Analytical asssessment of the structural behavior of a specific composite floor system at elevated temperatures using a newly developed hybrid intelligence method

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures

Sustainable design of self-consolidating green concrete with partial replacements for cement through neural-network and fuzzy technique

In order to achieve a sustainable mix design, this paper evaluates self-consolidating green concrete (SCGC) properties by experimental tests and then examines the design parameters with an artificial intelligence technique. In this regard, cement was partially replaced in different contents with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Moreover, fresh and hardened properties tests were performed on the specimens. Finally, an adaptive neuro-fuzzy inference system (ANFIS) was developed to identify the influencing parameters on the compressive strength of the specimens. For this purpose, seven ANFIS models evaluated the input parameters separately, and in terms of optimization, twenty-one models were assigned to different combinations of inputs. Experimental results were reported and discussed completely, where furnace slag represented the most effect on the hardened properties in binary mixes, and volcanic powder played an effective role in slump retention among other cement replacements. However, the combination of micro-silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs. Furthermore, ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC. Finally, when compared with other additive powders, the combination of micro-silica with volcanic powder provided the most strength, which has also been verified and reported by the test results

[In Press] Efficient machine-learning algorithm applied to predict the transient shock reaction of the elastic structure partially rested on the viscoelastic substrate

Due to numerous applications of piezoelectric materials in the modern technologies, this study assesses the thermomechanical shock behavior of the functionally graded graphene platelets reinforced nanocomposite (FG-GPLRN) annular plate surrounded by two piezoelectric layers and partially rested on the viscoelastic substrate for different cases of boundary conditions by metaheuristic optimized machine-learning methods, for the first time. Thermal and mechanical shocks are simultaneously applied on the upper surface of the mentioned structure. Governing equations of the system are formulated in the background of three-dimensional (3D) elasticity theory. Energy balance of the system is considered based on the Lord-Shulman theorem. Differential quadrature method (DQM) is selected as the main solver to determine the spatial response of the system from the state-space form of the governing differential equations. Additionally, Laplace transform is collaborated with modified Dubner and Abate’s approach to predict the temporal response of the system. Accuracy of the applied methods is carefully examined and verified through comparative study performed between the current results and those determined in the published high-quality studies. Valuable outcomes of the current approach would be directly employed in designing process of similar structures interacting with probable thermomechanical shocks

[In Press] Application of artificial intelligence technique in optimization and prediction of the stability of the walls against wind loads in building design

The present study is done to perform the optimal design of the structural components of the buildings against the unwanted wind load exerted on their outer face. To this end, the case study of the research is the outer wall made of a one-floor building modeled as a rectangular plate with only one free edge and three clamped ones. It is assumed that the wall is a sandwich plate whose core is made of auxetic material and dace-sheets are reinforced with nanoparticles of graphene platelets (GPL). Differential equations governing the system’s motion are obtained within the background of the plate’s shear-deformation theories. The stability analysis of the sandwich wall is performed based on the application of artificial intelligence (AI) methods optimized with an innovative optimization approach to gain a high level of accuracy. To determine the stability information of the system at the train points, the differential quadrature approach (DQA) is applied as the solver of differential equations of motion. The accuracy of the methods used in this paper is examined and verified by comparing the results with those acquired in the articles published previously. The results obtained in this study provide very useful information about the stability response of lightweight building components through AI-based solutions

Effect of pumice powder and nano-clay on the strength and permeability of fiber-reinforced pervious concrete incorporating recycled concrete aggregate

Pervious concrete (PC) has been widely used to construct concrete pavements and to increase the permeable surfaces all over the world. With the boost in the use of PC, it is necessary to make it more environmental-friendly and cost efficient. This study investigates employing recycled concrete aggregate (RCA) and pozzolanic additives as a partial replacement (PR) of natural coarse aggregate (NCA) and Portland cement, respectively. For this purpose, the NCA was replaced with 10%, 25%, 50% and 100% RCA and the Portland cement was replaced with 10%, 25% and 50% pumice used in combination with 1–3% nano-clay (NC). The compressive and flexural strengths, void content, density, and permeability of concrete were evaluated. Moreover, the effect of adding three different types of fibers including steel fiber (STF), macro-fiber (MF), and waste plastic fiber (WPF) at volume fractions of 1% and 2% on the properties of concrete was studied. A total number of 7791 specimens from 371 mixtures were cast and tested. Using RCA decreased density, compressive strength (CS) (up to 58%) and flexural strength (FS) (up to 64%) and increased the void content and permeability (up to 15%) of concrete. The use of pumice generally reduced the early-age strength of concrete; however, using 10–25% pumice increased the mechanical strength at 90 days. Incorporating 1–3% NC also had positive effects on the strength properties and led to a minor reduction in permeability. STF performed better than MF and WPF, and adding 1% STF, MF, and WPF increased the 180-day FS of RC25 by 78.9%, 67.4% and 37.1%, respectively. The effectiveness of fibers declined with the increase in RCA content, which could be related to the poor compaction of concrete. According to the test results, the 90-day CS of mix RC50Pu25 with 2% STF and mix RC100Pu10NC1 was equal to the control mix. Therefore, it sounds that it is a feasible approach to significantly reduce the consumption of NCA and cement by using specific dosages of the other materials used in this study