Load-Deflection Behaviour of Frp Concrete Composite Deck

Nowadays, Fiber Reinforced Polymer (FRP) concrete composite bridge deck system hasbeen introduced because of its light-weight and durability. Strong composition is neededbetween FRP and concrete to acquire the structural composite behavior of FRP concretecomposite deck. FRP has unique properties that, if disregarded, can lead to failure duringoperation. However, when these same unique properties are taken into advantages, they canprovide the engineers with a system superior to traditional metallic materials. This studyinvestigates analytically the deflection behavior of FRP concrete composite deck using shearconnectors under flexural loading. Finite element software (LUSAS) is used to model FRPcomposite deck. For this purpose, LUSAS has introduced some elements. Volume elementsare utilized to model concrete and Glass Fiber Reinforced Polymer (GFRP) section. Meshingelements are necessary in finite element in order to act as a member in modeling. 3D solidcontinuum elements are used to mesh the sample. Five GFRP module having differentthicknesses of 8mm, 9.6mm, 11.2mm, 12.8mm and 16mm are taken to analyze. Results showthat the thicknesses of GFRP module have significant effect on the ultimate load anddeflection of the deck. Once the thickness of GFRP section increased, the deflection at midspan decreased and the ultimate load increased accordingly. Furthermore, results revealed theappropriate interface material between FRP and concrete in finite element modeling. In orderto get an effective interface element, about 40 numerical models have been analyzed. Theresults were compared with experimental study. Inserted data for verified model in LUSASwere demonstrated as an appropriate interface element between FRP and concrete

Seismic Analysis of Earth Slope Using a Novel Sequential Hybrid Optimization Algorithm

One of the most important topics in geotechnical engineering is seismic analysis of the earth slope. In this study, a pseudo-static limit equilibrium approach is applied for the slope stability evaluation under earthquake loading based on the Morgenstern–Price method for the general shape of the slip surface. In this approach, the minimum factor of safety corresponding to the critical failure surface should be investigated and it is a complex optimization problem. This paper proposed an effective sequential hybrid optimization algorithm based on the tunicate swarm algorithm (TSA) and pattern search (PS) for seismic slope stability analysis. The proposed method employs the global search ability of TSA and the local search ability of PS. The performance of the new CTSA-PS algorithm is investigated using a set of benchmark test functions and the results are compared with the standard TSA and some other methods from the literature. In addition, two case studies from the literature are considered to evaluate the efficiency of the proposed CTSA-PS for seismic slope stability analysis. The numerical investigations show that the new approach may provide better optimal solutions and outperform previous methods

Concrete wedge and coarse sand coating shear connection system in GFRP concrete composite deck

In recent years, Glass Fiber-Reinforced Polymer (GFRP) has become a widely used material in Fiber-Reinforced Polymer (FRP) composite structures. Several studies have been performed on the bonding methods of FRP sheets and plates, but limited research has been undertaken on the critical shear connection systems for innovative GFRP concrete composite bridge decks structures. Coarse sand coating shear connection systems for GFRP structures show strong bondage in the shear direction but poor grip in the normal direction. An innovative concrete wedge system, supplementary to coarse sand coating overcomes the normal split between GFRP panel and concrete, in composite bridge deck structures. This study presents a finite element (FE) investigation on GFRP concrete composite deck using a concrete wedge shear connection system based on existing experimental evaluation. In this research, the thickness of the GFRP module was varied and the deflection behaviour of GFRP concrete composite deck was furthermore studied. FE results indicate that thickness increments of the GFRP module significantly reduce the mid-span deflection of the composite deck and subsequently increase the ultimate load. In order to investigate the interaction behaviour between GFRP and concrete in the numerical analysis, a structural interface element is proposed for finite element modelling (FEM) analysis. In order to undertake a rapid evaluation of deflection of the composite deck, an equation is proposed to estimate the deflection at the mid-span of the GFRP composite bridge deck based on the FEM results. In addition, the GFRP composite bridge deck was numerically analysed using light-weight concrete (LWC) and results were compared with GFRP composite deck with conventional concrete. The results indicate that using LWC increases the ultimate load proportionally till failure

Iterative Finite Element Analysis of Concrete-Filled Steel Tube Columns Subjected to Axial Compression

Since laboratory tests are usually costly, simulating methods using computers are always under the spotlight. This study performed a finite element analysis (FEA) using iterative solutions for simulating circular and square concrete-filled steel tube (CFST) columns infilled with high-strength concrete and reinforced with a cross-shaped plate (comprising two plates along the columns that divide the hollow columns into four equal sections) with and without opening. For this reason and for validation purposes, the columns had length of 900 mm, width/diameter of 150 mm and wall thickness of 3 mm. In this study, unlike in some other studies, the cross-shaped plate was assumed to be fixed at the top and the bottom of a column, and the columns were subjected to axial compression pointed in the center. The outcomes revealed that the cross-shaped plate could improve the axial strength of both circular and square CFST columns; however, the structural performance of the square CFST columns changed: local outward buckling was observed after inserting the cross-shaped plate. By inserting an opening on the cross-shaped plate, the bearing capacity of the circular CFST columns was further improved, while the square CFST columns experienced a decline in their ultimate bearing capacity compared with the corresponding models without the opening. The lateral deflection also improved for the circular CFST columns by adding the reinforcement. However, for the square CFST columns, while it initially improved, increasing the thickness of the cross-shaped plate inversely influenced the lateral deflection of the square CFST columns. The results were also compared with some available codes, and a good agreement was achieved with those outcomes

