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In this paper, an empirical model based on self-evolving neural network is proposed for predicting the flexural behavior of ferrocement elements. The model is meant to serve as a simple but reliable tool for estimating the moment capacity of ferrocement members. The proposed model is trained and validated using experimental data obtained from the literature. The data consists of information regarding flexural tests on ferrocement specimens which include moment capacity and cross-sectional dimensions of specimens, concrete cube compressive strength, tensile strength and volume fraction of wire mesh. Comparisons of predictions of the proposed models with experimental data indicated that the models are capable of accurately estimating the moment capacity of ferrocement members. The proposed models also make better predictions compared to methods such as the plastic analysis method and the mechanism approach. Further comparisons with other data mining techniques including the back-propagation network, the adaptive spline, and the Kriging regression models indicated that the proposed models are superior in terms prediction accuracy despite being much simpler models. The performance of the proposed models was also found to be comparable to the GEP-based surrogate model.

Steel and steel-concrete composite girders are two types of girders commonly used for long-span bridges. However, practice has shown that the two types of girders have some drawbacks. For steel girders, the orthotropic steel deck (OSD) is vulnerable to fatigue cracking and the asphalt overlay is susceptible to damage such as rutting and pot holes. While for steel-concrete composite girders, the concrete deck is generally thick and heavy, and the deck is prone to cracking because of its low tensile strength and high creep. Thus, to improve the serviceability and durability of girders for long-span bridges, three new types of steel-UHPC lightweight composite bridge girders are proposed, where UHPC denotes ultra-high performance concrete. The first two types consist of an OSD and a thin UHPC layer while the third type consists of a steel beam and a UHPC waffle deck. Due to excellent mechanical behaviors and impressive durability of UHPC, the steel-UHPC composite girders have the advantages of light weight, high strength, low creep coefficient, low risk of cracking, and excellent durability, making them competitive alternatives for long-span bridges. To date, the proposed steel-UHPC composite girders have been applied to 14 real bridges in China. It is expected that the application of the new steel-UHPC composite girders on long-span bridges will have a promising future.

This study examines the properties of fiber-reinforced reactive powder concrete (FR-RPC). Steel fibers, glass fibers, and steel-glass hybrid fibers were used to prepare the FR-RPC. The non-fibrous reactive powder concrete (NF-RPC) was prepared as a reference mix. The proportion of fibers by volume for all FR-RPC mixes was 1.5%. Steel fibers of 13 mm length and 0.2 mm diameter were used to prepare the steel fiber-reinforced RPC (SFR-RPC). Glass fibers of 13 mm length and 1.3 mm diameter were used to prepare the glass fiber-reinforced RPC (GFR-RPC). The hybrid fiber-reinforced RPC (HFR-RPC) was prepared by mixing 0.9% steel fibers and 0.6% glass fibers. Compressive strength, axial load-axial deformation behavior, modulus of elasticity, indirect tensile strength, and shear strength of the RPC mixes were investigated. The results showed that SFR-RPC achieved higher compressive strength, indirect tensile strength and shear strength than NF-RPC, GFR-RPC, and HFR-RPC. Although the compressive strengths of GFR-RPC and HFR-RPC were slightly lower than the compressive strength of NF-RPC, the shear strengths of GFR-RPC and HFR-RPC were higher than that of NF-RPC.

In the present contribution, operational modal analysis in conjunction with bees optimization algorithm are utilized to update the finite element model of a solar power plant structure. The physical parameters which required to be updated are uncertain parameters including geometry, material properties and boundary conditions of the aforementioned structure. To determine these uncertain parameters, local and global sensitivity analyses are performed to increase the solution accuracy. An objective function is determined using the sum of the squared errors between the natural frequencies calculated by finite element method and operational modal analysis, which is optimized using bees optimization algorithm. The natural frequencies of the solar power plant structure are estimated by multi-setup stochastic subspace identification method which is considered as a strong and efficient method in operational modal analysis. The proposed algorithm is efficiently implemented on the solar power plant structure located in Shahid Chamran university of Ahvaz, Iran, to update parameters of its finite element model. Moreover, computed natural frequencies by numerical method are compared with those of the operational modal analysis. The results indicate that, bees optimization algorithm leads accurate results with fast convergence.

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