Simplified Analysis Research Articles

Simplified finite-element analysis method for local and global prediction on axial compression behavior of square spiral-confined reinforced concrete-filled steel tubular columns

Square spiral-confined reinforced concrete-filled steel tubular (SSCRCFST) columns have recently been proposed as an advanced form of composite columns. However, there is still a lack of an efficient approach that can accurately predict both their section-average behavior and local concrete behavior. This paper presented a novel simplified method combining elastic finite element analysis with accurate material laws of concrete, steel tube and longitudinal bar to analyze the axially compressive behavior of SSCRCFST columns. The proposed method utilized elastic finite-element analysis to approximate the distribution and ultimate state of confining stress within each core concrete element, which was then substituted into the Mander confined concrete model to obtain the strength enhancement in each core concrete element subjected to different level of lateral confinement. Finally, the integrated compressive curve of a column can be derived by summarizing the strength of all elements. Verification of the proposed method against a large test database confirmed its capability to accurately capture the results of existing compression tests of SSCRCFST columns. The proposed computationally efficient method was then used to investigate both the section-average and local behavior of SSCRCFST column under monotonic compression for an improved understanding of the confinement mechanism. Finally, a reasonably modified design method for the axial strength of SSCRCFST column was proposed based on research findings in this study, which led to the continuity in the axial strength predictions of SSCRCFST columns with various corner radius ratio and showed good agreement with the test results.

Performance-based multi-hazard engineering (PB-MH-E): The case of steel buildings under earthquake and wind

Focusing on natural hazards as wind and earthquake, the goal of this work is to understand if interference occurs in the design choices for steel buildings of a certain height, thus determining which design requirement and hazard is predominant, between Ultimate Limit State (ULS) requirements under earthquakes, or comfort of the occupants and the Serviceability Limit State (SLS) of the structure under wind. Thus, in order to compare structural responses, both in linear (i.e., SLSs) and non-linear field (i.e., ULS) a simplified analysis procedure that could be also implemented in Standards and in design practice (so-called SAC-FEMA method, originally introduced in the seismic field by Cornell in the early 2000s and more recently extended to the wind) is adapted, leading to a true optimal Performance-Based Multi-Hazard Design (PB-MH-D) of the structure considering the two hazards. The procedure is applied to two case-study steel buildings, 17 and 60 floor heigh; the approach is shown to efficiently lead to a design solution who consistently tackles with the same level of reliability with the two hazards.

A simplified metabolomic analysis of dried blood spots in breast cancer patients

Breast cancer (BC) is among the most commonly diagnosed cancers. Besides mammography, breast ultrasonography and the routinely monitored protein markers, the variations of small molecular metabolites in blood may be of great diagnostic value. This study aimed to quantify specific metabolite markers with potential application in BC detection. The study enrolled 50 participants, 25 BC patients and 25 healthy controls (CTRL). Dried blood spots (DBS) were utilized as biological media and were quantified via a simplified liquid chromatography tandem mass spectrometry (LC-MS/MS) method, used in expanded newborn screening. The targeted metabolomic analysis included 12 amino acids and 32 acylcarnitines. Statistical analysis revealed a significant variation of metabolic profiles between BC patients and CTRL. Among the 44 metabolites, 18 acylcarnitines and 10 amino acids remained significant after Bonferroni correction, showing increase or decrease and enabled classification of BC patients and CTRL. The well-established LC-MS/MS protocol could provide results within few minutes. Therefore, the combination of an easy-to-handle material-DBS and LC-MS/MS protocol could facilitate BC screening/diagnosis and in the next step applied to other cancer patients, as well.

