Monte Carlo Simulation Framework Research Articles

Unveiling the nonlinear drivers of urban land resources on carbon emissions: The mediating role of industrial upgrading and technological innovation

As cities continue to develop, land resources play a pivotal role in the pursuit of sustainable urban development. However, the impact and mechanisms of land resources on reducing emissions remain unclear. Therefore, our study employs an ensemble learning model within a Monte Carlo Simulation framework to explore the nonlinear relationship between land resources and carbon emissions. Additionally, we utilize a fixed effects model to identify the mechanisms of impact. Specifically, industrial and residential land uses are identified as primary drivers of carbon emissions, contributing 17.04 % and 4.75 % of the total variance in emissions, respectively. In contrast, a standard deviation increase in land price leads to a reduction of 4.65 tons in per capita carbon emissions. In addition, industrial upgrading and innovation emerge as critical mediators in promoting carbon reduction. This research contributes to sustainable urban planning and policy-making, offering insights into how strategic land use planning reduces urban carbon footprints.

RayStation/GATE Monte Carlo simulation framework for verification of proton therapy based on the 12N imaging

Objective.12N, having a half-life of 11 ms, is a highly effective positron emitter that can potentially provide near real-time feedback in proton therapy. There is currently no framework for comparing and validating positron emission imaging of12N. This work describes the development and validation of a Monte Carlo (MC) framework to calculate the images of12N, as well as long-lived isotopes, originating from activation by protons.Approach. The available dual-panel Biograph mCT PET scanner was modeled in GATE and validated by comparing the simulated sensitivity map with the measured one. The distributions of12N and long-lived isotopes were calculated by RayStation and used as the input of GATE simulations. The RayStation/GATE combination was verified using proton beam irradiations of homogeneous phantoms. A 120 MeV pulsed pencil beam with 108protons per pulse was used. Two-dimensional images were created from the GATE output and compared with the images based on the measurements and the 1D longitudinal projection of the full 2D image was used to calculate the12N activity range.Main results. The simulated sensitivity in the center of the FoV (5.44%) agrees well with the measured one (5.41%). The simulated and measured 2D sensitivity maps agree in good detail. The relative difference between the measured and simulated positron activity range for both12N and long-lived isotopes is less than 1%. The broadening of the12N images relative to those of the longer-lived isotopes can be understood in terms of the large positron range of12N.Significance. We developed and validated a MC framework based on RayStation/GATE to support the in-beam PET method for quality assurance of proton therapy. The inclusion of the very short-lived isotope12N makes the framework useful for developing near real-time verification. This represents a significant step towards translating12N real-time in vivo verification to the clinic.

An improved method for statistics estimation of out-of-plane load resistance of masonry walls using design-code models and mechanics-based finite element models

The susceptibility of masonry walls to out-of-plane failure under seismic loading presents a significant engineering concern. In adherence to contemporary limit state design philosophy and as mandated by performance-based design guidelines, it is crucial to accurately estimate statistics parameters such as the mean and variance of out-of-plane resistance. To achieve this, two deterministic models are often considered within the Monte Carlo simulation framework: design-code models, known for their computational efficiency but potential for significant model error, and mechanics-based finite element models, noted for their accuracy but computational intensity. Relying solely on either model for statistics estimation presents challenges due to their respective inherent limitations. This study introduces an improved strategy that synergizes the strengths of both models, resulting in enhanced statistics estimators for the out-of-plane resistance of masonry walls. The proposed methodology involves the use of the control variate method by integrating numerous design-code model evaluations to boost computational efficiency while incorporating a limited number of finite element model evaluations to ensure accuracy. The constructions of the proposed estimators follow rigorous optimization procedures to minimize associated errors and variances. Theoretical derivations are provided for essential elements in the estimator constructions, such as model evaluation numbers for finite element and design-code models and control variate coefficients. Moreover, the accuracy of the proposed estimators is compared with that of the crude Monte Carlo estimator. A key benefit of the proposed estimators is their unbiased nature, and their accuracy is not compromised by the discrepancies between the finite element model and the design-code model. To demonstrate the practicality of these estimators, two case studies are illustrated: one focusing on unreinforced masonry walls and the other on reinforced masonry walls. The results indicate that design-code model-based estimators exhibit large bias, and compared to the estimators that solely rely on the finite element model, the proposed estimators achieve higher accuracy given the same computational budget.

