Scientia Iranica
Volume:22 Issue: 2, 2015

The thermoelasticbending response is presented for a simply-supported composite laminated plate subjected to a thermal field. The sinusoidal plate theory as well as classical and other shear deformation theories is used. The laminated plate may be composed of one material or a combination of two materials. The layers may be symmetric cross-ply or anti- or non-symmetric angle-ply lay-up. The numerical illustrations concern thermal bending responses of the presented rectangular plates are studied. The effects due to many parameters such as shear deformation, aspect ratio, side-to-thickness ratio, thermal loading and elastic foundations are all investigated.

Radio frequency identification (RFID) technology is emerging as an important technology for improving management efficiency. Web-based systems (WBS) are particularly useful for managing operations spread over multiple locations. Artificial intelligence (AI) can be used to process uncertain and incomplete information which inevitably occurs in the real world. This study aims to enhance managerial efficiency through the integration of RFID, web-based technologies, and artificial intelligence. RFID is primarily used to identify managerial objects while; web-based technology is used for data management; and AI is used to analyze the collected data. A real case is used to validate the applicability of the proposed method. Experimental results show that the integration of RFID, web-based systems, and AI can be effectively applied in a practical environment. The proposed method can improve managerial efficiency, data transfer, data quality, and service process time. This study is one of the first to investigate the integration of RFID technology with web-based technology and AI.

In this study, workability and mechanical properties of fiber reinforced self-compacting lightweight concretes (FRSCLC) were investigated. Concrete samples were produced with steel fiber include low and high carbon were added. Fluidity of FRSCLC has been conducted by two categories of flow ability property and viscosity of fresh concrete. Mechanical performance of the concrete mixes was determined with short and long-term tests, which include compressive and flexural strength at 3, 7, 28 and 365 days. The test results showed that adding fibers to self-compacting lightweight concrete mixtures decreased workability. On the other hand, compressive strength was lower enhanced compare to the results of flexural strength. As a result, compressive and flexure strengths of SCLWC’s increased up to 30% and 43% adding fiber at 28 days, respectively.

Selection of suitable site for dam is one of the problems associated with water resources management, and it is dependent on a set of qualitative and quantitative criteria. Such problems can be resolved using multi-criteria decision-making (MCDM) approaches. This study aims to develop a MCDM method integrated with fuzzy logic and group decision-making, specifically focused on dam site selection. A fuzzy AHP method was extended to group decision making, and then the resulting group fuzzy AHP was combined with the VIKOR method. In the integrated method, fuzzy concepts were used to account for decision-makers’ subjective judgments when considering the uncertainties of the site selection process. Group fuzzy AHP was used to determine the weights of different criteria and VIKOR was used to rank alternatives. The integrated method was applied to selection of the optimal site for an earth dam in Harsin city, Iran. The results show that the proposed method is an effective and reliable method in selecting the optimal dam site.

In this study, the dynamic response of a Timoshenko beam under moving mass is investigated. To this end, vectorial form orthogonality property of the Timoshenko beam free vibration modes is applied to the EEM (eigenfunction expansion method). The implication of the vectorial form series and an appropriate inner product of mode shapes in combination, are focused for a beam with arbitrary boundary conditions. Consequently, significant simplifications and efficacy in the utilization of the EEM in eliminating the spatial domain is achieved. In order to comprise validation, the present study is compared with the DET (discrete element technique) and the RKPM (reproducing kernel particle method).

The Harmony Search (HS) algorithm is a popular metaheuristic optimization method that reproduces the music improvisation process in searching for a perfect state of harmony. HS has a remarkable ability in detecting near global optima at low computational cost but may be ine ective in performing local search. This study presents the Multi- Adaptive Harmony Search (MAHS) algorithm for sizing optimization of skeletal structures with continuous or discrete design variables. The main di erence between the proposed algorithm and classic HS is the way of choosing and adjusting the bandwidth distance (bw). Furthermore, MAHS dynamically updates the Harmony Memory Consideration ate (HMCR) and Pitch-Adjusting Rate (PAR) parameters during the search process. The robustness and performance of the MAHS algorithm are evaluated in comparison with literature, and in particular, with well-known HS variants such as Global-best Harmony Search (GHS), Self-Adaptive Harmony Search (SAHS), and Ecient Harmony Search (EHS). Optimization results obtained by the MAHS algorithm con rm the validity of the proposed approach.

