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In the last decade, the state departments of transportation in the US have experienced an increase in their construction projects, while the level of their in-house inspection staff and resources has either remained the same or declined. Previous studies have found that one strategy that may reduce the inspection workload is prioritizing construction activities for inspection. However, reducing the number of inspections also has its risks, such as functional failures and reduced design life. Thus, available inspection resources should be allocated to the activities with significant risk consequences if inspection is reduced. The objective of this paper is to develop a risk-based inspection protocol to facilitate efficient allocation of available inspection resources to minimize the risks associated with reduced inspection. First, the risk consequences associated with reduced inspection are identified for various construction activities linked to transportation projects. Based on data collected from 23 state departments of transportation, 58 engineers and inspectors from the Indiana Department of Transportation and 20 inspection consultants in the Midwest, the subjective perceived probabilities associated with the occurrence of each risk consequence are encoded by using fuzzy analysis, from which the risk impacts due to reduced inspection are obtained. The construction activities are prioritized based on the risk impacts associated with reduced inspection into five priority levels. The greater the risk impacts are as a result of reduced inspection, the higher the priority would be for inspection of that activity. Thus, the proposed protocol can be used for resource allocation based on risk impacts. The proposed list of prioritized construction activities can assist project and program managers in state departments of transportation in better allocating their limited inspection resources while reducing the risks due to reduced inspection. [ABSTRACT FROM AUTHOR]

To prevent damage caused by corroding reinforcement, fiber-reinforced polymer (FRP) reinforcing bars have been used in place of steel in a relatively small but increasing number of structures in civil infrastructure. A concern with the use of traditional FRP bars, however, is the resulting lack of ductility. This problem has been overcome with the development of a new generation of composite reinforcement, ductile hybrid FRP (DHFRP) bars. However, standards that address the design of DHFRP bars are unavailable, and appropriate resistance factors for the use of DHFRP reinforcement are unknown. In this paper, a reliability analysis is conducted on tension-controlled concrete flexural members reinforced with DHFRP with the intent to estimate potential strength-reduction factors. Flexural members considered include a selection of representative bridge decks and building beams designed to meet strength requirements and target reliability levels dictated by relevant engineering standards. Nominal moment capacity is calculated from standard analytical models and is taken as first DHFRP material failure. Statistical parameters for load and resistance random variables in the reliability model are consistent with previous code calibration efforts. The resulting resistance factors ranged from 0.61-0.64 for tension-controlled sections, which indicates a potential increase in allowed strength with respect to flexural members using nonductile bars. [ABSTRACT FROM AUTHOR]

This research investigated the confined flow under hydraulic structures. The hydraulic conductivity of the soil was modeled as a spatially random field, which followed a lognormal distribution. Only porous media that have anisotropic heterogeneity were investigated. Results showed that larger anisotropic heterogeneity ratios (the ratio of horizontal to vertical scale of fluctuation) dramatically increased the seepage flow. This increase of flow was more pronounced at larger coefficients of variations. A similar trend was observed in the case of uplift force, although it was less sensitive to the increase in the anisotropic heterogeneity ratio. Moreover, both the seepage flow and the uplift force tended to their deterministic values as the ratio of anisotropic heterogeneity increased. Results also showed that the exit hydraulic gradient attained its greatest value at particular values of the anisotropic heterogeneity ratio. [ABSTRACT FROM AUTHOR]

Liner systems are increasingly being used to protect the environment from contaminated waste. At the same time, society is increasingly insisting on estimates of the probability that these liner systems will fail to achieve their design objectives, one of which is to limit hydraulic flow from the contaminated region to acceptably small levels. This paper presents a methodology to estimate the probability of excessive hydraulic flow, considering the spatial variability of the soil composing the liner (its mean, variance, and correlation length) as well as the liner thickness. Semiempirical equations predicting the mean and variance of the effective hydraulic conductivity of the liner, based on theory and calibrated by random finite-element method simulations, are presented and used to investigate the probability that a liner exceeds regulatory hydraulic flow requirements. The proposed methodology is illustrated by an example. [ABSTRACT FROM AUTHOR]

Droughts are characterized by drought indexes that measure the departures of meteorological and hydrological variables, such as precipitation and streamflow, from their long-term averages. Although many drought indexes have been proposed in the literature, most use predefined thresholds for identifying drought classes, ignoring the inherent uncertainties in characterizing droughts. This study employs a hidden Markov model (HMM) for the probabilistic classification of drought states. Apart from explicitly accounting for the time dependence in the drought states, the HMM-based drought index (HMM-DI) provides model uncertainty in drought classification. The proposed HMM-DI is used to assess drought characteristics in Indiana by using monthly precipitation and streamflow data. The HMM-DI results were compared to those from standard indexes and the differences in classification results from the two models were examined. In addition to providing the probabilistic classification of drought states, the HMM is suited for analyzing the spatio-temporal characterization of droughts of different severities. [ABSTRACT FROM AUTHOR]

Maintaining an adequate safety level in concrete bridges under gradual degradation due to traffic and environmental actions is not only an expensive task, but also filled with some level of uncertainty. Degradation in itself is not easily quantifiable because numerous aspects reduce the load-bearing capacity of a concrete bridge. Furthermore, the effects of preventive maintenance and rehabilitation actions are difficult to predict. Chloride ion ingress is an important aspect of durability design and maintenance, especially in regions where winter salt is commonly applied for traffic safety. This paper introduces a feasible approach to analyze the effects of chloride-induced deterioration on the overall safety level, which is validated by the application to a real highway bridge, which was demolished after 38 years of service. Concrete samples were extracted from this bridge and chemical analyses were performed to determine the chloride concentration profile in the concrete. Once the present chloride levels were determined, an inverse analysis of chloride ion ingress based on the cellular automata technique provided information regarding the temporal and spatial development of the chloride concentration. These data serve as basis for a probabilistic prognosis of the corrosion processes during the planned service life of the structure, which accounts for uncertainties in the experimental investigations. The prediction of nonvisible degradation, e.g., reduced reinforcement area, allows the localization of critically corroded structural elements, and thus represents the main input for a long-term performance assessment. In particular, comprehensive probabilistic analyses reveal the time-dependent reliability levels with respect to code-based serviceability and ultimate limit states. The outcomes of the proposed methodology are a quantification of the current safety levels as well as a prediction of their future development with and without (preventive or corrective) intervention. Both are quintessential for cost efficiency and save maintenance management. Although the concept of sampling and chloride ingress prognosis will be covered, the focus of this contribution lies with the incorporation of time-varying chloride concentration information into the framework of nonlinear analysis and reliability assessment. [ABSTRACT FROM AUTHOR]