Cost-effectiveness Analysis of Colorectal Cancer Screening Strategies Using Active Learning and Monte Carlo Simulation.

Detection of colorectal cancer (CRC) in the early stages through available screening tests increases the patient's survival chances. Multimodal screening policies can benefit patients by providing more diverse screening options and balancing the risks and benefits of screening tests. We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies. We developed a Monte Carlo simulation framework to model CRC dynamics. We proposed an innovative calibration process using machine learning models to estimate age- and size-specific adenomatous polyps' progression and regression rates. The proposed approach significantly expedites the model parameter space search. Two multimodal proposed policies (i.e., 1] colonoscopy at 50 y and fecal occult blood test annually between 60 and 75 y and 2] colonoscopy at 50 and 60 y and fecal immunochemical test annually between 70 and 75 y) are identified as efficient frontier policies. Both policies are cost-effective at a willingness to pay of $50,000. Sensitivity analyses were performed to assess the sensitivity of results to a change in screening test costs as well as adherence behavior. The sensitivity analysis results suggest that the proposed policies are mostly robust to the considered changes in screening test costs, as there is a significant overlap between the efficient frontier policies of the baseline and the sensitivity analysis cases. However, the efficient frontier policies were more sensitive to changes in adherence behavior. Generally, combining stool-based tests with visual tests will benefit patients with higher life expectancy and a lower expected cost compared with unimodal screening policies. Colonoscopy at younger ages (when the colonoscopy complication risk is lower) and stool-based tests at older ages are shown to be more effective. We propose a detailed Markov model to capture the colorectal cancer (CRC) dynamics. The proposed Markov model presents the detailed dynamics of adenomas progression to CRC.We use more than 44,000 colonoscopy reports and available data in the literature to calibrate the proposed Markov model using an innovative approach that leverages machine learning models to expedite the calibration process.We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies and compare their performances with the current in-practice policies.

Reliability framework for CLT floors in out-of-plane bending using Monte Carlo simulation

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index (β) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.

Decoupling the multi-drivers of urban extreme heat environment in urban agglomerations using ensemble learning

Urban extreme heat (UEH) has been imparting a formidable menace to various facets of sustainable urban and regional development. While increasing studies have examined the urban thermal environment, a gap exists in decoupling the nonlinear multi-drivers of UEH environment in intra-urban regions, especially across the urban agglomerations. In this study, we estimated UEH through the land surface temperature (LST) in the extended summer season exceeding a specific threshold computed through cross-regional statistics. Furthermore, an additive interpretable ensemble learning model enhanced via the Bayesian Optimization algorithm (BO) and Monte Carlo Simulation framework (MCS) was employed to dissect the intricate nonlinear interplay between UEH and intra-urban morphology, green-blue composition, and atmospheric determinants. Our results highlight the indispensably suppressive effect of green and blue factors on UEH, whereas urban morphological variables generally exhibit an opposite trend. Interestingly, we reveal heterogeneity responses of building heights, elevated urban structures mitigate UEH in temperate monsoon and subtropical monsoon zones and exhibit diminished marginal utility in temperate continental zones. Furthermore, the arid climatic characteristics are expected to exert an unexpectedly enhancing effect on water bodies' cooling capacity. These findings provide heterogeneous local guidance for landscape and urban planning towards the severe challenges of climate change.

