the surrounding stratum with different tunnel cover depths. The geological uncertainty of various strata was quantitatively measured based on the proposed geological uncertainty index. The three-dimensional strata surrounding the tunnel were reconstructed based on exceedance probabilities for adverse geological conditions. Quantitative analysis was conducted on the tunnel vertical convergence during the excavation process.

Modelling the effect of cascade reservoir regulation on ice-jam flooding

In regulated rivers, dams and their reservoirs are the main means to change the flow conditions, and strategic reservoir operations can be an important measure for mitigating ice-jam flooding (IJF). Previous studies have shown the effect of single reservoir operation on IJF probabilities, but little is known about the effect of cascade reservoir operation on IJF occurrences and severity. This study presents a probabilistic modelling framework that couples machine learning (LSTM, Long Short-Term Memory) and river ice (RIVICE) models to assess the effects of regulated and naturalized flows on IJF backwater levels. The trained LTSM shows good performance in both validation (RMSE = 398.88, R = 0.86 and NSE = 0.67) and testing (RMSE = 366.57, R = 0.84 and NSE = 0.71) periods and the calibrated RIVICE can well simulate backwater levels caused by three IJFs from 2008 to 2010 with total volumes of incoming ice for 0.4 million m3, 0.55 million m3 and 0.32 million m3. The completed framework was used to assess IJF hazard along the Sanhuhekou bend, a reach of the upper Yellow River, whose hydraulic regime is impacted by eight upstream reservoirs. The effect of naturalized and regulated flows on IJF are compared from two aspects. Firstly, comparing the median of naturalized and regulated IJF backwater level ensembles, the results show that regulation increases the IJF backwater levels. Secondly, comparing the extreme values of naturalized and regulated IJF backwater level ensembles, results show that regulation can increase the hazard of IJF inundation when annual exceedance probability (AEP) is less severe than 1:100 and regulation can decrease the hazard of IJF when AEP is more severe than 1:125.

A framework for multivariate analysis of compound extremes based on correlated hydrologic time series

While hydrologic design has primarily relied on use of annual maxima values, many events of hydrologic interest such as active and break spells in monsoonal rains, heat waves, flash flooding from snowmelt, etc. manifest at time scales of days to weeks, and require daily (or finer resolution) data for proper characterization. Often combinations of several hydrologic variables herald highly impactful events and are labelled as compound extremes. Using a time-varying interval-censored estimation method of copula models, a novel multivariate approach for dealing with compound extremes under temporal dependence amongst variables and when data contain significant ties is developed here to enable the determination of design magnitudes and associated risk. The efficacy of this method is demonstrated over the Godavari River Basin, India, using daily precipitation and temperature data from a recent period (1977 to 2020) during the monsoon season. A conservative approach is proposed for estimating the design magnitudes of hydrologic variables in multivariate settings. The significance of ties and temporal dependence amongst precipitation and temperature data in estimation of design magnitudes of cold-wet compound extremes at specified probabilities of exceedance is explored at various spatial scales. Ties and temporal dependence are both shown to have a profound influence on design estimates. Since ties and temporal variation in dependence amongst hydrologic variables are ubiquitous features of most hydrologic data, this framework would be applicable for characterization of other compound extremes in hydrology.

Effect of material characteristics of lead rubber isolators on seismic performance of box girder bridge

Near-field (NF) earthquakes have distinct ground motions, forward directivity pulses, and fling-step motions, causing structural responses to differ from far-field (FF) earthquakes. Seismic isolation is regarded as a developed and successful technology that may be applied to enhance a structure’s functionality and safeguard it against catastrophic earthquake effects. The variation in mechanical properties of seismic isolation also significantly influences bridge seismic response. The study investigates the influence of lead rubber (LRB) isolators and the characteristics of ground motions on seismically isolated bridges, aiming to determine optimal parameters for minimal earthquake response. Key parameters include ground motion characteristics, characteristic strength (Q), and isolator flexibility. The study modeled the force-deformation behavior of isolators using bilinear behavior, reflecting the Bouc-Wen hysteric model. CSI Bridge was used to model seismically isolated steel box girder bridges, with eight natural accelerograms assessing a 2% probability of exceedance in 50 years. The peak responses of pier displacement (MPD), isolator hysteric energy (HED), base shear, and deck acceleration are chosen as the response parameters for the comparison. To evaluate the response parameters, the earthquake data are scaled to the three studied peak ground acceleration (PGA) levels of design level (0.2 g), extreme level (0.4 g), and rare-extreme level (0.8 g). The findings offer insight on the relevance of isolator stiffness and its influences on the seismic performance of isolated bridges. The study identifies minimum values for pier displacement, hysteric energy, deck acceleration, and base shear at specific Q/weight sustained by isolator (W) and time period (T) values. Recommendations are made for the preliminary seismic isolation design of bridges with LRB isolators, highlighting the importance of PGV to PGA ratio in earthquake damage assessment.

