Cable bridges are one of the essential structures that are sensitive to vibrations. Therefore, it is necessary to investigate the seismic behavior of them. The uncertainty in structural members and earthquake excitation should be considered due to the undetectability and indeterminably them. In this paper, the reliability of the cable-stayed bridge with a 640-meter length span in two states with linear and nonlinear behavior for materials is investigated. The uncertainty in member parameters of pylons, girders, and cables, which includes the elasticity modules, cross-section, material yield strength, is considered, and the efficiency of each one is simulated by the Sampling method. Linear and nonlinear time history dynamic analyses are performed by artificial earthquakes produced at four different seismic hazard levels. The sensitivity analysis shows that the cable parameters have the highest sensitivity. The reliability analysis also indicates that the failure probability in the pylon is more than cable, and the failure probability in the nonlinear model is higher than the linear model.

Project management in construction industry, in many cases, is imperfect with respect to the integration of Occupational Health and Safety (OHS) risks. This imperfection exhibits itself as complications affecting the riskiness of industrial procedures and is illustrated usually by poor awareness of OHS within project teams. Difficulties on OHS regularly came about in the construction industry. The integration of OHS risk is not systematic in construction areas in spite of progressing laws and management systems. As project safety and risk evaluation in construction industry is an important issue, thus, the way on doing evaluation and liability of estimation is necessary. In this paper, we propose a new systematic approach based on Latin Hypercube Sampling (LHS) for integrating occupational health and safety into project risk evaluation. This approach tries to identify and evaluate reinforcement effects in a systematic approach for integrating OHS risks into project risk assessment. Furthermore, the proposed method allows evaluating and comparing OHS risks before and after the mitigation plan. A case study is used to prove the workability, credibility of the risk evaluation approach and uncomplicated integration of OHS risks at a construction project. This approach enables continual revaluation of criteria over the direction of the project or when new information is obtained. This model enables the decision makers such as project managers to integrate OHS risks toward schedule plan and compare them before and after the mitigation plan. The mentioned model is found to be useful for predicting OHS risks in construction industries and thus avoiding accidents over the path of the project.

Accurate solving of complex systems such as spacecraft is very costly and time consuming. By building a surrogate model, the solution time and the cost can be reduced. The closer the surrogate model is to the actual model, the more accurate the solution and the lower the error rate. Highprecision successor models are called metamodels. The basis of producing a high-precision metamodel is to perform high-precision sensitivity analysis with a suitable method. Sensitivity analysis can show the effect of input variables on output variables and produce a surrogate model by eliminating ineffective input variables. Therefore, sensitivity analysis is highly valuable in solving complex systems. The purpose of this article is to analyze the sensitivity of the multidisciplinary design of a monopropellant liquid propulsion system by the Latin Hypercube Sampling method. In this article, the topics related to the liquid monopropellant propulsion system are divided into six parts: High pressure gas tank, liquid fuel tank, injector, decomposition chamber, catalytic bed and nozzle. By determining the input and output variables of each subject, the results of sensitivity analysis are displayed in two ways: the sensitivity of the input variables to the output and the two-by-two correlation of the parameters with each other. In the results, as can be seen, the specific impulse input variable, in the high-pressure gas tank and the liquid fuel tank, has no effect on the output variables. In the injector, the number of grooves, groove angles and fuel tank pressure do not have a significant effect on the output variables. In the decomposition chamber sensitivity analysis diagram, the radius of the granule and for the catalyst bed, in addition to the radius of the granule, the percentage of ammonia decomposition are also ineffective. Finally, the sensitivity analysis for the nozzle shows that the ratio of specific heat has no effect on the output variables.

Drought monitoring by the Standardized Runoff Index (SRI) presents some uncertainties, mainly dependent on the choice of the probability distribution used to describe the cumulative precipitation and on the characteristics of the dataset. In this study, uncertainty analysis for estimation of the hydrologic drought characteristics (intensity, duration and frequency) was performed. Four distribution functions, two time period (30 and 49 years), six time scales (3, 6, 9, 12, 24 and 48 months) and Latin hyper cube Sampling (LHS) method ware used. For each event at per year and month, was generated 50000 random Sampling. Then, lower and upper bands of certainty was calculated for confidence level of 95%. In addition to the drought characteristics (intensity, duration and frequency) were calculated for six time scales, four distribution functions and two length of time series. Investigation of the longest duration and highest intensities showed that an increase time scale led to decrease the frequencies of drought classes and as a result increase drought intensity and duration. Further, no significant difference in the assessment of intensity and duration was between various distribution functions, meanwhile significant difference was between normal compared to weibull and gamma for the estimation of drought frequency in short time scales (3 and 6 months). Results of this study emphasized that considering drought intensity and duration, the normal distribution function, 24-month time scale and 30-years’ time series had the largest uncertainty for hydrologic drought estimation.