Comparison of Seismic Responses of Underground Utility Tunnels Using Simplified Analysis Methods

Comparison of Seismic Responses of Underground Utility Tunnels Using Simplified Analysis Methods

Using Big Data and analytics in accounting for strategic decisions

The purpose of this paper is to study the potential of using Big Data and analytics in accounting in order to improve strategic decision-making processes at the enterprise. To achieve the goal, the following tasks were performed in the research process: the concepts, advantages and disadvantages of using Big Data and analytics in accounting were considered; directions for using Big Data in accounting in general and for improving strategic decision-making processes in particular were investigated. The use of advanced technologies in accounting can significantly improve the quality and relevance of accounting information, reduce preparation time and ensure availability of data in real time. Studying the basic principles of accounting organization using information technologies, efficiency, accuracy, security and compliance with regulatory requirements were identified as key. The potential impact of Big Data on the accounting profession and its practices is very significant. As the volume of available information grows, new opportunities appear to improve the accuracy and efficiency of traditional accounting approaches. Big Data analysis can reveal financial trends, patterns, and anomalies that were previously difficult to access or invisible through traditional methods. The use of Big Data and analytics in accounting for making strategic decisions can significantly contribute to the achievement of the goals specified in the modern economic paradigm. Simplified acquisition and analysis of large volumes of data makes it possible not only to increase the accuracy and speed of decision-making in financial management, but also to respond more effectively to changes in macroeconomic conditions, such as diversification of production and exports, improvement of living conditions of the population, and reduction of social inequality. The introduction of high technologies, including Big Data analysis, is becoming a key tool for ensuring sustainable economic development, contributing to the growth of productive forces and optimization of production processes in accordance with modern requirements and challenges of the global market.

Experimental and theoretical study on the smoldering combustion of size-fractioned forest duff particles

Smoldering combustion process has been suggested as a new method potentially useful in the treatment of biosolids. Natural forest duff (FD) often consists of organic fuel layers with varying particle sizes, yet the influence of the size-fractioned particles on the smoldering combustion dynamics in terms of the heat and mass transfer is not well understood. In this study, the oxidative pyrolysis and smoldering behavior of FD samples with four particle sizes (0 < d1 ≤ 0.425 mm, 0.425 < d2 ≤ 1 mm, 1 < d3 ≤ 2 mm, 2 < d4 ≤ 4 mm) were experimentally and theoretically investigated. Micro-scale thermo-gravimetric (TG) analysis, and a four-step kinetic model incorporating water evaporation, FD pyrolysis, FD oxidation and char oxidation showed that the activation energy of the FD pyrolysis and the ash content are negatively correlated, while the activation energy of the char oxidation increases from 87.26 kJ mol−1 to 119.22 kJ mol−1 with the particle size increasing from d1 to d4. Furthermore, the order of the combustion performance of the FD samples is shown as d1<d2<d4<d3. A series of laboratory-scale smoldering experiments revealed that the peak smoldering temperature increases with the fuel depth while the horizontal spread rate decreases with the fuel depth. Both the peak mass loss rate and the smoldering duration presented a reverse order (d1>d2>d4>d3) of the combustion performance found in the TG tests. A simplified heat transfer analysis qualitatively revealed the beneficial effects of the size-fractioned particles on the smoldering spread rate, while a mass transfer analysis revealed the favorable and adverse influences of the particle size on the kinetic-controlled and diffusion-controlled char oxidation rate, respectively. These findings verify that particle sizes alter the FD physicochemical properties (e.g., specific surface area, bulk density, porosity, permeability, chemical components, and lower calorific value), which in return impact the chemical kinetics, heat and mass transfer process in smoldering combustion. This work provides new insights into the effects of the size-fractioned particles on smoldering combustion, ultimately improving the fundamental understanding for optimizing particle sizes for energy conversion and usage.

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution.

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

How many inflectional forms does a Somali verb have?

Abstract The Somali verb is traditionally presented as having a quite large number of inflectional paradigms with, e.g., special negative forms and several kinds of subordinate clause forms. The complexity of traditional descriptions tends to overshadow important generalisations that would otherwise reflect a relative simplicity of the Somali verb system. The most important reasons for the abundance of paradigms are that forms, periphrastic constructions and their functions are not systematically differentiated, but also that the tonal accent and case marking are treated as parts of the inflection, instead of phrasal marking of syntactic or pragmatic functions. The aim of this article is therefore to suggest a simplified analysis and to define the minimal set of synthetic forms and analytical constructions that are necessary for the description of Somali verb inflection as well as the basic syntactic functions of these forms and constructions.