A model for project budgeting and scheduling with uncertainty is developed. The traditional critical-path method (CPM) misleads because there are few, if any, real-life deterministic situations for which CPM is a great match; program evaluation and review technique (PERT) has been seen to have its problems, too (e.g., merge bias, unavailability of data, difficulty of implementation by practitioners). A dual focus on the distributions of the possible errors in the time and cost estimates and on the reliability of the estimates used as planned values suggests an approach for developing reliable schedules and budgets with buffers for time and cost. This method for budgeting and scheduling is executed through either simulation or a simple analytical approximation. The dynamic buffers provide much-needed flexibility, accounting for the errors in cost and duration estimates associated with planning any real project, thus providing a realistic, practical, and dynamic approach to planning and scheduling. [ABSTRACT FROM AUTHOR]

A probabilistic framework is presented for evaluation of hurricane wave and surge risk with particular emphasis on real-time automated estimation for hurricanes approaching landfall. This framework has two fundamental components. The first is the development of a surrogate model for the rapid evaluation of hurricane waves, water levels, and run-up based on a small number of parameters describing each hurricane: hurricane landfall location and heading, central pressure, forward speed, and radius of maximum winds. This surrogate model is developed using a response surface methodology fed by information from hundreds of precomputed, high-resolution Simulating Waves Nearshore ( SWAN) + Advanced Circulation Model for Oceanic, Coastal and Estuarine Waters ( ADCIRC) and One-Dimensional Boussinesq Model ( BOUSS-1D) runs. For a specific set of hurricane parameters (i.e., a specific landfalling hurricane), the surrogate model is able to evaluate the maximum wave height, water level, and run-up during the storm at a cost that is more than seven orders of magnitude less than the high-fidelity models and thus meets time constraints imposed by emergency managers and decision makers. The second component of this framework is a description of the uncertainty in the parameters used to characterize the hurricane through appropriate probability models, which then leads to quantification of hurricane risk in terms of a probabilistic integral. This integral is then efficiently computed using the already established surrogate model by analyzing thousands of different scenarios (based on the aforementioned probabilistic description). This allows the rapid computation of, for example, the storm surge that might be exceeded 10% of the time based on hurricane parameters at 48 h from landfall. Finally, by leveraging the computational simplicity and efficiency of the surrogate model, a simple stand-alone PC-based risk-assessment tool is developed that allows nonexpert end users to take advantage of the full potential of the framework. The proposed framework ultimately facilitates the development of a rapid assessment tool for real-time implementation but requires a considerable upfront computational cost to produce high-fidelity model results. As an illustrative example, implementation of hurricane risk estimation for the Island of Oahu in Hawaii is presented; results demonstrate the versatility of the proposed approach for delivering accurate tools for real-time hurricane risk estimation that have the ability to cross over technology adoption barriers. [ABSTRACT FROM AUTHOR]

Response surface methods (RSMs) are attractive approaches for slope reliability analysis because such methods can integrate deterministic numerical slope stability evaluation and reliability analysis. For a slope in layered soils, its performance function is generally nonlinear and the system failure probability could be larger than the failure probability of the most critical slip surface. In this study, the applicability of the kriging-based RSM for system reliability of soil slopes is assessed through its application to two slopes with obvious system effects. It is found that the kriging method combined with Monte Carlo simulation (MCS) can deliver accurate system failure probability estimation. For comparison, the classical RSM based on the iterative local approximation of the performance function may fail to detect the most critical slip surface. The classical RSM can only calculate the failure probability of one slip surface even if the first-order reliability method or MCS is used. Even when the same calibration samples are used, the second-order polynomial-based RSM is not as accurate as the kriging-based RSM. The results in this study show that the kriging-based RSM is an advantageous and promising approach for calculating the system reliability of soil slopes. [ABSTRACT FROM AUTHOR]

Accounting for waterway vessel collision is required in the structural design of bridges spanning navigable waterways. During collision events, massive waterway vessel groups such as barge flotillas are capable of dynamically transmitting horizontal forces to impacted bridge components. Furthermore, collision-induced forces can be sufficient to collapse piers or roadway spans in the vicinity of the impact location. If collapse takes place, economic loss is suffered because of subsequent traffic rerouting and bridge replacement costs. Additionally, fatalities may occur if the roadway is occupied during or shortly after collapse. This paper focuses on the development of a probability of collapse expression for bridge piers subject to barge impact loading, where the relationship can be readily integrated into existing bridge design methodologies. The expression is developed by employing probabilistic descriptions for a multitude of random variables related to barge traffic characteristics and bridge structures in conjunction with nonlinear dynamic finite-element analyses of barge-bridge collisions. Highly efficient, advanced probabilistic simulation techniques are necessarily incorporated into the barge-bridge collision analysis framework to allow feasible estimation of structural reliability parameters. These parameters facilitate the formation of an expression that, in turn, bridge designers can use to estimate probabilities of structural collapse attributable to barge collision, without performing probabilistic analyses. [ABSTRACT FROM AUTHOR]

The statistical characteristics of cost overruns experienced from contract award in 276 Australian construction and engineering projects were analyzed. The skewness and kurtosis values of the cost overruns are computed to determine if the empirical distribution of the data follows a normal distribution. The Kolmogorov-Smirnov, Anderson-Darling, and chi-squared nonparametric tests are used to determine the goodness of fit of the selected probability distributions. A three-parameter Frechet probability function is found to describe the behavior of cost overruns and provide the best overall distribution fit. The Frechet distribution is then used to calculate the probability of a cost overrun being experienced. The statistical characteristics of contract size and cost overruns were also analyzed. The Cauchy (), Wakeby (A$1 to 10 million, ) and four-parameter Burr (A$11 to 50 million) tests were found to provide the best distribution fits and used to calculate cost overrun probabilities by contract size. Ascertaining the best fit probability distribution from an empirical distribution at contract award can produce realistic probabilities of cost overruns, which should then be incorporated into a construction cost contingency. [ABSTRACT FROM AUTHOR]