Development of a convolutional neural network based regional flood frequency analysis model for South-east Australia

Flood is one of the worst natural disasters, which causes significant damage to economy and society. Flood risk assessment helps to reduce flood damage by managing flood risk in flood affected areas. For ungauged catchments, regional flood frequency analysis (RFFA) is generally used for design flood estimation. This study develops a Convolutional Neural Network (CNN) based RFFA technique using data from 201 catchments in south-east Australia. The CNN based RFFA technique is compared with multiple linear regression (MLR), support vector machine (SVM), and decision tree (DT) based RFFA models. Based on a split-sample validation using several statistical indices such as relative error, bias and root mean squared error, it is found that the CNN model performs best for annual exceedance probabilities (AEPs) in the range of 1 in 5 to 1 in 100, with median relative error values in the range of 29–44%. The DT model shows the best performance for 1 in 2 AEP, with a median relative error of 24%. The CNN model outperforms the currently recommended RFFA technique in Australian Rainfall and Runoff (ARR) guideline. The findings of this study will assist to upgrade RFFA techniques in ARR guideline in near future.

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

AbstractRegional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios.

Modelling non-stationarity in extreme rainfall using large-scale climate drivers

Flood estimates used in engineering design are commonly based on intensity–duration–frequency (IDF) curves derived from historical extreme rainfalls. Under global warming these extreme rainfalls are increasing, threatening the capacity of existing infrastructure to resist failure as IDF curves traditionally assume no change in rainfall magnitude. Hence, there is a need to investigate the implications of non-stationarity in extreme rainfalls used to derive IDFs across storm durations and annual exceedance probabilities (AEPs). One way of doing this is to incorporate covariates into the fitted probability distribution. However, there are few studies which examine non-stationarity in extreme rainfall using large-scale climate drivers as covariates, with little consensus on which covariate is the most appropriate. Here we evaluate non-stationarity in extreme rainfall across different durations, from the 1 in 5 AEP to the 1 in 100 AEP, using potential large-scale climate drivers including global and continental mean temperature, continental diurnal temperature range, continental dewpoint temperature, continental precipitable water, the Indian Ocean Dipole, the El Niño Southern Oscillation, and the Southern Annular Mode. These covariates are linked to the three parameters of the Generalized Extreme Value distribution to identify the most appropriate form of non-stationary probability model. We analyse 16 different storm durations from 6 min to 7 day annual maxima across 46 stations in Australia. Based on the Akaike Information Criteria, precipitable water is the superior covariate at a large proportion of stations irrespective of storm duration. However, when the modelled quantile changes are inspected, only global temperature is able to adequately capture the variability in changes across both storm duration and annual exceedance probability. Further, when regional average values of mean temperature, diurnal temperature range, dewpoint temperature and precipitable water were used as covariates, there was no improvement in the model performance compared to continental-wide covariates, particularly for short durations. The results using a mean global temperature covariate show that stationary historical rainfall quantile estimates are underestimated by 12 % – 9 % for frequent (1 in 5 AEP) and 23 % – 13 % for rare (1 in 100 AEP) short duration (6 min – 30 min) events. Moving forward our results suggests infrastructure design needs to incorporate non-stationarity in IDFs to ensure infrastructure and flood planning levels are not under-designed.