Quantifying the Impact of Sea Level on Coastal Cities using Bayesian optimized Monte Carlo Simulation model

The immediate treatment of the increasing of sea levels represent on coastal cities around the globe is the subject of this research. Using an all-encompassing and probabilistic methodology, the study quantifies the complex effects of rising sea levels using a Bayesian-optimized Monte Carlo simulation framework. The current approaches for assessing the effects of rising sea levels frequently do not adequately capture the intrinsic complexity and unpredictability of changing structures. This work presents a novel method combining Monte Carlo simulation and Bayesian optimization to overcome these drawbacks. The present research is innovative since it incorporates Bayesian optimization methods into a Monte Carlo simulation framework. By constantly modifying distributions of probabilities according to observable results, this combination improves simulation performance. This constantly changing optimization methodology closes a significant gap within current methods by ensuring a more accurate portrayal of shifting conditions. In addition, the method used in this work emphasizes cooperation among mathematicians, coastal scientists, and climate researchers, thereby promoting a comprehensive knowledge of the complex issues raised by rising sea levels. This set of variables includes the current level of the sea, land height, frequency of storm surges, population density, and infrastructure resilience. The model’s effectiveness is rigorously assessed, utilizing procedures for verification and calibration that use past information. Calculations are guided by various rising sea-level situations based on scientific forecasts and uncertainty ranges. Sensitivity analysis pinpoints important factors, guiding further efforts to gather data and improve the model. The structure for the performance assessment ensures that the model is dependable and applicable to those making decisions who want to prioritize flexible measures and create resilient communities along the coast.

Cellular automata-based simulators for the design of prescribed fire plans: the case study of Liguria, Italy

Abstract Background Socio-economic changes in recent decades have resulted in an accumulation of fuel within Mediterranean forests, creating conditions conducive to potential catastrophic wildfires intensified by climate change. Consequently, several wildfire management systems have integrated prescribed fires as a proactive strategy for land management and wildfire risk reduction. The preparation of prescribed fires involves meticulous planning, entailing the identification of specific objectives, verification of prescriptions, and the definition of various scenarios. During the planning phase, simulation models offer a valuable decision-support tool for the qualitative and quantitative assessment of different scenarios. In this study, we harnessed the capabilities of the well-established wildfire simulation tool , to identify areas where prescribed fires can be performed, optimizing the wildfire risk mitigation and the costs. We selected a case study in the Liguria region, Italy, where the model is utilized operationally by the regional wildfire risk management system in emergency situations. Results Initially, we employed the propagation model to simulate a historical wildfire event, showcasing its potential as an emergency response tool. We focused on the most significant fire incident that occurred in the Liguria region in 2022. Subsequently, we employed to identify optimal areas for prescribed fires with the dual objectives of maximizing the mitigation of wildfire risk and minimizing treatment costs. The delineation of potential areas for prescribed fires has been established in accordance with regional regulations and expert-based insights. The methodology put forth in this study is capable of discerning the most suitable areas for the implementation of prescribed burns from a preselected set. A Monte Carlo simulation framework was employed to evaluate the efficacy of prescribed burns in mitigating the spread of wildfires. This assessment accounted for a variety of conditions, including fuel loads, ignition points, and meteorological patterns. The model was utilized to simulate the progression of wildfire spread. Conclusions This study underscores the utility of in offering both quantitative and qualitative insights that can inform prescribed fire planning. Our methodology has been designed to involve active engagement with subject matter experts throughout the process, to develop scenarios grounded in their expert opinions. The ability to assess diverse scenarios and acquire quantitative information empowers decision-makers to make informed choices, thereby advancing safer and more efficient fire management practices.

A Monte Carlo simulation of tracer diffusion in amorphous polymers.

Tracer diffusion in amorphous polymers is a sought-after quantity for a range of technological applications. In this regard, a quantitative description of the so-called decoupling from the reverse proportionality between viscosity and diffusion coefficient into a fractional one remains a challenge requiring a deeper insight. This work employs a Monte Carlo simulation framework in 3 dimensions to investigate the consequences of different scenarios for estimating this fractional exponent on the diffusion coefficient of tracers in polymers near glass transition. To this end, we adopted a continuous-time random walk model for tracer diffusion in the supercooled liquid state. The waiting time distribution of the diffusants was computed based on the rotational correlation times of the polymer. This proposed procedure is of particular interest because it brings the quantity of waiting time (and its statistics) in connection with the measurable observable of rotational times. In the framework of our simulations the aforementioned fractional exponent appears in the relation between the diffusant's waiting time and the rotational time of the diffusion medium. A limited comparison with experimental diffusivities from the literature revealed a reasonable agreement with a fractional exponent on the basis of the molar volumes of the diffusant and the monomeric unit. Finally, an analysis of time-averaged mean squared displacement pointed to normal Brownian dynamics for tracer diffusion in polymers above the glass transition temperature.