Production planning of an open-pit mine is a procedure during which the rock blocks are assigned to different production periods in a way that leads to the highest net present value (NPV) subject to some operational and technical constraints. This process becomes much more complicated by incorporation of the uncertainty existing in the input parameters. The commodity price uncertainty is among the most significant factors, whose effects cannot be mitigated through further exploration or investigation. The present work introduced a new approach for integration of the commodity price uncertainty into long-term production planning of open-pit mines. The procedure involves solving the problem by the integer programming method based on a series of economic block models that are realized based on the sampled prices from commodity price distribution function using the median Latin Hypercube Sampling method. The results obtained showed that the new methodology is able to reduce the risks and the net present value of the new approach at a confidence level 80% more than the conventional methods.

When designing complex products such as space thrusters, accurate simulation models are needed to evaluate and improve the design during development. The implementation of these accurate simulation models is often expensive and time-consuming. Surrogate models or metamodels are simplified models of accurate and expensive simulations that can be used to reduce some computational costs during studies or design optimization. The closer the surrogate model is to the real model, the more accurate the solution and the lower the percentage of error. These models with high accuracy are called metamodels. The purpose of this article is to design the metamodel of the liquid single-propellant thruster system using the kriging method, which can predict the behavior of the model to some extent. The purpose of this article is metamodeling of liquid monopropellant propulsion system by kriging method, which can predict the behavior of the model to some extent. The disciplines related to the liquid monopropellant propulsion system are divided into five parts: high-pressure gas tank, liquid fuel tank, injector, catalyst bed and nozzle. First, according to the input and output variables of each discipline, the design of the experiment has been done using the Latin Hypercube Sampling method. Then, using the kriging method, metamodel and distribution diagram of design points related to each of the subjects are extracted. In addition to the mass metamodel of each of the discipline, for the injector, the metamodel related to the mass flow rate of the fuel, for the catalytic bed, the characteristic speed, and for the nozzle, the specific impulse of the engine was also produced. Also, four Gaussian, Exponential, Linear and Spherical functions with degree two were compared for each of the metamodels in the kriging method. In this comparison, it was observed that due to the same coefficient of determination, the Gaussian function has less error than other functions and, as a result, better accuracy.

Computational complexity and time-consuming iteration of simulation for tuning of Proportional-Integral-Derivative (PID) controller is a common drawback in many types of existing methods. This paper aims to propose a new method for achieving an optimal design for PID gains parameters with the least number of simulation runs. To achieve this purpose, we combine polynomial regression and Latin Hypercube Sampling (LHS) in order to Design and Analyze of Computer Experiments (DACE). In this method, the LHS is performed three times to design the associated sample points for different usage that includes training sample points to fit polynomial regression as a common surrogate model; validating sample points to scale standardized residuals; grid search sample points for investigating optimal point over whole design space. To show the flexibility and applicability of the proposed method, we serve a numerical case in the tuning of PID controller for linear speed control of Direct Current (DC) motor. Four different polynomial regression fit input/output (I/O) data over separately four model’ s performances that includes Integral-Square-Error (ISE), Integral-Absolute-Error (IAE), Integral-Time-Square-Error (ITSE), and Integral-Time-Absolute-Error (ITAE). Comparison of the result with two existing approaches such as traditional Zeigler-Nichols method and Taguchi-Gray Relational Analysis (Taguchi-GRA) confirms the reliability and superiority of the proposed method.

Now, the required samples to achieve the specific precision of sensitivity analysis in design are performed based on trial and error methods. The purpose of this paper is to develop for determining the number of the required sample to achieve the specific precision of sensitivity analysis. Thus, in this paper, a new sensitivity analysis method is proposed based on the Progressive Latin Hypercube Sampling ((PLHS)) and the convergence of the analysis results. For this purpose, a (PLHS) method has been developed. This cystic approach has led to a sensitivity analysis of accuracy, efficiency and speed in a variety of models with a large number of large parameters and large changes. Sensitivity analysis has been performed on the design of a hydrazine monopropellant thruster catalyst bed model as a case study. The results of this study indicate that in the sensitivity analysis based on the (PLHS), the minimum population required for sensitivity analysis with specified accuracy can be determined. This leads to lower processing costs in the sensitivity analysis process, especially in complex models.