Dynamic serviceability and safety reliability analysis of aging PC girder bridges with non-prestressed reinforcement considering concrete shrinkage, creep and stochastic vehicle load flows

Prestressed concrete (PC) bridges’ service performance displays time-dependency due to several factors including concrete shrinkage, creep, resistance degradation, and stochastic vehicle load flows. These non-stationary vehicle load flows, combined with concrete shrinkage, creep, and resistance degradation, result in sequential alterations of internal forces and deformations in simply supported PC girder bridges, ultimately influencing their dynamic reliability. This study proposes a robust methodology that considers the impact of traffic loading, shrinkage, and creep to analyze the dynamic reliability of simply supported PC girder bridges through the development of a load probability model. A stochastic model for truck loads is generated using motion measurement data, which can be used to calculate bridge internal forces and subsequent quantitative analysis of structural deformation due to shrinkage and creep. Subsequently, the formula for the first exceedance probability under resistance degradation is presented and validated through Monte Carlo simulation. Reliability analysis is conducted for both serviceability and load carrying capacity limit states to assess the impact of shrinkage, creep, and increased mean vehicle load on structural reliability. The proposed method offers the theoretical foundation and practical guidance for assessing the structural health status of in-service bridges, thereby providing valuable insights into their long-term performance and maintenance requirements.

A copula-based approach to instream ecological flow determination considering inter- and intra-annual runoff variability

ABSTRACT Instream ecological flow (IEF) provides a flow reference value for maintaining the stability of river ecosystems and enriching biodiversity. Existing methods for determining the IEF often consider only one of the intra-annual runoff distribution characteristics or the inter-annual runoff distribution characteristics. In this study, a copula-based ecological runoff determination method is proposed to consider both annual and monthly runoff magnitudes. The marginal distribution functions of annual and monthly runoff are first determined using the maximum likelihood method. Then the copula function is used to construct the joint distribution function of annual and monthly runoff series. Using the flow duration curve method, the IEF under different annual and monthly runoff exceedance probabilities is calculated. The probability values are combined with the initial IEF values to determine the final IEF process. A case study is conducted using real-world data from the Tongtian River in China. The results show that the total annual basic ecological water demands of the Tongtian River in abundant, flat, and dry water years are 138, 102 and 72.2 billion m3. The proposed method effectively avoids the disturbance of extreme runoff.

Frequency curves of high and low flows in intermittent river basins for hydrological analysis and hydraulic design

Upper and lower percentiles of Flow Duration Curves (FDCs) of daily streamflow data were investigated to develop frequency curves. Upper percentiles with exceedance probability of 1, 5 and 10% (Q1, Q5, Q10) were used for high flows, and lower percentiles with non-exceedance probability of 10, 5 and 1% (Q90, Q95, Q99) for low flows. Median value (Q50) was covered to represent the average conditions of streamflow. A mixed frequency analysis based on the total probability theorem taking zero values into account was applied for the lower percentiles of FDC. Case studies were performed for three intermittent Streamflow Gauging Stations (SGSs) from Kucuk Menderes River Basin in western Turkey. An overall assessment of results shows that the best-fit probability distribution function does not change from one SGS to another considerably for low flows while each SGS has its own probability distribution function for high flows. Upper and lower percentiles, and median value were calculated at various return periods by using the identified probability distribution functions. The calculated values were plotted in the form of frequency curves of high flow percentiles and low flow percentiles. The frequency curves have a practically significant potential use in hydrological analysis, water resources management and hydraulic design under high and low flow conditions. They are yet open to further development for regionalization and their applicability can be extended to ungauged sites in river basins.

Maximum Drift Demands of Earthquake Damaged Reinforced Concrete Columns Based on Residual Flexure Cracks

Assessment of existing reinforced concrete (RC) structures after an earthquake is a challenging task that must somehow relate qualitative and quantitative observations in the plastic hinge regions and the associated residual deformation capacity of damaged structures. Having an estimate available for the remaining drift capacity will result in more economical and informed decisions regarding demolition or strengthening options. This study aims to develop a practical methodology to estimate the maximum drift demand of an RC column based on the residual crack width. For this purpose, fiber-based frame elements are used to model the RC column considering appropriately concrete behavior in compression and tension stiffening effects. Afterwards, the accuracy and reliability of the proposed methodology are demonstrated by validating the computational approach with two cyclic experimental results from literature and new test data for a one-bay one-story RC frame conducted within the course of this study. A comprehensive parametric study is performed for RC columns with different axial loads, longitudinal and transverse reinforcement ratios, and ground motions to exhibit the stochastic behavior. The study identifies the axial load ratio as the predominant parameter. Key findings include strong correlations between maximum drift ratios and total residual crack widths, as well as maximum compressive strains, with regression analysis yielding equations for accurate drift ratio estimation. Simple predictive models are proposed to estimate the maximum deformation demands based on observed residual crack widths. Residual cracking exceeding 5 mm poses significant risk for the columns with axial load ratios above 0.4, with 90% probability of exceedance 2% drift ratio.

