In this research, a mathematical derivation is made to develop a nonlinear dynamic model for the nonlinear frequency and chaotic responses of the multi-scale hybrid nano-composite reinforced disk in the thermal environment and subject to a harmonic external load. Using Hamilton’s principle and the von Karman nonlinear theory, the nonlinear governing equation is derived. For developing an accurate solution approach, generalized differential quadrature method (GDQM) and perturbation approach (PA) are finally employed. Various geometrically parameters are taken into account to investigate the chaotic motion of the viscoelastic disk subject to harmonic excitation. The fundamental and golden results of this paper could be that in the lower value of the external harmonic force, different FG patterns do not have any effects on the motion response of the structure. But, for the higher value of external harmonic force and all FG patterns, the chaos motion could be seen and for the FG-X pattern, the chaosity is more significant than other patterns of the FG. As a practical designing tip, it is recommended to choose plates with lower thickness relative to the outer radius to achieve better vibration performance.

Chaotic simulation of the multi-phase reinforced thermo-elastic disk using GDQM

Full-face tunnel boring machine (TBM) is a modern and efficient tunnel construction equipment. A reliable and accurate TBM performance (like penetration rate, PR) prediction can reduce the cost and help to select the appropriate construction method. Therefore, this study introduces a new hybrid intelligence technique, i.e., grey wolf optimizer-feature weighted-multiple kernel-support vector regression (GWO-FW-MKL-SVR) to predict TBM PR. For this purpose, a tunnel in China was selected as a case study and the most important parameters on TBM performance, i.e., chamber earth pressure, total thrust, cutterhead torque, cutterhead speed, cohesion, internal friction angle, compression modulus, the ratio of boulder, uniaxial compressive strength and rock quality designation, were measured and considered as model inputs. To show the capability of the GWO-FW-MKL-SVR model, three models including biogeography-based optimization (BBO)-FW-MKL-SVR, MKL-SVR, and SVR were also proposed to predict the TBM PR. To select the best predictive models, some performance indices, i.e., coefficient of determination (R2), root mean square error (RMSE) and variance accounted for (VAF) were considered and calculated. The obtained results showed that the GWO-FW-MKL-SVR model receives the highest accuracy in predicting the TBM PR for both train and test stages. R2 values of 0.946 and 0.894, for train and test stages of the GWO-FW-MKL-SVR model, respectively, confirmed that this new hybrid model is considered as a powerful, applicable and simple technique in predicting the TBM PR. By performing feature weight analysis, it was found that the effects of the uniaxial compressive strength, rock quality designation and cutterhead speed features were higher than the other input parameters on the TBM PR.

A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance

Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect on geothermal applications. Since the thermal conductivity is influenced by multiple variables including soil type and mineralogy, dry density, and water content, its precise prediction becomes a challenging problem. In this study, novel computational approaches including hybridisation of two metaheuristic optimisation algorithms, i.e. firefly algorithm (FF) and improved firefly algorithm (IFF), with conventional machine learning techniques including extreme learning machine (ELM), adaptive neuro-fuzzy interface system (ANFIS) and artificial neural network (ANN), are proposed to predict the thermal conductivity of unsaturated soils. FF and IFF are used to optimise the internal parameters of the ELM, ANFIS and ANN. These six hybrid models are applied to the dataset of 257 soil cases considering six influential variables for predicting the thermal conductivity of unsaturated soils. Several performance parameters are used to verify the predictive performance and generalisation capability of the developed hybrid models. The obtained results from the computational process confirmed that ELM-IFF attained the best predictive performance with a coefficient of determination = 0.9615, variance account for = 96.06%, root mean square error = 0.0428, and mean absolute error = 0.0316 on the testing dataset (validation phase). The results of the models are also visualised and analysed through different approaches using Taylor diagrams, regression error characteristic curves and area under curve scores, rank analysis and a novel method called accuracy matrix. It was found that all the proposed hybrid models have a great ability to be considered as alternatives for empirical relevant models. The developed ELM-IFF model can be employed in the initial stages of any engineering projects for fast determination of thermal conductivity.

A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil

Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes. In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla watershed in northern Pakistan by integrating particle swarm optimization (PSO) and grey wolf optimization (GWO) with extreme learning machine (ELM) as ELM-PSOGWO. The proposed method was compared with the standalone ELM, hybrid of ELM-PSO, and binary hybrid PSOGSA (hybrid of PSO with gravitational search algorithm) methods. Monthly precipitation and runoff data were used as inputs to the models to examine their accuracy in terms of different statistical indexes. Test results showed that the proposed ELM-PSOGWO provided more accurate results than the standalone ELM, hybrid ELM-PSO, ELM-GWO nd binary hybrid PSOGSA methods in monthly runoff prediction. ELM-PSOGWO reduced the RMSE in prediction of ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA by 38.2, 22.8, 22.4 and 16.7%, respectively. The PSO and GWO based ELM models also performed better than standalone ELM models, with an improvement in RMSE by 19.9 to 20.3%, respectively. Results also showed that adding precipitation as input enhanced the prediction accuracy of models. ELM-PSOGWO was also able to provide more precise estimates of peak runoff with the lowest absolute mean relative error compared to other methods. The results indicate the potential of ELM-PSOGWO model to be recommended for monthly runoff prediction.