In this paper, artificially prepared sand-bentonite samples with different bentonite contents were employed to investigate the role of the clay content in the swelling potential, swelling pressure and oedometric behavior of expansive clays. Specimens were also dynamically compacted utilizing an identical compaction effort at various initial water contents to study the effect of the initial fabric of the soil on its volume change behavior and also to find the moisture content corresponding to the minimum drying-wetting induced swell and shrinkage of the material. Furthermore, commercially available Potassium Silicate is evaluated as a potential stabilizer in improving the volume change features of the sand-bentonite mixtures. Three dosage levels of the Potassium Silicate solutions were utilized to stabilize the expansive clayey soil. Free swelling, drying-wetting and consolidation tests were conducted on the stabilized soil samples. The results indicate that the swelling potential of the improved soil specimens are much lower than those of the non-stabilized soils. Also, Potassium Silicate eliminates the shrinkage behavior of the expansive clay. Besides, the mechanical oedometric behavior of the stabilized and non-stabilized soils is similar and Potassium Silicate does not change the response of the soil samples to the mechanical loading.

The main problem in performance-based of structures is the extremely high computational demand of time-history analyses. In this paper, an efficient framework is developed for solving the performance-based multi-objective optimal design problem considering the initial cost and the seismic damage cost of steel moment-frame structures. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. For improving the time efficiency of the solution algorithm, the generalized regression neural network (GRNN) is utilized as the meta-model for fitness approximation and a specific evolution control scheme is developed. In this scheme, in order to determine which individuals should be evaluated using the original fitness function and which by the meta- model, the fuzzy c-mean (FCM) clustering algorithm is used to choose the competent individuals rather than choosing the individuals randomly. Moreover, the computational burden of time history analyses is decreased through a particular wavelet analysis procedure. The constraints of the optimization problem are considered in accordance with the FEMA codes. The results obtained from numerical application of the proposed framework demonstrate its capabilities in solving the present multi-objective optimization problem.

The nonlinear behavior of reinforced concrete columns subjected to biaxial bending with consideration of bond-slip at the steel-concrete interface is investigated. Separate degrees of freedom are used for the steel and concrete parts to allow for the difference in displacement between the reinforcing bars and the surrounding concrete. The effect of bond-slip is investigated on the numerical bearing capacity of reinforced concrete column subjected to axial and biaxial bending forces. The axial force–bending moment (P-M) interaction surface of the reinforced concrete column for the two conditions of (with and without bar slip) is calculated and compared also with ACI criteria. The results show that, although the ACI criteria is based on the perfect bond assumption, the results are conservative anyway due to the fact that the beneficial effect of stirrups confinement on the concrete compressive strength is neglected, and the use of the reduction factor φ, does not make any modification necessary for considering bond-slip effect on the ultimate capacity of RC section.

Uncertainties in earthquake catalogs, earthquake recurrence parameters, and in the variation of ground motion parameters are often considered in the evaluation of seismic hazard analysis. The purpose of this study is to develop an artificial statistical procedure based on Bayes’ formulation and weighted bootstrap sampling to estimate seismicity parameter (b-value of the Gutenberg-Richter law) from both historical and instrumental data in a given region. The procedure allows for uncertainty in the period of completeness, and assigns different weights to historical seismicity as compared to instrumental seismicity. Variation of seismicity within seismic sources is allowed with this procedure. This variation generalizes the condition of spatially homogeneous seismicity within seismic sources and permits accurate representation of historical seismicity. As a case study, the earthquake catalog of the greater Tehran, Iran is considered to estimate seismicity parameters as well as probabilistic seismic hazard analysis (PSHA) using the proposed procedure, and then the results are compared with those obtained from a conventional PSHA method. This comparison confirms the applicability of the procedure used in this study.

Prediction of river flow is one of the main issues in the field of water resources management. Because of the complexity of the rainfall-runoff process, data-driven methods have gained increased importance. In the current study, two newly developed models called Least Square Support Vector Regression (LSSVR) and Regression Tree (RT) are used. The LSSVR model is based on the constrained optimization method and applies structural risk minimization in order to yield a general optimized result. Also in the RT, data movement is based on laws discovered in the tree. Both models have been applied to the data in the Kashkan watershed. Variables include (a) recorded precipitation values in the Kashkan watershed stations, and (b) outlet discharge values of one and two previous days. Present discharge is considered as output of the two models. Following that, a sensitivity analysis has been carried out on the input features and less important features has been diminished so that both models have provided better prediction on the data. The final results of both models have been compared. It was found that the LSSVR model has better performance. Finally, the results present these models as a suitable models in river flow forecasting.