This paper proposes a generalized probabilistic framework for optimum inspection and maintenance planning of deteriorating structures. The proposed framework covers (1) the damage occurrence and propagation and service life prediction under uncertainty, (2) the relation between degree of damage and probability of damage detection of an inspection method, and (3) the effects of inspection and maintenance on service life and life-cycle cost. Optimum inspection and maintenance types and times are obtained through an optimization formulation by maximizing the expected service life and minimizing the expected total life-cycle cost consisting of inspection and maintenance costs. The service life, life-cycle cost, and maintenance delay, along with inspection and maintenance actions, are formulated using a decision tree model. The selection of the appropriate maintenance type depends on the degree of damage. The proposed framework is general and can be applied to any types of deteriorating structures or materials. Applications of the proposed framework may include, but are not limited to, bridges, buildings, aircrafts, and naval ships. [ABSTRACT FROM AUTHOR]

Characterization of critical water distribution system (WDS) contamination scenarios-defined by a set of attributes, a probability of occurrence, and a specific level of consequences-is a prerequisite for preparation of reliable and cost-effective mitigation, preparedness, and emergency response plans. This study develops Monte Carlo and risk-based optimization schemes to evaluate contamination risk of WDSs for generation of this important class of scenarios, which are representative of the most vulnerable aspects of the system. Defining attributes of contamination scenarios are identified as contaminant type and amount, contamination location, start time, duration, and time of year scenario occurs. Well-documented waterborne outbreaks reported in developed nations are analyzed to empirically estimate statistical characteristics of defining attributes in accidental events. Monte Carlo simulation is conducted to determine the probability distribution of public-health consequences, aggregate conditional risk, and significance of different scenario attributes. A multiobjective optimization methodology is proposed to capture the attributes of critical accidental contamination scenarios. The principal risk components of likelihood and health consequences are treated as optimization objectives and are maximized simultaneously to identify an ensemble of nondominated critical scenarios. The multiobjective approach provides insight into system risk and potential mitigation options not available under maximum-risk or maximum-consequences analyses. Performance and applicability of developed models is demonstrated on the WDS of a virtual midsize city that possesses characteristics of complex real-world distribution networks. [ABSTRACT FROM AUTHOR]

Fundamental uncertainties governing sea level rise projections are associated with the rate of acceleration in global sea level rise and the local factors affecting relative sea level. Recent models and observations of global ice sheets and their contributions to sea level, as well as climate models, suggest that significant sea level acceleration is imminent, yet observational data from tide gauges and satellites have not yet found rates consistent with some of the projections. To allow coastal planners a way to incorporate the uncertainties of acceleration, a synthesis of observed sea level data with selected distributions of acceleration and the current rate of rise is proposed to provide probabilistic estimates of future sea levels. The resulting distributions can be adopted in a risk-based framework to assess project vulnerability. As geophysical understanding of the climate-forced sea level dynamics improves, the selection and veracity of these distributions and their sea level projections will improve. As a case study, the methodology is demonstrated by application in Key West in southern Florida. [ABSTRACT FROM AUTHOR]

A probabilistic porous model is used to simulate the retention curves of different types of soils. The model is based on the same principles of porous networks models; however, it shows important advantages over them. It considers two different elements: the sites and the bonds. It is able to simulate the retention curves of a material when its pore size distribution is known. It can also be used to interpret more realistically the results of mercury intrusion porosimetry tests. In this paper, the model is used to obtain the pore size distribution of different soils using the retention curve as its data source. The comparison between numerical and experimental results for different soils shows that the model approximately reproduces the pore size distribution obtained from mercury intrusion porosimetry tests. [ABSTRACT FROM AUTHOR]

Maximum seismic shear stresses () have been recognized as one of the important parameters in design practice. This study develops ground-motion parameters for and implements these in probabilistic seismic hazard analysis to provide the distribution of deep soil layers for design purposes. The application of improved ground-motion parameters for is demonstrated at the Oakland International Airport, where a thick Young Bay Mud deposit exists under the artificial fill. Model biases in the predictive equations of seismic shear-stress reduction coefficients () are evaluated by comparison with the site response analyses performed with a wide range of input ground motions. Based on these results, we introduce improved ground-motion parameters for as a linear combination of spectral accelerations, implemented in probabilistic seismic hazard analysis to calculate seismic hazard curves. Conditional mean spectra are calculated, given at 10% in 50 years to illustrate the variations in frequency contents with depth compared with the uniform hazard spectra. Finally, is calculated with depth by using hazard values of and compared with the peak-ground-acceleration-based and uniform-hazard-spectra-based calculations. Analysis results show that will be underestimated for deep soil layers by peak-ground-acceleration-based calculation if the median value of is used in design practice. [ABSTRACT FROM AUTHOR]

Environmental factors directly affect a building's performance and durability. They are particularly detrimental in the case of building envelope materials and systems because these tend to exhibit a large exposed area and relatively narrow thickness. Therefore, tools are needed to model degradation and provide an insight into its occurrence and its relationship with the environmental factors that have the worst effect. In this paper, the degradation of stone wall cladding directly adhered to the substrate is estimated based on the evaluation of the characteristics of 140 wall cladding systems inspected in Lisbon, Portugal. Because it is a complex phenomenon, a probabilistic analysis is developed using multinomial logistic regression. The analysis focuses on the environmental factors that most influence the overall degradation of the façades (condition level). Probabilistic patterns are thus obtained for the evolution over time of each condition level according to their exposure level to outdoor environmental conditions. [ABSTRACT FROM AUTHOR]

A computer program is developed to carry out reliability and optimization analysis with many interconnected probabilistic models. The program is freely available online and it contains a library of versatile models. The user can also implement new models-without any recompilation of the program-by several means, including a powerful scripting option. Other novel features include a comprehensive parameterization that facilitates flexible and effective model communication and the computation of direct-differentiation response sensitivities in multimodel analysis. The new program has already been successfully employed for regional risk analysis with thousands of model objects and hundreds of random variables. This paper presents the software architecture and a multimodel example of a structure that is modeled in an external finite-element program and subjected to multiple hazards, damage, and long-term deterioration. [ABSTRACT FROM AUTHOR]

An easy-to-use and physically based analytical probabilistic model is developed to evaluate the long-term average hydrologic performance of green roofs. The probabilistic models of local rainfall characteristics are introduced first, the hydrologic and hydraulic processes occurring on and inside a green roof system are then described mathematically, and the closed-form mathematical expressions depicting the storm-water management performance of a green roof system are finally obtained using the derived probability distribution theory. Simplifying assumptions are made to mathematically describe the hydrologic and hydraulic processes. The validity of these assumptions and the overall probabilistic approach is demonstrated by comparing its outcomes with results from a series of continuous simulations using long-term rainfall data from Detroit, Michigan and observations from a real case study in Portland, Oregon. [ABSTRACT FROM AUTHOR]