Iterative Finite Element Analysis of Concrete-Filled Steel Tube Columns Subjected to Axial Compression

Since laboratory tests are usually costly, simulating methods using computers are always under the spotlight. This study performed a finite element analysis (FEA) using iterative solutions for simulating circular and square concrete-filled steel tube (CFST) columns infilled with high-strength concrete and reinforced with a cross-shaped plate (comprising two plates along the columns that divide the hollow columns into four equal sections) with and without opening. For this reason and for validation purposes, the columns had length of 900 mm, width/diameter of 150 mm and wall thickness of 3 mm. In this study, unlike in some other studies, the cross-shaped plate was assumed to be fixed at the top and the bottom of a column, and the columns were subjected to axial compression pointed in the center. The outcomes revealed that the cross-shaped plate could improve the axial strength of both circular and square CFST columns; however, the structural performance of the square CFST columns changed: local outward buckling was observed after inserting the cross-shaped plate. By inserting an opening on the cross-shaped plate, the bearing capacity of the circular CFST columns was further improved, while the square CFST columns experienced a decline in their ultimate bearing capacity compared with the corresponding models without the opening. The lateral deflection also improved for the circular CFST columns by adding the reinforcement. However, for the square CFST columns, while it initially improved, increasing the thickness of the cross-shaped plate inversely influenced the lateral deflection of the square CFST columns. The results were also compared with some available codes, and a good agreement was achieved with those outcomes

Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns

The type of materials used in designing and constructing structures significantly affects the way the structures behave. The performance of concrete and steel, which are used as a composite in columns, has a considerable effect upon the structure behavior under different loading conditions. In this paper, several advanced methods were applied and developed to predict the bearing capacity of the concrete-filled steel tube (CFST) columns in two phases of prediction and optimization. In the prediction phase, bearing capacity values of CFST columns were estimated through developing gene expression programming (GEP)-based tree equation; then, the results were compared with the results obtained from a hybrid model of artificial neural network (ANN) and particle swarm optimization (PSO). In the modeling process, the outer diameter, concrete compressive strength, tensile yield stress of the steel column, thickness of steel cover, and the length of the samples were considered as the model inputs. After a series of analyses, the best predictive models were selected based on the coefficient of determination (R2) results. R2 values of 0.928 and 0.939 for training and testing datasets of the selected GEP-based tree equation, respectively, demonstrated that GEP was able to provide higher performance capacity compared to PSO–ANN model with R2 values of 0.910 and 0.904 and ANN with R2 values of 0.895 and 0.881. In the optimization phase, whale optimization algorithm (WOA), which has not yet been applied in structural engineering, was selected and developed to maximize the results of the bearing capacity. Based on the obtained results, WOA, by increasing bearing capacity to 23436.63 kN, was able to maximize significantly the bearing capacity of CFST columns

Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile

Prediction of pile-bearing capacity developing artificial intelligence models has been done over the last decade. Such predictive tools can assist geotechnical engineers to easily determine the ultimate pile bearing capacity instead of conducting any difficult field tests. The main aim of this study is to predict the bearing capacity of pile developing several smart models, i.e., neuro-genetic, neuro-imperialism, genetic programing (GP) and artificial neural network (ANN). For this purpose, a number of concrete pile characteristics and its dynamic load test specifications were investigated to select pile cross-sectional area, pile length, pile set, hammer weight and drop height as five input variables which have the most impacts on pile bearing capacity as the single output variable. It should be noted that all the aforementioned parameters were measured by conducting a series of pile driving analyzer tests on precast concrete piles located in Pekanbaru, Indonesia. The recorded data were used to establish a database of 50 test cases. With regard to data modelling, many smart models of neuro-genetic, neuro-imperialism, GP and ANN were developed and then evaluated based on the three most common statistical indices, i.e., root mean squared error (RMSE), coefficient determination (R2) and variance account for (VAF). Based on the simulation results and the computed indices’ values, it is observed that the proposed GP model with training and test RMSE values of 0.041 and 0.040, respectively, performs noticeably better than the proposed neuro-genetic model with RMSE values of 0.042 and 0.040, neuro-imperialism model with RMSE values of 0.045 and 0.059, and ANN model with RMSE values of 0.116 and 0.108 for training and test sets, respectively. Therefore, this GP-based model can provide a new applicable equation to effectively predict the ultimate pile bearing capacity

Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models

During design and construction of buildings, the employed materials can substantially impact the structures’ performance. In composite columns, the properties and performance of concrete and steel have a significant influence on the behavior of structure under various loading conditions. In this study, two metaheuristic algorithms, particle swarm optimization (PSO) and competitive imperialism algorithm (ICA), were combined with the artificial neural network (ANN) model to predict the bearing capacity of the square concrete-filled steel tube (SCFST) columns. To achieve this objective and investigate the performance of optimization algorithms on the ANN, one of the most extensive datasets of pure SCFST columns (with 149 data samples) was used in the modeling process. In-depth and detailed predictive modeling of metaheuristic-based models was conducted through several parametric investigations, and the optimum factors were designed. Furthermore, the capability of these hybrid models was assessed using robust statistical matrices. The results indicated that PSO is stronger than ICA in finding optimum weights and biases of ANN in predicting the bearing capacity of the SCFST columns. Therefore, each column and its bearing capacity can be well-predicted using the developed metaheuristic-based ANN model