A Comprehensive Evaluation of Tuned Vertical Isolation System for Seismic Risk Mitigation

Abstract Vertical isolation by dividing the building into two soft and stiff sub-systems benefits from the period shifts and the damping mechanism across the height. However, the displacement demand imposed on the soft sub-system is less applicable in congested urban areas. As a result, in this study, a hybrid system of vertical isolation system benefiting from a tuned soft subsystem divided into upper and lower portions is investigated. A parametric linear 3-Degree of Freedom (DoF) model of the system incorporating mass and frequency ratio of the sub-systems was introduced and analyzed by response spectrum in MATLAB. A closed-form solution for the system frequency and mode shapes was also established. Response spectrum analysis indicates increasing the Tuned Mass Damper (TMD)’s fundamental period to 2.5 times the soft sub-system’s, reduces its displacement to more than 40 percent. The Multi Degree of Freedom (MDoF) model of the system is parametrically generated in MATLAB. Time history analysis of the building subjected to 40 records with 2 and 10 percent probabilities of exceedance in 50 years compared with conventional vertical isolation reveals the lower soft sub-system displacement can be reduced up to 45 percent by shifting drift to upper stories. The innovative tuned vertical isolation by demonstrating superior control performance as comprises lower floors’ drift may be an applicable solution for adjacent high- and low-rise buildings.

Assessing the environmental pesticides impact of river sediments from a basin in western Mexico: Spatiotemporal distribution, risk assessment of aquatic invertebrates and pesticides prioritization

The intensive use of pesticides in Mexican agriculture has contributed significantly to the increase in food production, but at the same time represents potential risk to biota. This situation creates a dilemma between the need to increase food production and the preservation of the environment and human health. Aquatic invertebrates play a vital role in the balance of aquatic ecosystems but are sensitive to pesticides contamination. The sensitivity of aquatic invertebrates to pesticides contamination has led them to be used to assess the potential impact of this contamination on aquatic ecosystems. In the present study, conducted in the Ayuquila-Armería basin, the following aims were achieved: 1) quantifying the presence of 20 pesticides in river sediments, 2) assessing the spatiotemporal distribution of pesticides in river sediments, 3) determining the potential risk to aquatic invertebrates, and 4) prioritizing pesticides based on their potential risk. Twelve pesticides were consistently quantified in 192 river sediments samples. The pesticides with the highest concentrations were ametrine, malathion and picloram. The temporal analysis showed seasonality in pesticide concentrations, with higher detection frequencies during the wet season. The risk assessment showed that aquatic invertebrates may be affected by the concentrations of carbofuran, malathion, diazinon and ametrine. Pesticides prioritization identified ametrine, carbofuran, and diazinon as major concerns based on the methodology that considers the Frequency and Extent of Exceedance. This study provides valuable insights into the current pesticides scenario in the Ayuquila-Armería River sediments. The findings underscore the need for sustainable alternatives to mitigate the ecological risks associated with pesticides contamination in this aquatic ecosystem.