This paper proposes deriving regional intensity-duration-frequency (IDF) curves for Edmonton, Canada, based on the scaling property of precipitation data using ensemble empirical mode decomposition (EEMD). Selected sets of annual maximum precipitation data were decomposed by the EEMD to intrinsic mode functions (IMFs), and the scaling property was investigated. Next, representative scale exponents were extracted. The results show that quantiles derived from general extreme value (GEV) probability distribution (PD) with parameters derived by the probability-weighted moment (PWM) are more accurate than those derived from the extreme value type I (EVI) PD with parameters derived by the method of moment (MOM), whose underestimation of rainfall intensity becomes obvious for high return period (greater than 25 years) and short duration (less than 1 h). The results also show that for Edmonton, generally three of four IMFs of the precipitation data showed a simple scaling property, and regional IDF curves derived from the scaling IDF and EEMD approach predict accurate storm intensities for rain-gauging sites at both the calibration and validation stages, but there could be errors associated with predicted storms of high return periods (100 year). [ABSTRACT FROM AUTHOR]

The use of jet grouting columns to increase the bearing capacity and reduce the settlements of foundations is dealt with, the aim being to formulate a complete design procedure where theoretical analyses and site observations are combined. The study is based on experimental results reported in the literature, which show that jet grouting columns are able to transfer high vertical loads to the surrounding soil but also that structural collapse may locally occur because of a sudden narrowing of their cross sections and/or poor soil cementation. Noticeable attention has been then given to these aspects by interpreting the results of full-scale tests specifically performed to investigate the characteristics of jet grouting columns and their mechanical interaction with the surrounding soil. The results of this analysis form the basis of a numerical method developed to simulate the response of vertically loaded rafts supported by arrays of columns. The nonlinear load-settlement response of axially loaded columns is modeled with the load-transfer curves method customized to account for the irregular shape of jet grouting columns. Extension of the analysis to the whole reinforced foundation is accomplished by considering the mutual interaction between the columns and the upper concrete raft. The variation of column properties is statistically evaluated from the results of field trials, simulated with probabilistic models and introduced into the calculation with a Monte Carlo simulation technique. The main advantages of the proposed method is that the design limit load of the foundation can be expressed in terms of acceptable probability of failure and that the uncertainty arising from the jet grouting process can be experimentally quantified and rationally introduced in the analysis. Benefits and limitations of reinforcement with jet grouting are finally discussed by applying the proposed method to a case study. [ABSTRACT FROM AUTHOR]

To advance the use of reliability-based design procedures, it is necessary to evaluate the sources of the design parameter uncertainty including inherent variability, measurement error, and transformation uncertainty. The results of a probabilistic evaluation of undrained footing bearing performance are discussed in the context of an extensive test site characterization described in a companion paper. Kriged cone tip resistance values are transformed into design parameters using a second-moment probabilistic approach and compared with the parameters obtained from the laboratory test analyses on specimens retrieved from the test site. The spatial, measurement, and transformation uncertainty are incorporated into probabilistic finite-element and bearing capacity analyses in which the results are compared against a full-scale load test performed at the test site. The results indicate that the reliable assessment of the spread footing response depends to a large degree on the assumed strength anisotropy and soil layering. A comparison of the probabilistic estimates of the bearing capacity to deterministic estimates developed from the correlations and average design parameters indicate that the consideration of uncertainty results in a desirable prediction accuracy. [ABSTRACT FROM AUTHOR]

The need for probabilistic slope analysis that takes into account the uncertainty of soil properties has been acknowledged by the geotechnical profession. Traditionally, probabilistic slope analysis involves only single-mode failure that is considered based on the critical slip surface. This may result in underestimating the failure probability. In contrast, system reliability analysis for slopes is deemed more rational. This study aims at improving the existing methods of slope reliability analysis by considering system reliability. A stratified response surface method (stratified RSM) is proposed to describe the performance functions of possible failure modes. The proposed method differs from conventional response-surface-based slope reliability analysis (which constructs a single approximate performance function) by generating a group of (stratified) response surfaces. Based on these stratified response surfaces, system reliability analysis can be efficiently carried out by means of either a first-order reliability method (FORM) or Monte Carlo simulations. The efficient FORM based on the concept of a dispersion ellipsoid in the space of the original variables is used. Application of the proposed approach to probabilistic assessment of slopes is illustrated by case studies, and the results obtained are compared with Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Through modern nondestructive inspection (NDI) techniques, fatigue cracks can be detected with varying accuracies in steel bridge structures, and consequently assessment and updating of fatigue reliability can be performed. A probabilistic fracture mechanics method based on the Bayes theorem using information from NDI has been demonstrated to update the relevant parameters of crack growth models effectively. Using the probabilistic characteristics of the NDI techniques, the probabilistic fatigue failure method, and the Bayes theorem, a procedure for assessing and updating the fatigue reliability of existing steel bridge components is presented. Data collected from two steel bridges, the Zhejiang Street Bridge and Ganjiang River Bridge, are used to illustrate the fatigue reliability assessment and updating method. In the case study, the suspender curves of the Zhejiang Street Bridge and Ganjiang River Bridge are determined after inspection and updating. It is found in this research that the fatigue reliability of the selected members in the existing steel bridges increases with no cracks detected, decreases with no size cracks detected, and changes remarkably with cracks detected. [ABSTRACT FROM AUTHOR]

Liquefaction potential of four Pleistocene beach sand deposits in the Greater Charleston area, South Carolina, is assessed. The assessment is based on a review of 51 sites of conspicuous craterlets and horizontal ground displacement that occurred in beach sand deposits during the 1886 Charleston earthquake and an analysis of 82 seismic cone penetration tests with pore-pressure measurements. Of the 51 ground failure sites, 23 are associated with the Ten Mile Hill beds; 13 with the Wando Formation; 13 with the Silver Bluff terrace and younger deposits that lie adjacent to the harbor, rivers, and creeks; and two with the Ladson Formation. Liquefaction potential is analyzed using the seismic cone data with and without correction for age-related processes (diagenesis) and then expressed in terms of the liquefaction potential index (LPI). Probability curves are developed from the LPI calculations for different earthquake ground-shaking parameters. The probability curves for the Wando Formation overpredict liquefaction potential when no corrections for diagenesis are made. When corrections for diagenesis are made, the probability curves for all four sands generally agree with the observed field behavior. [ABSTRACT FROM AUTHOR]