A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study

This study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective in addressing computational challenges associated with a large number of simulations. Support Vector Machine (SVM), as an emerging machine learning method suitable for small-sample scenarios, offers a well-established theoretical foundation and presents an effective model substitution approach for reliability analysis in engineering structures. However, the existing literature lacks a comprehensive and thorough comparative analysis of SVM's hybrid adaptive modeling approach, encompassing initial sampling methods and learning functions, with regards to both computational efficiency and accuracy. Additionally, there is a gap in adaptive modeling methods capable of accommodating diverse types of input uncertainty, the nonlinearity of limit state functions, and various application scenarios. In response to these gaps, this article introduces the ASVM-MCS framework, which addresses these challenges by considering different types of input variables and various failure modes. Moreover, this study provides a comprehensive evaluation of the ASVM-MCS framework's performance, including its initial sampling methods and learning functions, across a range of application scenarios, such as scenarios involving only random variables, mixed variables, and the reliability of series-parallel systems.

Economic performance evaluation of flexible centralised and decentralised blue hydrogen production systems design under uncertainty

Blue hydrogen is viewed as an important energy vector in a decarbonised global economy, but its large-scale and capital-intensive production displays economic performance vulnerabities in the face of increased market and regulatory uncertainty. This study analyses flexible (modular) blue hydrogen production plant designs and evaluates their effectiveness to enhance economic performance under uncertainty. The novelty of this work lies in the development of a comprehensive techno-economic evaluation framework that considers flexible centralised and decentralised blue hydrogen plant design alternatives in the presence of irreducible uncertainty, whilst explicitly considering the time value of money, economies of scale and learning effects. A case study of centralised and decentralised blue hydrogen production for the transport sector in the San Francisco area is developed to highlight the underlying value of flexibility. The proposed methodological framework considers various blue hydrogen plant designs (fixed, phased, and flexible) and compares them using relevant economic indicators (net present value (NPV), capex, value-at-risk/gain, etc.) through a detailed Monte Carlo simulation framework. Results indicate that flexible centralised hydrogen production yields greater economic value than alternative designs, despite the associated cost-premium of modularity. It is also shown that the value of flexibility increases under greater uncertainty, higher learning rates and weaker economies of scale. Moreover, sensitivity analysis reveals that flexible design remains the preferred investment option over a wide range of market and regulatory conditions except for high initial hydrogen demand. Finally, this study demonstrates that major regulatory and market uncertainties surrounding blue hydrogen production can be effectively managed through the application of flexible engineering system design that protects the investment from major downside risks whilst allowing access to favourable upside opportunities.

Genetic matching for time-dependent treatments: a longitudinal extension and simulation study

BackgroundLongitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching.MethodsTo evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models.ResultsIn all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates.ConclusionsWhile the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.

Effect of iron thicknesses on spin transport in a Fe/Au bilayer system

This paper is concerned with a theoretical analysis of the behavior of optically excited spin currents in bilayer and multilayer systems of ferromagnetic and normal metals. As the propagation, control, and manipulation of the spin currents created in ferromagnets by femtosecond optical pulses is of particular interest, we examine the influence of different thicknesses of the constituent layers for the case of electrons excited several electronvolts above the Fermi level. Using a Monte-Carlo simulation framework for such highly excited electrons, we first examine the spatiotemporal characteristics of the spin current density driven in a Fe layer, where the absorption profile of the light pulse plays an important role. Further, we examine how the combination of light absorption profile, spin-dependent transmission probabilities, and iron layer thickness affects spin current density in a Fe/Au bilayer system. For high-energy electrons studied here, the interface and secondary electron generation have a small influence on spin transport in the bilayer system. However, we find that spin injection from one layer to another is most effective within a certain range of iron layer thicknesses.

Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers Using Constrained Spherical Deconvolution

The properties of fiber reinforced polymers are strongly related to the length and orientation of the fibers within the polymer matrix, the latter of which can be studied using X-ray computed tomography (XCT). Unfortunately, resolving individual fibers is challenging because they are small compared to the XCT voxel resolution and because of the low attenuation contrast between the fibers and the surrounding resin. To alleviate both problems, anisotropic dark field tomography via grating based interferometry (GBI) has been proposed. Here, the fiber orientations are extracted by applying a Funk-Radon transform (FRT) to the local scatter function. However, the FRT suffers from a low angular resolution, which complicates estimating fiber orientations for small fiber crossing angles. We propose constrained spherical deconvolution (CSD) as an alternative to the FRT to resolve fiber orientations. Instead of GBI, edge illumination phase contrast imaging is used because estimating fiber orientations with this technique has not yet been explored. Dark field images are generated by a Monte Carlo simulation framework. It is shown that the FRT cannot estimate the fiber orientation accurately for crossing angles smaller than 70∘, while CSD performs well down to a crossing angle of 50∘. In general, CSD outperforms the FRT in estimating fiber orientations.

Preliminary debris risk assessment for mega-constellations in low and medium earth orbit due to satellite breakup

This paper presents a theoretical analysis of the potential risk posed by artificial debris clouds in low Earth orbit (LEO) from mega-constellations, modeled after current communication constellations such as Starlink and OneWeb with 750 satellites each. The analysis examines three different constellation designs: a low-altitude LEO, a high-altitude LEO, and a medium Earth orbit (MEO) constellation, which will be positioned using the Walker-Delta design. The study is based on physics-based digital mission engineering and a Monte Carlo simulation framework. The simulation involves debris generated from a single breakup of one randomly selected satellite per run, but does not consider cascading debris events. This debris cloud is propagated for 1 week and how it interacts with the mega-constellation is recorded. The results show an average of 705.65 potential conjunctions within the LEO constellation, with 14.40% of those being considered catastrophic, and an average of 165.5 conjunctions in the MEO constellation, with 0.72% considered catastrophic.

Seismic resilience assessment of Small Modular Reactors by a Three-loop Monte Carlo Simulation

We develop a three-loop Monte Carlo Simulation (MCS) framework for the seismic resilience assessment of Small Modular Reactors (SMRs), embedding Probabilistic Seismic Hazard Analysis (PSHA), seismic fragility evaluation and multiple SMR units accident sequence analysis. A set of metrics are computed to capture different aspects of SMR resilience to earthquakes, specifically the ability to withstand seismic disruption, mitigate consequences and restore normal operation. The MCS framework allows accounting for the aleatory and epistemic uncertainties of the PSHA and fragility parameters. An application is given with regards to an advanced Nuclear Power Plant (aNPP) consisting of four reactor units of NuScale SMR design. A comparison is made to a conventional NPP (cNPP), i.e., a typical large reactor of equivalent generation capacity. Both plants are fictitiously located on the Garigliano nuclear site (southern Italy). The results show that resilient features of SMRs overcome cNPPs in terms of post-accident scenario mitigation and restoration capabilities.

SIMO-Underwater Visible Light Communication (UVLC) system

The advent of visible light communication (VLC) towards 5G and beyond (5GB) networks opened new doors to unveil the mysteries of unguided mediums such as ocean. The varying physicochemical properties of water introduces the underwater optical turbulence (UOT). The UOT of UVLC link is modelled as unified Gamma-Gamma (GG) distribution fading under moderate-to-strong turbulence channel conditions as well as the misalignment of transceivers following exponential fading are included and studied. This study also contains the effect of UOT which is characterized by multiple water layers within the Monte Carlo simulation framework. The end-to-end (E2E) performances of a Single-Input and Multi-Output (SIMO) underwater VLC (UVLC) system is in consideration of Intensity-Modulation/Direct-Detection (IM/DD) technique which is obtained to fulfill the current communication requirements in hostile aqueous channels. More specifically, the probability distribution function (PDF) and cumulative distribution function (CDF) are derived to obtain the E2E performance metrics of the SIMO-UVLC system in terms of Meijer-G function. Consequently, novel closed-form expressions for average bit-error-rate (ABER) and outage probability of the system are formulated. Finally, the numerical and simulation results are obtained based on the experimental data in mixed water layers of the oceanic environments, and analytically verified through the Monte Carlo simulation approach in harsh channel conditions.

