In recent years some multi-mode pushover procedures taking into account higher mode effects, have been proposed. The responses of considered modes are combined by the quadratic combination rules, while using the elastic modal combination rules in the inelastic phases is not valid. Here, an optimum weighted mode combination method for nonlinear static analysis is presented. Genetic algorithm is used for optimization of the modal weight. The proposed procedure is applied for a sample building. The results show that the resulted response from the proposed method has minimal error in comparison with the response of the nonlinear time history analysis.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

A B S T R A C TFor choosing suitable place multiple methods are introduced, which examine the qualities, methods and patterns of location selection. The current methods use global approach and do not pay attention to spatial differences, environmental variation in variables, local changes modeled by local weighting indicators. It has been selected as a case study to evaluate the EMS center of Fars province and to evaluate the indicators of distance from the power plant, distance from logistics centers, industries, transportation terminals, hospitals, hotels and tourism, cities, rural areas and custom centers. has been used, using the AHP method to weight the qualities, and then for local weights, non-overlapping neighborhood filtering within a radius of 25 km are used and calculate the corresponding changes, quantity, ratio Presented in this article, the obtained weights, then the compute local weighted layers, and then local weight was multiplied in the normalized layers and the final layer were obtained. In the next step, it was combined and calculated to compare the data using the global method. The results of the two methods were transferred to the points of the EMS and the results were analyzedExtended AbstractIntroductionIn geographical studies and spatial planning, there are different approaches to choosing the right place for all kinds of applications and uses, and a process is followed to choose the best places. This process includes stating the research problem (cognition), choosing the criteria and preferences of decision makers and how to determine the weight of indicators, combining criteria (design) and finally, choosing the best option. In the current methods of spatial decision, the criteria are given a fixed weight, while in geographical locations, the distribution of geographical phenomena is not uniform. In this case, attention should be paid to the spatial distribution pattern of geographical phenomena based on the principle of sensitivity of the range of changes, the size of the range of changes affects the outputs, and the data with a smaller range of changes also have a lesser effect on the output. The data with a larger range of changes will have a more significant effect on the output. Therefore, in spatial analysis, indicators that have small changes are less important. Local models have been presented to model local changes in spatial multi-criteria decision-making; in this research, the main issue is how to model the local changes that affect the expected outputs. MethodologyThe research process is based on multi-criteria spatial decision-making methods (national and local). First, spatial patterns of indicators were analyzed through exploratory analyzes of ESDA spatial data. Then based on the review of the research criteria, the direct weighting method was used to calculate the weight of the indicators, and the experts were asked to specify the weight of the criteria in the range of 1 to 10. The opinions were combined, and the final weight was obtained. First, the data were spatially modelled to model and calculate the local weights. Then Block Statistics spatial filter without overlap was used to determine the neighborhood. The range of local changes, maximum and minimum values, were calculated, and local weights were obtained based on that. Finally, the information layers were descaled. After that, the unscaled criterion layers were multiplied by the national weights and the final national scores were obtained. In the next step, the data of the evaluated information layers (local and national) were transferred to the points of emergency databases. Based on the analysis, the fit was done. Results and discussionGeneral or global weights pay attention to the relative importance of indicators, which is the starting point for calculating local weights. Based on the research results, the most critical indicators are industries, cities and hospitals, which are more important in the location of road emergency stations. However, in order to achieve better results, it is necessary to build a local weight layer. The results of the research show that the indicators' weight and the difference in the criteria (general weight) are not equal in the entire geographical area, and the areas with a larger range of changes have a higher coefficient.Because, unlike general weights that are obtained based on different methods, local weights are estimated based on the principle of sensitivity of the range of changes and are changed spatially, and a fixed value is not considered for all areas. According to the research findings, the highest level in the general and local evaluation method corresponds to 0.5 to 0.7. Based on the local (geographic) weight method, it is less than the national or general method in the range of 0.9 to 1. Furthermore, this is due to the more precise nature of this method, which models the effects of local changes. To evaluate the compliance of the emergency centers with the final maps of suitability assessment, the information on the suitability maps was transferred to the map of the emergency center points of Fars province. Based on the evaluation of 207 emergency stations, according to the general method, 14 percent are in the high group (0.9-1), and according to the local or geographical method (16.9), percent are in this group. ConclusionAt the level of space and geographical regions, phenomena and geographical complications are not uniformly distributed, some phenomena have a homogeneous distribution, and some have a heterogeneous distribution. On the one hand, the phenomena and decision issues are affected by the context in which they are formed and evolve; on the other hand, these phenomena are placed in the network of mutual relations. Furthermore, the results of the set of phenomena may differ from the results of the phenomena individually and separately; in this article, an attempt has been made to address the issue that in spatial analysis and planning, it is necessary to pay attention to geographical differences in decision-making. Furthermore, it should not be evaluated in general terms of spatial decision problems; one of the methods of modelling these differences and spatial changes is to consider the local weight of the criteria. Based on the research findings, local (geographical) and national analysis results are different and can better show these changes. On the other hand, the pattern of spatial differences also has its spatial pattern, and paying attention to these differences in spatial differences can be the subject of further studies. FundingThere is no funding support. Authors’ ContributionAuthors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of InterestAuthors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper.