A probabilistic version of the Idriss and Boulanger standard penetration test (SPT)-based liquefaction triggering correlation is derived using a maximum likelihood approach and an updated case history database. Measurement and estimation uncertainties in the cyclic stress ratio (CSR) and SPT values and the effects of the choice-based sampling bias in the case history database are taken into account. The results of sensitivity analyses show that the position of the most likely triggering curve is well constrained by the case history data and that the magnitude of the total error term is also reasonably constrained. The most likely value for the SD of the error term in the triggering correlation is, however, found to be dependent on the uncertainties assigned to the CSR and . The results of the sensitivity study appear to provide reasonable bounds on the effects of different interpretations on the positions of the triggering curves for various probabilities of liquefaction. Methods for including model and parameter uncertainties in probabilistic liquefaction analyses are briefly discussed. The derived triggering correlation is compared with relationships developed from cyclic laboratory test results for specimens obtained using frozen sampling techniques. [ABSTRACT FROM AUTHOR]

Acceptance procedures are usually defined in terms of the fraction of elements that fall outside certain specification limits, such as the data dispersion around a central tendency. In contrast, design methods usually consider measures of a central tendency or location (i.e., mean) or, occasionally, of a central tendency together with measures of data dispersion. Price adjustments can be derived from data analysis using empirical models or algorithms based on life cycle cost analysis. As a consequence, there is no common logical framework for designing, analyzing, testing, and accepting road and airport pavements. This study analyzes the dependence of pay adjustment on hot mix asphalt air void characteristics using several acceptance procedures. The results demonstrate that the transition from location-based to dispersion-based systems depends on the probability density function describing a given quality characteristic. The drawbacks of several methods, brought to light in the analysis and discussion presented here, informed a new model in which both location and dispersion indicators are considered. The model was applied to a case study, and practical applications are outlined. [ABSTRACT FROM AUTHOR]

Simulation-based design and analysis of biotreatment systems depends to a great extent on the accuracy of the model parameters used. In many cases, site-specific parameter estimates are not available and parameter values recommended in the literature are adopted along with a measure of judgment to account for bioprocess uncertainty. To better quantify process certitude associated with this approach and thus provide guidance to process designers lacking site-specific parameter values, stochastic simulations were conducted and the empirical frequency distributions derived from the simulation results were interpreted in terms of process certitude. In general, the simulation results confirmed field experience and current practice in biotreatment from a probabilistic standpoint and provided context in terms of process certitude. The certitude of achieving specific treatment levels increased with sludge age, validating the application of a design safety factor on the minimum solids retention time. The increase in process certitude was not substantial, however, beyond a sludge age of about 4 days for the removal of organic material or about 10 days for the removal of ammonia. Process certitude was also declined when the hydraulic behavior of the treatment system approached complete mixing, which could result from such operational strategies as increasing the recycle ratio or the utilization of step feed. [ABSTRACT FROM AUTHOR]

A relatively simple technique is presented for estimating the first-passage probability of any stationary Gaussian process. In particular, the technique is shown to give meaningful results for the troublesome case of a process with a bimodal spectral density that cannot be truly classified as either narrowband or broadband. Mathematically, the technique is only a seemingly minor revision of a long-known method for narrowband processes. What is new here is the demonstration of its applicability not only to narrowband processes but also to other spectral densities, including challenging bimodal situations. The fundamental idea is that of a Markovian point process in which the probability of the process never having crossed a given level u is approximated as the probability that all members of the set of amplitude (or envelope) values at a discrete set of time values are below u, along with the assumption that the set of amplitude values has Markovian conditioning. The critical parameter that must be determined is a correlation coefficient that appears in the jointly Rayleigh distribution of the amplitude at two time values. A method involving simple numerical integration (or a series solution) is shown to give an appropriate value for this parameter for a given spectral density and a given value of u. The model is shown to give generally good agreement with simulation results for processes in which the commonly used Vanmarcke approximation is inadequate. [ABSTRACT FROM AUTHOR]

This paper presents a structural health monitoring methodology that uses acoustic emission (AE) features to predict crack growth in structural elements subjected to fatigue. This allows for the prediction of the failure of the structural element at the current load level. The methodology uses Bayesian inference to account for different sources of uncertainty such as uncertainty in the data (AE signal), unknown fracture mechanics parameters, and model inadequacy. The methodology is divided into two main components: a model updating component that uses available data to build a joint probability distribution of the different unknown fracture mechanics parameters, and a prognosis component in which this multivariable probability distribution is sampled to predict the stress intensity factor range at a future number of cycles. The application of the methodology does not require knowledge of the load amplitude nor the initial crack length. The methodology is validated using experimental data from a compact test specimen under cyclic loading. [ABSTRACT FROM AUTHOR]

Inelastic mechanisms leading to failure in interior reinforced concrete beam-column (RCBC) connections, designed on the concept of strong column-weak beam philosophy, primarily result from failure of the joint region and yielding of longitudinal reinforcement in beams. In this manuscript, two novel easy-to-use probabilistic methodologies have been developed that can determine with sufficient accuracy the occurrence of either of these inelastic mechanisms leading to failure, given the geometric, material and loading parameters of an experimental investigation. One model was developed by using the relevance vector machine method, a machine learning methodology that uses a Bayesian formulation and results in a sparse representation. Another model was binomial logistic regression, which can relate the qualitative event of inelastic mechanism resulting in failure initiation with several experimentally obtained independent parameters. It can also quantify the relative importance of each of these independent parameters. Both methods show good predictive efficiency and can be utilized by a designer, engineer, or researcher to obtain a preliminary probabilistic estimate of inelastic mechanisms that lead to failure of interior RCBC connections. This manuscript also presents comparative evaluations of utilizing these two models. [ABSTRACT FROM AUTHOR]

This paper demonstrates the size effect on the shear strength of reinforced concrete (RC) beams with stirrups and does so in two separate and independent ways: (1) by fracture mechanics, based on finite-element analysis calibrated by a large beam test; and (2) by purely statistical analysis in which a newly assembled database of 234 tests is filtered to eliminate spurious size effects caused by nonuniformity of secondary influencing parameters. Both ways show that stirrups, whether minimum or heavier, cannot suppress the size effect completely, although they can mitigate it significantly for beam depth d<1 m (39.4 in.). The effect of stirrups is to push the size effect curve in logarithmic scale into sizes larger by about one order of magnitude. For beam depths d<0.5 m, 1, 2, and 6 m (19.7, 39.4, 78.7, and 236.2 in.), the percentages of beams whose shear strength is below the code limit are calculated as 3.5, 6.5, 15.7, and 55.1%, respectively. The corresponding failure probabilities are 10-6, 10-5, 10-4, and 10-3, whereas 10-6 is the generally accepted standard for a tolerable maximum in risk analysis. It follows that, for beams with stirrups having depth >1 m (39.4 in.), the size effect cannot be neglected. [ABSTRACT FROM AUTHOR]