Investigation of Infiltration Loss in North Central Texas by Retrieving Initial Abstraction and Constant Loss from Observed Rainfall and Runoff Events

Accurate modeling of infiltration losses is vital for runoff estimation and thus the development of flood design/protection criteria and water management schemes, etc. In design flood practices, the initial abstraction and constant loss (IACL) method has been widely applied due to its simplicity. However, due to a lack of physical equivalent properties, the IACL method is often subject to issues in parametrization and has large dependency on calibration efforts for storm events. Despite the wide range/variability of IACL values, a single set of IA and CL values is normally adopted for specific flood frequency, which may introduce uncertainty and bias in resulting peak streamflow. In this study, we identified a total of 2,036 rainfall-runoff events for 18 watersheds in North Central Texas to estimate the total losses with their IA and CL components based on time-series of mean areal precipitation (MAP) and streamflow data. Threshold behavior is found for all studied subbasins between the summation of gross rainfall and antecedent soil moisture versus runoff depth: below the threshold, runoff depth is minimal; whereas above it, runoff is largely linearly correlated with the summation of rainfall and antecedent soil moisture. This finding provides a convenient way to estimate/predict total loss or runoff depth given MAP and antecedent soil moisture. In addition, this study shows that the IA and CL values can be approximated by the gamma and Weibull distributions, respectively. The fitted distributions of IA and CL values can be applied in a Monte Carlo simulation framework to stochastically simulate numerous rainfall-runoff events for a flood frequency analysis.

Integrated analysis of soil-structure interaction for statically indeterminate beams on spatially random soils

ABSTRACT This study aimed to develop an integrated approach to analyse the interaction between soils and beams. The research methods involved placing a statically indeterminate beam on spatially random soils, where soil springs acted as supports for the beam. The soil spring stiffness values were simulated using random field theory. This integrated approach was embedded in the Monte Carlo simulation framework to facilitate probabilistic assessment. This study concluded that the force method solution accurately determined the bending moment and shear diagrams for a beam supported by soil springs. Additionally, soil spatial variability had a significant impact on the beam responses, including the variations in footing settlements, support reactions, bending moment, and shear force. This study also identified a critical scale of soil fluctuation that coincides with the beam span, which resulted in the highest probability of structural bending failure. Overall, this study highlights the importance of accounting for soil spatial variability in an integrated geotechnical and structural design approach.

Toward a Plausible Methodology to Assess Rock Slope Instabilities at a Regional Scale

Slope failures along road cuts and highways, occurring due to heavy rainfalls or earthquakes, pose significant threats to people, vehicles, and emergency plans. In the present study, a methodology to assess the stability of rock slopes at a regional scale is proposed using a kinematic analysis and a probabilistic limit equilibrium analysis for plane sliding and wedge failure modes. The workflow adopted is described through its implementation along the main road network of the island of Thasos, located in northern Greece. On-site investigations and measurements along the island’s road network formed the basis of the present study. The results of the kinematic analysis showed that the joint sets, which were identified during the on-site investigations, formed critical intersections that could lead to wedge and plane sliding failures. The on-site measurements and the results of the kinematic analysis were utilized to perform limit equilibrium back-analyses at sites of identified failures due to the water pressure effects to probabilistically estimate the material strength properties of the joints. Subsequently, numerous limit equilibrium analyses were executed within a Monte Carlo simulation framework to produce representative fragility curves of rock slopes against plane sliding and wedge failures along the main road network, due to earthquake loading and water pressures.