Underwater radiated noise numerical analysis of polar transport vessels under ship-ice-water-air coupling continuous icebreaking based on the S-ALE algorithm

With the gradual opening of the Arctic channel, the underwater radiated noise induced by the ship-ice collision cannot be ignored. However, the underwater radiated noise induced by ship-ice collision is significantly different from that of ships navigating in open water. To explore the response rule of transient underwater radiation noise induced by ship-ice collision, this paper proposes a simplified numerical analysis method of ship-ice collision-induced transient underwater radiated noise in the full frequency band by combining the explicit dynamic method and acoustic analysis method. The ship-ice-water-air coupled collision analysis method based on the S-ALE algorithm is used to simulate and verify the ice load in the process of ship-ice continuous collision at different speeds. Then, the frequency-domain acoustic analysis method is used to predict the transient underwater radiated noise response in the full frequency band under icebreaking excitation and evaluate it by referring to the steady-state underwater radiated noise standard. The results show that the simplified analysis method can effectively predict the transient underwater radiation noise under icebreaking excitation. Under the icebreaking excitation, the radiation shape is irregularly scattered and the radiation direction is concentrated towards the bottom and bow of the ship with the frequency increase. The influence range of transient underwater radiated noise is mainly within 1000 Hz at different speeds, and the variation in the ship's speed affects the amplitude of the sound power level and the smoothness of the periodic response. The synthesized total sound power levels are 172.56 dB (V = 2.0kn), 174.15 dB (V = 2.5kn), 175.70 dB (V = 3.0kn), and 182.49 dB (V = 4.0kn), respectively. According to the comparison with the evaluation standard, it is found that the transient underwater radiation noise induced by ship icebreaking has a greater impact on marine organisms. It should give more detailed requirements within 10–1000 Hz when using relevant standards to evaluate underwater radiated noise induced by polar icebreaking. This paper can provide a reference for the research of underwater radiated noise induced by ship-ice collision and the formulation of relevant standards.

Energy demand and carbon emission analyses of a solar-driven domestic regional environment mobile robot as household auxiliary heating and cooling method

Under the trend of energy saving and carbon reduction, reducing household energy consumption, which is an important part of social energy consumption, has become a hot topic. In order to solve the problem of mismatch between supply and demand of household environment control systems as well as to better meet the individual needs of users, we innovatively designed a solar-driven domestic regional environment mobile robot as auxiliary heating and cooling method. In this paper, a simplified thermal network analysis of the thermal conditions of a dwelling is utilized to calculate the energy saving and carbon reduction potential on summer and winter reference days in Beijing and Guangzhou, China. The simulation results show that the system has good energy saving potential and carbon reduction effect. In particular, the single-day energy saving potential of the system on winter days in Beijing reaches to 583.6 kWh compared to the traditional method. Under the power supply mode, the single-day carbon reduction can reach 462 kg CO2 on winter days in Guangzhou, and it can reach 333.2 kg CO2 on summer days in Beijing. We also discuss the effect of regional space size and clothing thermal resistance. As the local space size and the clothing thermal resistance increases, the energy saving potential and carbon reduction benefits of the system decrease, but the overall impact is small. The proposed mobile robot following in the vicinity of home inhabitants to help maintain their thermal comfort, can improve the efficiency of dwellings heating and cooling systems and utilize the limited solar energy resources in family area.

Leveraging parameterized Chernoff bounds for simplified algorithm analyses

In this paper, we derive parameterized Chernoff bounds and show their applications for simplifying the analysis of some well-known probabilistic algorithms and data structures. The parameterized Chernoff bounds we provide give probability bounds that are powers of two, with a clean formulation of the relation between the constant in the exponent and the relative distance from the mean. In addition, we provide new simplified analyses with these bounds for hash tables, randomized routing, and a simplified, non-recursive adaptation of the Floyd-Rivest selection algorithm.