Drift seriously increases the destructive power of a flood event. Drift accumulations and blockages at river bridges are a widespread problem, possibly leading to their total destruction. Although drift is a major threat, limited knowledge is currently available on the likelihood of drift blocking. Drift either accumulates at a single pier, or it spans between two or more piers, or it gets blocked at the bridge deck. The main purpose of this experimental study is to analyze the drift-blocking probability at bridge decks depending on: (1) drift dimensions, (2) freeboard, (3) flow characteristics, and (4) bridge characteristics. Systematic model tests include the accumulation of both single logs and rootstocks. The test flow conditions represent a major flood event, where the freeboard tends to zero and the drift is able to touch the bridge deck. The results indicate significant effects of the freeboard, the approach flow Froude number, and the bridge characteristics on drift accumulation. They allow for an estimation of the blocking probability and therefore can be used as a risk assessment tool to identify endangered bridges prior to a flood event. The model tests demonstrate further the randomness of the blocking process, resulting occasionally in a wide scatter of data. [ABSTRACT FROM AUTHOR]

A model using lifetime functions is used to evaluate the probability of survival of bridge components. The possible outcomes associated with nondestructive inspections (NDIs) are incorporated in an event-tree model. Each time a bridge component is inspected, different decisions can be made. The use of a lifetime function for each component of the structural system enables one to express the probability that the component survives. In theory (i.e., perfect inspection), each NDI should be associated with two possible outcomes: survival or failure. In the first case, no damage is detected and the probability density function of time to failure is updated knowing that the component has survived until the inspection. In the second case, damage is detected and maintenance action is planned. In practice, NDIs are subjected to uncertainties (i.e., imperfect inspections) and detecting or not detecting damage depends on the inspection quality (i.e., probability of detection). For poor-quality inspections, there is a significant risk to overestimate the probability of safe performance. The aim of this paper is to provide a practical methodology for determining optimal NDI strategies for different components of steel bridges. The different types of inspections considered in this paper are visual, magnetic particle, and ultrasonic. An economic analysis is performed and NDI strategies are optimized by simultaneously minimizing both the expected inspection/maintenance cost (i.e., the sum of inspection and maintenance costs) and the expected failure cost. The proposed approach is applied to an existing steel bridge. [ABSTRACT FROM AUTHOR]

The Bernoulli pulse process has been used in the past for modeling snow loads. However, it is not an appropriate model for heavy snow-load areas as the snow accumulation cannot be simulated, which may lead to an underconservative assessment of buildings in such areas. In this study, a filtered Poisson process (FPP) is investigated and demonstrated to be an effective stochastic model capable of simulating snow loads with or without accumulation. Weather records obtained from the National Climatic Data Center are used to calibrate the simulated ground snow-load records using the FPP model. A genetic algorithm is employed to determine the parameters of the FPP model. Illustrated by three selected sites in the United States, the annual maximum and daily ground snow-load characteristics are well captured by the FPP model. Potential applications of the model in reliability analysis and risk assessment are discussed. [ABSTRACT FROM AUTHOR]

This paper presents the formulation of a time-dependent seismic fragility format for bridges, as well as new insights into the potential effects of aging and deterioration on seismic vulnerability traditionally neglected in fragility modeling, including joint impacts of multiple component deterioration not investigated to date. The study evaluates the impact of lifetime exposure to chlorides from deicing salts on the seismic performance of multispan continuous highway bridges, considering corrosion of reinforced concrete columns and steel bridge bearings. The components' degradation and their influence on seismic response are illustrated through three-dimensional nonlinear dynamic analysis. A full probabilistic analysis accounting for variation in bridge, ground motion, and corrosion parameters is conducted to develop time-dependent seismic fragility curves. These fragility curves indicate the evolving potential for component and system damage under seismic loading considering time-dependent corrosion-induced deterioration. The results indicate that while corrosion may actually decrease the seismic vulnerability of some components, most critical components suffer an increase in vulnerability. Quadratic models depicting the change in lognormal seismic fragility parameters are proposed to capture the time-dependent effect of aging on the fragility of the bridge system. Overall, the seismic vulnerability significantly increases throughout the lifetime of the representative bridge geometry, with a 32% shift in the median value of complete damage fragility near the end of the bridge's life. [ABSTRACT FROM AUTHOR]

In this paper, the mean-centered first-order second-moment (FOSM) method is employed to perform probabilistic push-over analysis (POA) of structural and/or soil-structure systems. Approximations of first and second statistical moments (FSSMs) of engineering demand parameters (EDPs) of structural and/or geotechnical systems with random material parameters are computed based on finite-element (FE) response and response sensitivity analysis (RSA) results. The FE RSA is performed accurately and efficiently by using the direct differentiation method (DDM) and is employed to evaluate the relative importance (RI) of the various modeling material parameters in influencing the variability of the EDPs. The proposed approximate methodology is illustrated through probabilistic POA results for nonlinear inelastic FE models of: (1) a three-story reinforced-concrete (RC) frame building and (2) a soil-foundation-structure interaction system consisting of a RC frame structure founded on layered soil. FSSMs of EDPs computed through the FOSM method are compared with the corresponding accurate estimates obtained via Monte Carlo simulation. Results obtained from 'exact' (or 'local') and 'averaged' (or 'global') response sensitivities are also compared. The RI of the material parameters describing the systems is studied in both the deterministic and probabilistic sense, and presented in the form of tornado diagrams. Effects of statistical correlation between material parameters are also considered and analyzed by the FOSM method. A simple approximation of the probability density function and cumulative distribution function of EDPs due to a single random parameter at a time (while all the other parameters are fixed to their mean values) is also proposed. Conclusions are drawn on both the appropriateness of using local RSA for simplified probabilistic POA and on the application limits of the FOSM method. It is observed that the FOSM method combined with the DDM provides accurate estimates of FSSMs of EDPs for low-to-moderate level of inelastic structural or system behavior and useful qualitative information on the RI ranking of material parameters on the structural or system response for high level of inelastic behavior. [ABSTRACT FROM AUTHOR]