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization

Alkali-activated slag (AAS) paste: Correlation between durability and microstructural characteristics

Compressive strength of concrete is one of the most determinant parameters in the design of engineering structures. This parameter is generally determined by conducting several tests at different ages of concrete in spite of the fact that such tests are not only costly but also time-consuming. As an alternative to these tests, machine learning (ML) techniques can be used to estimate experimental results. However, the dependence of compressive strength on different parameters in the fabrication of concrete makes the prediction problem challenging, especially in the case of concrete with partial replacements for cement. In this investigation, an extreme learning machine (ELM) is combined with a metaheuristic algorithm known as grey wolf optimizer (GWO) and a novel hybrid ELM-GWO model is proposed to predict the compressive strength of concrete with partial replacements for cement. To evaluate the performance of the ELM-GWO model, five of the most well-known ML models including an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), an extreme learning machine, a support vector regression with radial basis function (RBF) kernel (SVR-RBF), and another SVR with a polynomial function (Poly) kernel (SVR-Poly) are developed. Finally, the performance of the models is compared with each other. The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model. Also, it is deducted that the ELM-GWO model is capable of reaching superior performance indices in comparison with those of the other models.

A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement

The reduction of the moisture content of concrete during the drying process reduces the concrete's volume and causes it to shrink. In general, concrete shrinkage is a phenomenon that causes concrete volume to dwindle and can lead to durability problems. There are different types of this phenomenon, among them chemical shrinkage, autogenous shrinkage, drying shrinkage including free shrinkage and restrained shrinkage, and thermal contraction. Shrinkage-reducing admixtures are commercially available in different forms. The present study investigates the effect of liquid propylene glycol ether on mechanical properties and free shrinkage induced by drying at different water-cement (w/c) ratios. Furthermore, the effect of shrinkage-reducing admixtures on the properties of hardened concrete such as compressive and tensile strength, electrical resistivity, modulus of elasticity, free drying shrinkage, water absorption, and depth of water penetration was investigated. The results indicated that shrinkage reducing agents performed better in a low w/c ratio and resulted in up to 50% shrinkage reduction, which was due to the surface reduction of capillary pores. The prediction of free shrinkage due to drying was also performed using an artificial neural network.

https://www.mdpi.com/1996-1944/13/24/5721/pdf

Effect of Shrinkage Reducing Admixture on Drying Shrinkage of Concrete with Different w/c Ratios

Shrinkage is the volume change of concrete without influence of external forces. Drying shrinkage, which occurs after the concrete hardening, is one of the most important causes of the cracking in concrete members. In addition to adverse effects on the appearance of concrete, cracks may reduce the strength and durability by increasing the permeability of concrete and facilitate the entry of aggressive agents into it. Free and restrained drying shrinkage are the most important types of shrinkage. The present study investigated the effect of two admixtures—including damp proofing and expansive materials— on compressive strength, tensile strength, electrical resistivity, modulus of elasticity, unrestrained and restrained shrinkage and water absorption of concrete specimens. In addition, the effect of admixtures on crack properties was investigated. The cracks properties, which occurred in unrestrained shrinkage test, such as average and maximum crack width and total cracks area, were computed by image processing. The results showed that the use of damp proofing additive, (i.e. calcium stearate) reduced maximum free drying shrinkage by 42.4%, maximum restrained drying shrinkage by 22.8%, maximum crack width by 51% and final crack area by 21%. Moreover, the use of expansive additive (i.e. aluminum powder) almost eliminated the free shrinkage, but on average, it increased the restrained drying shrinkage by 18% and reduced the maximum crack width and the final crack area by 2.67 and 27%, respectively.

Effect of calcium stearate and aluminum powder on free and restrained drying shrinkage, crack characteristic and mechanical properties of concrete

Investigating the Effectiveness of the Stable Measurement Tests of Self-Compacting Concrete

Abstract. Eccentrically braced frames are one of the most popular systems in buildings because they provide both high stiffness and ductility to the structure. Other systems such as moment frames and concentrically braced frames do not usually provide desirable stiffness and ductility, respectively. Steel shear walls are also popular systems in steel buildings; however, they can be expensive due to the large amount of steel used in these systems. Therefore, it is of interest to investigate new types of eccentrically braced frames. In this paper a truss-shaped brace is proposed and its behavior under cyclic loading in an eccentrically braced frame is numerically investigated using finite element software. Different cross-sections are implemented in the trussshaped brace and the effect of the cross-section on the behavior of the frame is studied and compared to the reference specimen with conventional configuration. The results of this study show that hollow square cross-section with 100 mm width and 4 mm thickness had the best performance in terms of strength, absorbed energy and pinching compared to other specimens.

Fazel Azarhomayun

Papers

A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement

Effect of Shrinkage Reducing Admixture on Drying Shrinkage of Concrete with Different w/c Ratios

Effect of calcium stearate and aluminum powder on free and restrained drying shrinkage, crack characteristic and mechanical properties of concrete

Investigating the Effectiveness of the Stable Measurement Tests of Self-Compacting Concrete

Numerical investigation of truss-shaped braces in eccentrically braced steel frames