A Simplified Analysis of Radial Consolidation of PVD-Installed Soft Soil Considering Sand Seam and Well Resistance

A Simplified Analysis of Radial Consolidation of PVD-Installed Soft Soil Considering Sand Seam and Well Resistance

Corrosion-induced fragility of existing prestressed concrete girder bridges under traffic loads

Italian and European transportation networks include a significant number of existing bridges, built since the early ‘60s, characterised by simply supported prestressed concrete (PC) girders with post-tensioned steel tendons. Corrosion of tendons, which may lead to significant loss of structural capacity, cannot be detected by simple visual inspections and requires advanced and expensive testing by bridge owners. Therefore, procedures aimed at risk-informed prioritisation of advanced inspections and possibly retrofit are needed. The paper presents a study on the fragility of existing PC girder bridges considering traffic loads, accounting for corrosion-induced effects. An automated framework is proposed, aiming at the efficient probabilistic structural assessment of the investigated bridge typology accounting for different critical corrosion scenarios and the influence of knowledge-based uncertainty related to geometric and mechanical properties. To simulate corrosion effects, prestressing steel tendon geometric and mechanical characteristics are modified through a specific algorithm able to estimate variations in flexural and shear bearing capacity of critical cross-sections. To simulate the traffic loads, a simplified analysis is performed by using code-based traffic load models. In the paper, the framework is tested with reference to a dataset of case-study superstructures. The obtained fragility curves are deeply discussed, highlighting the effects of the number of girders and span length on the corrosion-induced increase in fragility. Although the proposed methodology presents some simplifications, it could improve the current practices of risk prioritisation, by supporting transportation authorities in ensuring the safety of the existing bridge stock.

Three-Dimension Epithelial Segmentation in Optical Coherence Tomography of the Oral Cavity Using Deep Learning.

This paper aims to simplify the application of optical coherence tomography (OCT) for the examination of subsurface morphology in the oral cavity and reduce barriers towards the adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for the simplified analysis of the large volume of data collected during OCT. Imaging and corresponding histopathology were acquired in-clinic using a wide-field endoscopic OCT system. An annotated dataset (n = 294 images) from 60 patients (34 male and 26 female) was assembled to train four unique neural networks. A deep learning pipeline was built using convolutional and modified u-net models to detect the imaging field of view (network 1), detect artifacts (network 2), identify the tissue surface (network 3), and identify the presence and location of the epithelial-stromal boundary (network 4). The area under the curve of the image and artifact detection networks was 1.00 and 0.94, respectively. The Dice similarity score for the surface and epithelial-stromal boundary segmentation networks was 0.98 and 0.83, respectively. Deep learning (DL) techniques can identify the location and variations in the epithelial surface and epithelial-stromal boundary in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes.

Development and Performance Evaluation of a New Test Kit for Quantifying the Degree of DNA Fragmentation.

PCR-based genetic testing of agricultural products and foods is widely used for detecting various analytical targets such as genetically modified organisms and food allergens. However, it is difficult to obtain accurate genetic testing results from processed foods because DNA is fragmented by heat and pressure during food processing. Thus, we previously developed an analytical method to quantitatively evaluate the degree of DNA fragmentation for the purpose of QC of genetic testing for processed foods. Our previous analytical method requires four PCR primer sets, resulting in high reagent costs and heavy analytical workloads. Therefore, we attempted to develop an easy-to-use test kit for quantifying the degree of DNA fragmentation and to evaluate its analytical performance. To simplify the analysis procedure, we used only two primer sets. In addition, no-fragmentation control templates were prepared to obtain stable measurement results. The precision of the simplified analysis was evaluated through blind tests between laboratories. It was confirmed that plant species and extracted DNA concentrations had little effect on analysis with the newly developed test kit. In addition, the analytical values indicating the degree of DNA fragmentation exhibited small variability between laboratories. We confirmed the high practicality of the developed test kit. Because DNA fragmentation in cells is a universal phenomenon, we anticipate that the test kit will be used not only for QC of genetic testing but also for food testing, medical diagnostics, and other applications in a range of fields. The newly developed test kit enables quantitative evaluation of the degree of DNA fragmentation in a simple manner.