Systematic transient analysis in water supply systems is intended to determine the reliability of water supply and the integrity of hydraulic devices when hydraulic transients (i.e., water hammers) occur. In the analysis, parameters such as wavespeed, pipe diameter, friction, and others, usually considered as known inputs in traditional water hammer models, are actually subject to uncertainties due to natural or human behaviors. The paper considers these uncertainties and investigates their effects on the reliability of water transportation systems and water supply networks under transient conditions with or without protection devices. In addition, the optimization of transient protection devices is also evaluated based on the probabilistic analysis. The results show that the probabilistic analysis method could inspect the influence of the different input uncertainties on the system responses under transient conditions and provide detailed information on the design for water supply systems and protection devices. A one-dimensional water hammer model of pipe fluid systems and Monte Carlo simulation method are used in this investigation. [ABSTRACT FROM AUTHOR]

The presence of air voids, moisture, and chlorides inside tendon systems on segmental posttensioned (PT) bridges has been cited as a reason for the early age corrosion and failure of strands in these bridges. This paper develops probabilistic models to predict the time-variant tension capacity of PT strands exposed to wet-dry conditions. A total of 384 unstressed and 162 stressed strand test specimens were exposed to various void, moisture, and chloride conditions for 0, 12, 16, and 21 months; the residual tension capacities of the strands were then determined. Using these experimental data, a Bayesian approach is used to develop probabilistic capacity models for unstressed and stressed strands. The tension capacities of stressed strands under potential void, wet-dry, and chloride conditions in the field are predicted using the developed models. Probabilistic time-variant models are formulated in such a way that they can be updated by other researchers using additional information from the testing of unstressed strands only, avoiding expensive and cumbersome testing of stressed strands. The mean absolute percentage errors of these models are less than 3.2%, indicating good overall model accuracy. [ABSTRACT FROM AUTHOR]

One of the greatest challenges in the United States today is overcoming the deterioration of an aging infrastructure system. In particular, the highway/bridge infrastructure system has traditionally been neglected of maintenance and repair. One of the most concerning degrading mechanisms in the bridge infrastructure is corrosion, and it is compounded by the fact that much of the older infrastructure is not consistent with current seismic code requirements. The research presented here determines the effects of corrosion on the seismic response of a typical reinforced concrete bridge designed according to recent standards, predominantly governed by flexural deformations, and based on realistic lifetime deteriorations in strength due to the reduction in cross-sectional area of the reinforcement and in stiffness due to concrete cover spalling. The findings of the research, given the noted modeling assumptions, are that the losses in strength and stiffness only marginally influence the seismic fragility of the selected bridge. [ABSTRACT FROM AUTHOR]

Flutter-critical velocity is usually estimated from deterministic analyses by assuming that physical and geometrical parameters are perfectly known. However, aleatory and epistemic uncertainties, especially those associated with the definition of material properties, are intrinsically present partly because of the random nature of every physical system and partly because those properties are evaluated from a finite number of observations. The paper focuses on the combination of structural reliability analysis with aeroelastic simulation to give a correct flutter speed evaluation for design purposes. Two typical aeronautical test cases have been considered: (1) an isotropic material structure and (2) a composite material structure. Different computational methodologies (classical and developed by authors) have been coupled with a simple two-dimensional aeroelastic model to study the qualitative consequences of uncertainty in determination of critical flutter speed and to provide a comparison between different reliability methods such as Monte Carlo (MC) simulation, first-order reliability method (FORM), second-order reliability method (SORM), and response surface by fixed or adaptive order regressor coupled with FORM and MC. For the isotropic case best prediction of MC results is always obtained with high-order response surface (HORS) (coupled with both MC and FORM) above all for low levels of coefficient of variation (C.V.) (10% or under) for which results are basically equivalent. FORM and SORM give acceptable results only above cumulative distribution function values of 35-40%. Only response surface with a fixed polynomial order always gives totally misrepresentative results even for very low variability of basic physical quantities. For the composite case, no generic conclusion can be made about the influence of physical quantities on uncertainty propagation because it strongly depends on layup configuration. From a design point of view, variability in composite materials is even more important because, in a general sense, more variables are involved. The flutter speed reductions reveal greater slope of the linear regressors with respect to the isotropic counterpart, with stronger differences when the uncertainty afflicting the basic random variables is higher. [ABSTRACT FROM AUTHOR]

The low-bid method, typically used for competitive bidding in the United States, may result in a contract with a firm that submits either accidentally or deliberately an unrealistically low-bid price. Such an occurrence hurts both the owner and the contractor by promoting disputes, increased costs, and schedule delays. To address this problem, other countries have adopted bidding methods based on the average of the bids submitted. One such approach is the below-average method where the winning bid is closest to but below the average of all bids. A competitive bidding model for the below-average-bid method is presented and its merits relative to the average-bid method and the low-bid method are explored. The below-average-bid process is investigated analytically and through Monte Carlo simulation. The results of bidding models for the below-average, the average, and the low-bid methods are presented in four easy-to-use nomograms which allow contractors to determine the optimal lump-sum bid price for each method without the need for complicated analysis. A comparison of the three methods provides information and insights to help owners with the difficult choice of a suitable bidding method for the project at hand. [ABSTRACT FROM AUTHOR]

A probabilistic hydrodynamic model for the wet and dry zone on a permeable structure is developed to predict irregular wave action on the structure above the still water level. The model is based on the time-averaged continuity and momentum equations for nonlinear shallow-water waves coupled with the exponential probability distribution of the water depth. The model predicts the cross-shore variations of the mean and standard deviation of the water depth and horizontal velocity. The model is compared with four test series in which measurement was made of the wave overtopping rate and probability as well as the water depth, velocity, and discharge exceeded by 2% of incident 1,000 waves. The agreement is mostly within a factor of 2. Damage progression of a stone armor layer is predicted by modifying a formula for bed load on beaches with input from the hydrodynamic model. The damage progression model is compared with three tests that lasted up to 28.5 h. The model predicts the eroded area of the damaged armor layer well but overpredicts the deposited area because it does not account for discrete stone units deposited at a distance seaward of the toe of the damaged armor layer. [ABSTRACT FROM AUTHOR]