A framework for multi-risk assessment in a historical area of Lisbon

Historic areas have faced escalating risks due to population growth, urbanization, climate change, and increasing public awareness. Existing risk assessment methods often focus on individual hazards, necessitating a more integrated approach. This research introduces a comprehensive multi-risk model to assess the potential impacts of rainfall and earthquakes on Lisbon's historic city centre. The model considers key risk components – hazard, exposure, and vulnerability – providing a detailed evaluation. Pluvial flood hazards for different return periods were analysed using a proper hydrological model, and seismic hazards were estimated based on PGA values (0.035g–0.25g) with a 10 % exceedance probability over 50 years. Concerning the exposure, the paper integrates spatial distribution data acquired through satellite remote sensing and GIS tools, along with census data. Vulnerability assessments for ordinary unreinforced masonry buildings (URM) used the Risk-UE method and flood stage-damage curves. The results obtained through a multi-exposure and multi-vulnerability framework provide insight into the expected building losses: for a 475/500-year return period, the estimated losses are approximately 1150 M€ for earthquakes and 22 M€ for floods. The higher losses for earthquakes are attributed to structural damage, whereas flood losses are predominantly associated with indirect impacts and loss of contents. This study marks a novelty in the application of risk assessment methodologies in Portugal addressing multiple hazards and highlights the protection of the built environment to mitigate economic and human losses. The paper serves as a starting point for further assessments, offering an overview of the influence of multiple risks on urban settings.

Investigation of temperature effects on wide steel box girder of suspension bridge based on long-term monitoring data

The time-varying temperature distributions on bridge structures may remarkably change structural performance, which may result in differential strain/stress responses on structural members compared with the design conditions. Therefore, it is crucial to have a comprehensive understanding of temperature distributions and its effects on bridges. In this study, taking advantage of structural health monitoring technology, 1-year field monitoring data collected from a long-span suspension bridge were used to investigate the temperature distributions and their effects on the steel box girder. Specifically, the distributions and probability statistics of temperatures on the top and bottom plates were firstly analyzed. Based on which, the transverse and vertical temperature differences on the box girder were further examined, moreover, the representative values of temperature differences for various return periods were calculated by exceedance probability method. At end, a temperature prediction method was proposed to simulated the temperature field distributions during bridge life cycle, to provide substantial temperature data for estimating future operation condition. The results of this study were beneficial to structural evaluation of in-service bridges to ensure their serviceability and integrity in the life cycle.

XWaveNet: Enabling uncertainty quantification in short-term ocean wave height forecasts and extreme event prediction

To aid accurate short-term forecast of wave heights and extreme events prediction from buoy data, a deep learning approach is proposed. Wave heights are forecasted up to 72 h in advance from multivariate meteorological and oceanographic data, followed by quantification of uncertainty in the forecasted values, and estimation of exceedance probabilities for extreme event conditions. Using Monte Carlo dropout technique, network regularization and forecast uncertainty assessment were performed. To forecast wave heights, a stacked long short-term memory (LSTM) model was trained on historical buoy data augmented with positionally encoded features. Per comparative evaluation of different models, across the 72 h forecast horizon, positionally encoded temporal attributes resulted in a mean prediction error and forecast uncertainty of only 3 % and 13.5 %, respectively. Contrarily, without temporal feature augmentation mean error was 5 % and uncertainty, 15 %. Therefore, the former model was ensembled with a Random Forest model for exceedance probability estimation. The hybrid model yielded a 21.4 % better separability between the extreme and normal observations in the test data from a nearby buoy in the northern Gulf of Mexico than individual models. All the four extreme events in the test set were correctly predicted with 97 % mean accuracy. Further evaluation of the deep ensemble model on 5 other spatially diverse buoy datasets from the Gulf of Mexico, Atlantic and Pacific coasts, suggested excellent generalizability for short-term wave forecasts and extreme event prediction, with an average maximum error of just 5.2 % across all the 6 buoy test datasets and the entire forecast horizon.

Experimental Study Of The Wave Field Around A Monopile Due To Moderate Steepness Irregular Incident Waves

Most offshore wind turbine foundations are of monopile type and need regular marine operations to be conducted nearby during the lifetime of the turbine. Proper wave estimation around the monopile is needed to assure the safety of the operations. The trend of increasing monopile's diameter also adds the need to investigate the wave field around the increased monopile diameter for the marine operation analysis. Experimental campaigns of wave field around a monopile in irregular incident waves with different steepness are reported in this study to extend the available experiments which focused mostly on the runup of cylindrical structures. The wave field at two radial positions and six angular positions around the monopile is investigated. The results are processed to obtain the Linear Transfer Function of the wave field around the monopile and the Exceedance Probability of the wave height and crest of the waves around the monopile. For the tested conditions, the linear solution accurately captures the frequency domain characteristics and the exceedance probability of wave height but fails to predict the crest exceedance probability.