Introduction Soil properties play a crucial role as they determine the soil's suitability for different types of plant growth, ecosystems, and biota functioning. They have a significant impact on nutrient cycling, carbon sequestration, and soil management. Digital Soil Mapping (DSM) is a process aimed at delineating soil properties. Soil Sampling for DSM serves as  a fundamental  step in improving prediction accuracy and is crucial for incorporating variability in terms of environmental covariates. Conditioned Latin Hypercube (CLH) Sampling is a technique utilized to generate a sample of points from a multivariate distribution conditioned on one or more covariates. Numerous researchers (Ramirez-Lopez et al., 2014; Adhikari et al., 2017; Zhang et al., 2022) have endorsed this approach in their studies, following its inception by Minasny and McBratney in 2006. However, there has been limited research to date on the impact of the Latin Hypercube method's random sample selection process on the accuracy of resulting maps. Hence, the central question remains: Is the Latin Hypercube Sampling method, which is currently widely adopted, always a dependable approach in this field?   Materials and Methods The study area covers longitudes 50°35'47'' to 51°29'' east and latitudes 31°36''31'' to 32°15'48'' north in Borujen city, Chaharmahal, and Bakhtiari Province. The region, with an average elevation of 2338 meters above sea level, receives an annual rainfall of 250 millimeters and maintains an average temperature of 11.5 degrees centigrade. In this investigation, inherited data from soil studies were utilized, consisting of 250 samples distributed across the study area. In this research, the studied characteristics included percentage of equivalent calcium carbonate, clay, and soil organic carbon at a depth of 0 to 30 cm. Land component variables were extracted using the Alus Palsar digital elevation model with a spatial resolution of 12.5 meters. In the initial stage, digital maps of equivalent calcium carbonate, clay, and soil organic carbon were generated using the support vector machine method. The modeling process proceeded until a highly accurate model was achieved, with the root mean square error percentage (RMSE%) being less than 40. The Latin Hypercube approach was utilized for sample design, with 500 repetitions in this study. After selecting Sampling points for each run using the Latin Hypercube method, these points were mapped onto a detailed map, and the corresponding feature values were retrieved. The final map was created based on the extracted points. Subsequently, the Latin Hypercube approach was employed to generate soil property maps for each selected dataset. Validation was conducted using criteria such as the coefficient of explanation, root mean square error, and root mean square error in multiple iterations to ensure the accuracy of the generated maps.   Results and Discussion The results distinctly illustrates the varied selection of Sampling positions with each implementation of the Latin Hypercube method. It is important to note that there may be some overlaps in different implementations. Consequently, the primary question arises: Is a one-time execution of the Latin Hypercube sufficient for selecting study points? The findings indicate that the support vector machine model achieves satisfactory accuracy for all the examined characteristics. In the studied area, the environmental factors such as slope and elevation were identified as a significant predictors for estimating percentage of equivalent calcium carbonate.   Conclusion In the present study, the accuracy of the Latin Hypercube method was assessed for selecting Sampling location for digital soil mapping endeavors in Chaharmahal and Bakhtiari Province. Given the impracticality of collecting numerous field samples to evaluate the soil Sampling method, this research aimed to employ simulation methods based on highly accurate maps for this purpose. The results indicate that the different outputs of the Latin Hypercube method influence the accuracy of modeling, although this effect is also influenced by the specific feature under investigation and the extent of its variability within the study area.  Considering that the Latin Hypercube method is based on the principle that samples are randomly selected in each class of environmental parameters, it is suggested that future studies using this method should account for this principle. Adequate consideration should be given, and the selection of Sampling locations should rely on multiple implementations of the Bhattacharya distance method to ensure robustness and reliability.

The excavation damaged zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. In this paper, a methodology was examined for computing the creation probability of damaged zone by Latin Hypercube Sampling based on a feed-forward artificial neural network (ANN) optimized by hybrid particle swarm optimization and genetic algorithm (HPSOGA). The HPSOGA was carried out to decide the initial weights of the neural network. A case study in a test gallery of the Gotvand dam, Iran was carried out and creation probabilities of 0.191 for highly damaged zone (HDZ) and 0.502 for EDZ were obtained.