Reductive Immobilization of Hexavalent Chromium in Soil Using Hydrazine – Kinetics, Process Optimization, Sonochemical Enhancement, and Cost Estimation

ABSTRACT The presence of hexavalent chromium in soil poses a grave hazard to the environment and human health. This study explores the effectiveness, mechanism, and economic viability of remediation of Cr(VI)-contaminated soil by hydrazine. Soil-phase batch kinetic experiments were performed to examine the feasibility of reducing Cr(VI) while finding the effects of relevant process parameters, such as initial Cr(VI) concentration, hydrazine dosage, pH, soil-to-water ratio, and temperature. The materials were thoroughly characterized to gain insight into the mechanism. TCLP tests were done to affirm the immobilization stability. Complete Cr(VI) reduction was achieved within 3 h using a moderate 326% excess of hydrazine at neutral pH. Sonication of the soil mixed with hydrazine using a probe-type sonicator accelerated the reduction of Cr(VI) to Cr(III). Kinetic data could be adequately fitted in a second-order rate model, where the rate constant shows a linear dependency on the soil-to-water ratio. Additionally, the logarithmic rate constant demonstrates linearity within the pH range. The leaching toxicity of Cr(VI) and Crtotal complied with the USEPA regulatory limit and remained stable for 75 d. Hydrazine alone and its combination with ultrasound are sustainable and economically feasible for effective remediation of Cr(VI)-contaminated soil. The statistical analysis using RSM indicated a 99.4% reduction, aligning closely with the calculated optimized value. The optimal process conditions are easy to implement for field application at a competitive cost compared to other reductants. A simplified economic analysis was done to check the competitive acceptability of the technique, considering the construction cost of the remediation plant and its daily operational and maintenance expenses. The results show that the proposed technique will be a competitive one for field applications for the remediation of soil contaminated with Cr(VI). The data and kinetic rate equation will be useful for the process design of the remediation of soil contaminated with Cr(VI).

Enhanced weathering for CO2 removal using carbonate-rich crushed returned concrete; a pilot study from SE Ireland

A 1.21-ha pilot study in SE Ireland investigated crushed returned concrete (CRC), applied at a rate of 7.5 tonnes/ha as a soil amendment for carbon dioxide removal (CDR) by enhanced weathering (EW) over a 10-month period. Most, but not all amended sites showed enhanced concentrations of calcium and bicarbonate in shallow soil waters compared with adjacent controls. Soil pH increased rapidly by 0.3 to >1 pH units (5.75 ± 0.25 (n = 8) in controls to 6.5 ± 0.7 (n = 24) at amended sites), with greatest increases in soils that had lowest initial pH (<6.0). Aside from one site where concentrations continued to increase, calcium (1.76–0.39 mM Ca) and bicarbonate (4.06–0.64 mM) values in soil waters declined gradually over the course of the study, consistent with rapid initial dissolution and progressive exhaustion of the fine-grained fraction, as indicated by a shrinking-core model. Most sites exhibited high bicarbonate to cation ratios (1.2–2.2) consistent with CRC dissolution by soil carbonic acid, although two sites adjacent to the field-boundary river that floods periodically showed evidence for strong acid (nitric) dissolution. Enhanced bicarbonate levels in soil waters at amended sites compared with controls, coupled with a downward-flush hydrological model driven by annual effective rainfall to calculate bicarbonate export and a simple stoichiometric correction for nitric acid weathering, indicate gross CDR rates in the range zero to 0.52 tonnes CO2/ha (69 kg CO2/tonne), limited by bicarbonate export rather than weathering rates. Further optimisation of the CDR efficiency of this material should be possible using strategies to avoid or pre-neutralise non-carbonic soil acids and by taking account of sub-aerial carbonation at the post-crushing, pre-amendment stage that arises when the material is crushed in preparation for EW applications. A simplified life-cycle analysis indicates that a discount of about 11% should be applied to account for CO2 emissions from crushing, transport and spreading operations.