In this paper swarm intelligence based methodology is proposed for optimal and reliable design of irrigation channels. The input parameters involved in channel design are prone to uncertainty and the solution of deterministic model may result in flooding risk and affect the stability of the channel. To provide reliability in the design, an overtopping probability constrained design is presented in this study. The deterministic equivalent of the probabilistic constraint is derived by following the principle of first order uncertainty analysis. In order to account for the uncertainty of design parameters in the objective function, a modified cost function is proposed. A methodology is propounded to solve it in a metaheuristic environment and solved it using elitist-mutated particle swarm optimization (EMPSO) method. The EMPSO based solutions are found to be quite successful and better than the classical optimization methods. Finally, it is concluded that the proposed methodology has a good potential for reliable design of composite channels for designer specified reliability values. [ABSTRACT FROM AUTHOR]

Rainfall-induced up-slope failure has been a major issue for the mountain road mitigation. To determine the mitigation priority of the roadside slopes, a model to evaluate the stability of the up-slopes is proposed in this paper. This model is developed using Bayesian classification theorem, historical rainfall and landslide data, and an artificial intelligence skill—Gaussian process. The parameters needed for this model include the up-slope features, catchment area, lithology, vegetation coverage, and rainfall data. Among these parameters, rainfall is considered to be the triggering factor of landslide in this study. In addition, each slope has also demonstrated a unique failure probability relationship with the 72-h accumulated rainfalls. Such a relationship is adopted to define a threshold rainfall factor which is used as an index to estimate the likelihood of landslide occurrence under a rainfall event. Using the threshold rainfall factor of each slope, a priority list is proposed for the mitigation of roadside up-slopes along the Alishan mountain road in Taiwan. [ABSTRACT FROM AUTHOR]

This paper attempts to provide insight into the sensitivity of the collapse capacity of moment-resisting frame and shear wall structures to variation in basic structural parameters, and the choice of an appropriate ground motion intensity measure, based on probabilistic estimation of the collapse capacity of a structural system. The effects of fundamental period and base shear strength and of deformation and deterioration properties of structural components on the collapse capacity of frame and wall structures are quantified. It is shown that the collapse potential of moment-resisting frames is highly sensitive to the ratio of column to beam strength; increasing this parameter from 1.2 to 2.4 will increase the median of collapse capacity by up to 90%. Using a scalar ground motion intensity measure for estimating the collapse capacity can lead to underestimation of median collapse capacity by up to 50%, compared to using a vector-valued intensity measure. The provided information can be used to assist in the selection of a suitable structural system and associated parameters in design for collapse safety. Closed-form solutions are formulated using a database of collapse fragility curves developed for the sensitivity study. Application of these closed-form solutions for design decision making is illustrated through a comprehensive example. [ABSTRACT FROM AUTHOR]

In this paper, a numerical procedure for probabilistic slope stability analysis is presented. This procedure extends the traditional limit equilibrium method of slices to a probabilistic approach that accounts for the uncertainties and spatial variation of the soil strength parameters. In this study, two-dimensional random fields were generated based on a Karhunen-Loève expansion in a fashion consistent with a specified marginal distribution function and an autocorrelation function. A Monte Carlo simulation was then used to determine the statistical response based on the generated random fields. This approach makes no assumption about the critical failure surface. Rather, the critical failure surface corresponding to the input random fields of soil properties is searched during the process of analysis. A series of analyses was performed to verify the application potential of the proposed method and to study the effects of uncertainty due to the spatial heterogeneity on the stability of slope. The results show that the proposed method can efficiently consider the various failure mechanisms caused by the spatial variability of soil property in the probabilistic slope stability assessment. [ABSTRACT FROM AUTHOR]

Probabilistic assessments of the seismic ductility demand for hysteretic bilinear single-degree-of-freedom systems have been reported in the literature. However, a systematic assessment of possible differences in the estimated ductility demand for different earthquake types using recorded ground motions is not available, although ground motion prediction equations for different earthquake types are developed. The assessment of the differences can be important for estimating structural reliability and expected damage cost under seismic excitations since partial damage and collapse could be related to the ductility demand. Therefore, if the differences are significant one must use consistent sets of ground motion prediction equation and ductility demand relation for each earthquake type affecting a site of interest to evaluate the seismic hazard and risk. To assess the differences of the ductility demand, 413 records for Mexican interplate earthquakes, 275 records for Mexican inslab earthquakes, and 592 records for California earthquakes are employed. The evaluation considers ranges of values of natural vibration periods and ratios of initial to postyield stiffness. The obtained results indicate that the statistics of displacement ductility demand differs for different earthquake types. The results are used to develop empirical relations for predicting the expected displacement ductility demand. [ABSTRACT FROM AUTHOR]

Storm-water pipe networks in Australia are designed to convey water from rainfall and surface runoff. They do not transport sewerage. Their structural deterioration is progressive with aging and will eventually cause pipe collapse with consequences of service interruption. Predicting structural condition of pipes provides vital information for asset management to prevent unexpected failures and to extend service life. This study focused on predicting the structural condition of storm-water pipes with two objectives. The first objective is the prediction of structural condition changes of the whole network of storm-water pipes by a Markov model at different times during their service life. This information can be used for planning annual budget and estimating the useful life of pipe assets. The second objective is the prediction of structural condition of any particular pipe by a neural network model. This knowledge is valuable in identifying pipes that are in poor condition for repair actions. A case study with closed circuit television inspection snapshot data was used to demonstrate the applicability of these two models. [ABSTRACT FROM AUTHOR]

Measurement of the compressive strength of parallel strand lumber (PSL) is conducted on specimens of varying size with nominally identical mesostructure. The mean of the compressive strength is found to vary inversely with the specimen size, and the coefficient of variation of the strength is found to decrease with increasing specimen size, and to be smaller than the coefficient of variation of strength for solid lumber. The correlation length of the compressive strength is approximately 0.5 m, and this correlation length leads to significant specimen-to-specimen variation in mean strength. A computational model is developed that includes the following properties of the PSL mesostructure: the strand length, the grain angle, the elastic constants, and the parameters of the Tsai-Hill failure surface. The computational model predicts the mean strength and coefficient of variation reasonably well, and predicts the correct form of correlation decay, but overpredicts the correlation length for compressive strength, likely because of sensitivity to the distribution of strand length used in the model. The estimates of the statistics of the PSL compressive strength are useful for reliability analysis of PSL structures, and the computational model, although still in need of further development, can be used in evaluating the effect of mesostructural parameters on PSL compressive strength. [ABSTRACT FROM AUTHOR]