Today, maintenance and repair have become very important in the manufacturing industry. An ecient solution to prevent downtime is to predict equipment failure. Therefore, accurate and correct prediction of breakdown events in the eld of predictive maintenance can be very useful. In general, each prediction will be accompanied by a certain amount of error, which in various ways tries to control this error or limit it to a reasonable amount. In this thesis, a framework has been proposed that species when the system under review will need maintenance and repairs to prevent downtime as much as possible. Therefore, the main purpose of this thesis is to design and implement an ecient combination structure to accurately predict failure events using both standard statistical standard models and machine learning in predictive maintenance. The literature review results indicate that the use of these methods in recent years has led to extensive advances in the eld of providing accurate forecasts and subsequently improved the level of decisions made by managers and decisionmakers. The proposed model is used to predict failure events in benchmark data related to the truck air pressure system. Finally, the performance of the proposed model is compared with other data-driven techniques individually and in combination, which includes logit models, support vector machines, and multilayer perceptron neural networks. According to the numerical values obtained from the nal analysis, the results indicate that the backup vector machine model has higher prediction accuracy than other single models, and also the results indicate the eciency and eectiveness of the proposed parallel combination structure compared to the use of models individually and in series combination in modeling and forecasting issues. The parallel hybrid model improved the accuracy of predictions by an average of 11% in test data and 7% in training data. Therefore, due to the greater accuracy in combining classical statistical models and machine learning in parallel, the use of this combined method to improve the accuracy of predictions in the eld of predictive maintenance is recommended for future studies.

1. Introduction: Shape memory alloys (SMA) have found various applications in structural engineering such as active, semi active and passive control due to its characteristics such as high damping capacity, durability, resistance to fatigue and corrosion and its unique characteristics such as shape memory and super-elasticity [1]. One of the most important and effective applications of SMA in civil engineering is using these materials as braces, because of the super-elastic and shape memory properties of SMA. They have the ability to re-center the original state and to provide high energy dissipation. However, most of the researches about the use of shape memory alloys in structural engineering are in theory level and few of them are experimental.

