• Worked with Additive Powders
• Developed Rheology modifications
• Finite Element modelling of Reinforced Concrete connections or other concrete structural elements
• Fire Performance of Geopolymer Concrete-filled Steel Columns
• Experimentally Worked on Durability, Permeability, Thermal conductivity, Spalling, Corrosion and Mechanical Properties of Plain Concrete, Self-Consolidating Concrete and novel-fibrous pervious eco-efficient concrete.
• Developed Adaptive Neuro-Fuzzy technique, Genetic Programming and Machine Learning
• Development of innovative structural systems for buildings and bridges
• Finite Element modelling of Steel and composite connection
• Assessment, retrofit and rehabilitation of existing structures
• Innovative structural applications of advanced composite materials
• Design and analysis for serviceability and sustainability of civil infrastructure
• Effect of fire load on different types of connections and structural elements
Investigation On the Effect of Pumice Powder, Granulated Blast, Furnace Slag and Fly Ash on Fresh Properties, Mechanical Properties and Durability Of Self Consolidating Concrete:
Although the use of self-consolidating concrete is inevitable in many modern structures, but high consumption of cement in mixing plans causes to increase production costs and adverse environmental effects. In this study, with the intention of finding resources economically, more efficient and environmentally friendly, natural pumice Pozzolan powder and molten overburden have been studied in the binary plans 10 to 50 percent replacement and to test fresh concrete properties, mechanical properties and durability. As a natural Pozzolan, pumice requires no sophisticated equipment to prepare to use, and just it needs to become the powder. Because of the ease in preparation, pumice is the basis of this research. In order to validate the results, in addition to control samples of each project, binary fly ash known as a powder is tested. Afterwards, ternary pumice and silica fumes are investigated to strengthen the binary results. The results of this study demonstrate desired performance of pumice powder and overburden in high percentage of replacement values.
Performance Evaluation Of Buckling Restrained Brace With Tubular Profile:
The past years the use of buckling restrained braces (BRBs) have had a dramatic growth due to their better performance comparing to conventional braces. BRBs have more ductility and energy absorption capacity by excluding the overall brace buckling.
This article aims to discuss a simple, easy replaceable yet efficient kind of buckling restrained brace which is a all-steel BRB, made up of two major components as all conventional ones do: (1) a steel core carrying the whole axial forces and (2) a steel restrained tube that prevents the core from buckling; the filling mortar has been eliminated leading to a simple detailing BRB system. The proposed BRB consisting of a rectangular tube core has been investigated and results are compared to the ones obtained by researches done before on a configuration with a plate core profile with the same core cross sectional area and their hysteretic behaviour has been contrasted.
It is known that when the wall thickness of the restrained tube is small compared to its width or to the cross-section area of the core plate, the limiting condition of local buckling may not be satisfied and it causes instability and loss of strength of the BRB. In this study, a parametric investigation for BRBs with different formations has been performed to verify the effect of the design parameters such as different core section profiles, restraining member width to thickness ratio and relative cross-sectional area of core to restrained, on buckling load evaluation. The proposed BRB investigation results have been also presented comparing to the ones obtained by past researches on BRBs with plate profile as the core section and the advantages and disadvantages of this configuration has been discussed and it is concluded that BRBs with tubular core section exhibit a better seismic performance than the ones with plate core profile.
Analyzing the parameters influence the Concrete Compressive Strength by adaptive neuro-fuzzy technique :
High consumption of cement in mixing plans causes to increase production costs and adverse environmental effects. In this study, the main aim is to analyze the impact of different variables on the Concrete Compressive Strength prediction.
At first, with the intention of finding resources economically, more efficient and environmentally friendly, natural pumice Pozzolan powder and molten overburden have been studied in the binary plans 10 to 50 percent replacement and to test fresh concrete properties, mechanical properties, and durability. As a natural Pozzolan, pumice requires no sophisticated equipment to prepare to use, and just it needs to become the powder. Because of the ease in preparation, pumice is the basis of this research. In order to validate the results, in addition, to control samples of each project, binary fly ash known as a powder is tested. Afterward, ternary pumice and silica fumes are investigated to strengthen the binary results. Later using the results, Adaptive neuro-fuzzy inference system (ANFIS) was used in order to determine the parameters influence the Concrete Compressive Strength. The variable selection process was used to select the most dominant factors which affect the Concrete Compressive Strength.
This paper is aimed to develop and verify a novel hybrid intelligent model, which is indeed a new version of GMDH algorithm and named the generalized structure of GMDH (GS-GMDH) to solve engineering problems. The proposed GS-GMDH model has validated its capability of predicting blast-induced ground vibration, very important safety issues in the mining industry. For this regard, a data set with a total of 96 samples were gathered from a blasting site in Shur River Dam region, Iran. Among them, 67 and 29 samples were used for constructing and testing the model, respectively. To check the accuracy and robustness of the proposed algorithm, the values of the performance evaluation measures, i.e., R2, the variance accounted for (VAF), mean absolute error, root mean square error, scatter index, and Nash–Sutcliffe model efficiency (EN–S), were used. The results showed the efficiency of the GS-GMDH algorithm in the prediction of the blast-induced ground vibration. It was also confirmed that the proposed algorithm can be applied effectively to solving/predicting the engineering problems and it has also the potential to be generalized to other fields.
The estimation of moment and rotation in steel rack connections could be significantly helpful parameters for designers and constructors in the initial designing and construction phases. Accordingly, Extreme Learning Machine (ELM) has been optimized to estimate the moment and rotation in steel rack connection based on variable input characteristics as beam depth, column thickness, connector depth, moment and loading. The prediction and estimating of ELM has been juxtaposed with genetic programming (GP) and artificial neural networks (ANNs) methods. Test outcomes have indicated a surpass in accuracy predicting and the capability of generalization in ELM approach than GP or ANN. Therefore, the application of ELM has been basically promised as an alternative way to estimate the moment and rotation of steel rack connection. Further particulars are presented in details in results and discussion.