Exponential stability analysis of delayed partial differential equation systems: Applying the Lyapunov method and delay-dependent techniques

This paper presents an investigation into the stability and control aspects of delayed partial differential equation (PDE) systems utilizing the Lyapunov method. PDEs serve as powerful mathematical tools for modeling diverse and intricate systems such as heat transfer processes, chemical reactors, flexible arms, and population dynamics. However, the presence of delays within the feedback loop of such systems can introduce significant challenges, as even minor delays can potentially trigger system instability. To address this issue, the Lyapunov method, renowned for its efficacy in stability analysis, is employed to assess the exponential stability of a specific cohort of delayed PDE systems. By adopting Dirichlet boundary conditions and incorporating delay-dependent techniques such as the Galerkin method and Halanay inequality, the inherent stability properties of these systems are rigorously examined. Notably, the utilization of Dirichlet boundary conditions in this study allows for simplified analysis, and it is worth mentioning that the stability analysis outcomes under Neumann conditions and combined boundary conditions align with those of the Dirichlet boundary conditions discussed herein. Furthermore, this research endeavor delves into the implications of the obtained results in terms of control considerations and convergence rates. The integration of the Galerkin method aids in approximating the behavior of dominant modes within the system, thereby enabling a more comprehensive understanding of stability and control. The exploration of convergence rates provides valuable insights into the speed at which stability is achieved in practice, thus enhancing the practical applicability of the findings. The outcomes of this study contribute significantly to the broader comprehension and effective control of delayed PDE systems. The elucidation of stability behaviors not only provides a comprehensive understanding of the impact of delays but also offers practical insights for the design and implementation of control strategies in various domains. Ultimately, this research strives to enhance the stability and reliability of complex systems represented by PDEs, thereby facilitating their effective utilization across numerous scientific and engineering applications.

Regional potential of coastal ocean alkalinization with olivine within 100 years

The spreading of crushed olivine-rich rocks in coastal seas to accelerate weathering reactions sequesters atmospheric CO2 and reduces atmospheric CO2 concentrations. Their weathering rates depend on different factors, including temperature and the reaction surface area. Therefore, this study investigates the variations in olivine-based enhanced weathering rates across 13 regional coasts worldwide. In addition, it assesses the CO2 sequestration within 100 years and evaluates the maximum net-sequestration potential based on varying environmental conditions. Simulations were conducted using the geochemical thermodynamic equilibrium modeling software PHREEQC. A sensitivity analysis was performed, exploring various combinations of influencing parameters, including grain size, seawater temperature, and chemistry. The findings reveal significant variation in CO2 sequestration, ranging from 0.13 to 0.94 metric tons (t) of CO2 per ton of distributed olivine-rich rocks over 100 years. Warmer coastal regions exhibit higher CO2 sequestration capacities than temperate regions, with a difference of 0.4 t CO2/t olivine distributed. Sensitivity analysis shows that smaller grain sizes (10 µm) exhibit higher net CO2 sequestration rates (0.87 t/t) in olivine-based enhanced weathering across all conditions, attributed to their larger reactive surface area. However, in warmer seawater temperatures, olivine with slightly larger grain sizes (50 and 100 µm) displays still larger net CO2 sequestration rates (0.97 and 0.92 t/t), optimizing the efficiency of CO2 sequestration while reducing grinding energy requirements. While relying on a simplified sensitivity analysis that does not capture the full complexity of real-world environmental dynamics, this study contributes to understanding the variability and optimization of enhanced weathering for CO2 sequestration, supporting its potential as a sustainable CO2 removal strategy.

Probability distributions of geotechnical properties for heterogeneous lignite mine spoils

ABSTRACT During surface coal mining, large amounts of overburden waste materials, known as spoils, are excavated and dumped, creating massive heaps. The sustainable management of these areas is a global priority, but a comprehensive geotechnical characterization has not yet been achieved due to limited research on the spoils’ geotechnical properties. This study conducts an advanced statistical analysis of lignite mine spoil materials from a large heap in Greece to determine suitable probability distributions. Five distributions - normal, lognormal, Weibull (2 and 3 parameters), and Gamma - were evaluated, considering the high variability of spoil material properties. The lognormal and Weibull distributions were found to best fit most geotechnical parameters, except for cohesion, which showed an uncommon distribution. In some cases, the normal distribution can be used for a simplified analysis. The findings can guide the analysis of spoil materials, as advanced numerical and stochastic methods are essential for robust geotechnical design.