This study presents a new method of damage detection using a combination of the second-order gradient Levenberg-Marquardt algorithm (SOGLMA) and fuzzy logic (FL) to solve the nonlinear damage detection equation for space frame structures. For damage detection in structures with a large number of degrees of freedom using the second-order gradient Levenberg-Marquardt algorithm, it is necessary to perform an iterative process of analysis and solving a set of simultaneous nonlinear equations that requires a lot of time. Therefore, the computation time and the number of iterations are reduced by using the proposed method "combination of the second-order gradient Levenberg-Marquardt algorithm and fuzzy logic (SOGLMA-FL)". Acceleration response in sensor nodes obtained at different time steps from dynamic analysis are considered as input values for fuzzification. The output values of the proposed method after defuzzification are the damage extent of structural elements. The results show that the proposed damage detection method (SOGLMA-FL) has faster convergence, lower numbers of iteration and reduced computation time than the damage detection method (SOGLMA) for space frame structures.

One of the effective ways to mitigate earthquake damage in structures is passive control of structures. Yielding metallic dampers are economic passive control devices, which are easy to fabricate and install on the structure. In this paper, the aim is to develop a design procedure for steel structures equipped with a combination of yielding metallic dampers so that, dampers will experience specific nonlinear behavior when subjected to various seismic hazard levels. For this purpose, the first step is to have the seismic hazard spectra with different return periods for the intended site of construction. In this research, this step has been taken by using the Tehran probabilistic analysis hazard project data and then plotting uniform hazard spectra with 75-year, 475-year, 975-year and 2475-year return periods. In the next step, behaviors of structures equipped with yielding metallic dampers have been investigated in the form of one-storey one-span, one-storey two-span and multi storey multi span frames. Required equations for behavior of these structures under monotonic loading is developed to begin the design process, the performance criteria for the structure and the damper is proposed. By using the derived equations, design of one storey, one span frame, as single degree of freedom structures based on performance criteria has been carried out. These single degree of freedom structures have different periods and strength reduction factors. After designing the single degree of freedom structures, nonlinear static analysis results have been compared with result of nonlinear time history analysis. For this purpose, 7 earthquake records have been chosen and scaled based on Iranian code of practice for seismic resistant design of buildings and used for dynamic analysis. Results have shown that all performance criteria of 75-year and 475-year hazard levels have been satisfied but for 975-year and 2475-year hazard levels, six cases have satisfied the desired critera with 12 percent error. Performance criteria for dampers used in multi degree freedom structures have also been proposed and a method for designing these systems based on the concept of uniform drift in all stories have been presented. Three, 3-storey, 6-storey and 9-storey buildings equipped with dampers have been designed based on proposed method. Time history analysis have been carried out for each return period to check if the desired performance have been achieved. For these analyises, 7 earthquake records were chosen and scaled based on Iranian code of practice for seismic resistant design of buildings. Comparison of performance point displacement levels and the displacements obtained from 28 nonlinear analyses, has shown up to 13 percent error. Meanwhile, the displacement levels of each set of dampers for 75-year, 475-year, 975-year and 2475-year return periods, confirmed efficiency of proposed design method and all dampers met the mentioned performance criteria. The results also showed that when hazard level increased, the difference between the results of nonlinear time history analyses and static nanlinear analyses have also increased.

Probability density functions of the involved random variables are essential for the reliability-based design of offshore structures. The objective of present research was the derivation of probability density function (PDF) for the local joint flexibility (LJF) factor, fLJF, in two-planar tubular DK-joints commonly found in jacket-type offshore structures. A total of 162 finite element (FE) analyses were carried out on 81 FE models of DK-joints subjected to two types of axial loading. Generated FE models were validated using available experimental data, FE results, and design formulas. Based on the results of parametric FE study, a sample database was prepared for the fLJF values and density histograms were generated for respective samples based on the Freedman-Diaconis rule. Nine theoretical PDFs were fitted to the developed histograms and the maximum likelihood (ML) method was applied to evaluate the parameters of fitted PDFs. In each case, the Kolmogorov-Smirnov and chi-squared tests were used to evaluate the goodness of fit. Finally, the Inverse Gaussian model was proposed as the governing probability distribution function for the fLJF. After substituting the values of estimated parameters, two fully defined PDFs were presented for the fLJF in tubular DK-joints subjected to two types of axial loading.