•The compressive strength of lightweight self-compacting concrete was modelled intelligently.•Beetle antennae search algorithm was firstly used to tune hyper-parameters of random forest.•The ...importance of different input variables was measured.
Self-compacting concrete (SCC) can achieve compaction into every part of the formwork through its own weight without any segregation of the coarse aggregate. Lightweight concrete (LWC) can reduce the dead load of the structure by incorporating the lightweight aggregate (LWA). In recent years, more and more studies have focused on combining the advantages of SCC and LWC to produce lightweight self-compacting concrete (LWSCC). As one of the most important mechanical properties, uniaxial compressive strength (UCS) values need to be tested before field application of this new material. However, conducting UCS tests with multiple influencing variables is time-consuming and costly. To address this issue, this paper proposed, for the first time, a beetle antennae search (BAS) algorithm based random forest (RF) model to accurately and effectively predict the UCS of LWSCC. This model was developed and verified using data from LWSCC laboratory formulation. Results show that BAS was efficient in searching the optimum hyper-parameters of RF. The proposed BAS-RF model achieved high predictive accuracy indicated by a high correlation coefficient (0.97). In addition, by measuring the variable importance, we conclude that temperature was the most sensitive to UCS development, followed by scoria content and water-to-binder (w/b) ratio, while UCS was less sensitive to fiber content. This pioneering work provides a simple and convenient method for evaluating UCS of LWSCC at varying temperatures.
•BPNN has good prediction accuracy for UCS, while RF performs better in predicting slump.•PSO is efficient in tuning hyperparameters of machine learning models.•The Pareto front of the mixture ...optimization problem is obtained by MOPSO.
For the optimization of concrete mixture proportions, multiple objectives (e.g., strength, cost, slump) with many variables (e.g., concrete components) under highly nonlinear constraints need to be optimized simultaneously. The current single-objective optimization models are not applicable to multi-objective optimization (MOO). This study proposes an MOO method based on machine learning (ML) and metaheuristic algorithms to optimize concrete mixture proportions. First, the performances of different ML models in the prediction of concrete objectives are compared on data sets collected from the published literature. The winner is selected as the objective function for the optimization procedure. In the optimization step, a multi-objective particle swarm optimization algorithm is used to optimize mixture proportions to achieve optimal objectives. The results show that the backpropagation neural network has better performance on continuous data (e.g., strength), whereas the random forest algorithm has higher prediction accuracy on more discrete data (e.g., slump). The Pareto fronts of a bi-objective mixture optimization problem for high-performance concrete and a tri-objective mixture optimization problem for plastic concrete are successfully obtained by the MOO model. The MOO model can serve as a design guide to facilitate decision-making before the construction phase.
•Aligned fiber reinforced cementitious composite was proposed for 3D printing.•Mechanical behaviors of 3D printed samples exposed to various loadings were tested.•Mesoscale structures of printing ...material were detected through CT scanning.•Mechanical and acoustic indexes are proposed to evaluate the anisotropic properties.•Empirical relationships between the mechanical anisotropic properties and ultrasonic signals were established.
3D printing techniques are being researched extensively in the construction sector. However, the key issue lies in the development of cementitious materials with both favorable printability and enough mechanical capability by means of high strength and ductility. In this study, an optimal basalt fiber content was determined basing firstly on suitable printability and then on mechanical performance. A self-developed 3D printer was used for extrusion of the cementitious material and also for mechanical enhancement of fiber alignment along the print direction by keeping the nozzle diameter smaller than the length of the basalt fiber. The printing process deposits directional filaments, intrinsically resulting in laminated structures and mechanical anisotropy. Anisotropic performances of the printed material were evaluated by direction-based mechanical performance testing and confirmed by ultrasonic pulse velocity testing. The mechanical behaviors of 3D printed samples exposed to compressive, tensile, flexural and shearing loadings were experimentally investigated. The mesoscale structures of printed samples were detected through the advanced CT scanning technique. Both mechanical and acoustic indexes were proposed to evaluate the anisotropic properties of printed materials. In particular, empirical relationships between the mechanical anisotropic properties and ultrasonic signals were established. On the microstructural level, mechanical enhancement of fiber alignment, fiber pullout and fiber fracture were all probed through scanning electron microscope (SEM) imaging.
A micro-mechanical based numerical manifold method (NMM) is proposed in this study to investigate the micro-mechanisms underlying rock macroscopic response and fracture processes. The Voronoi ...tessellation technique is adopted to create randomly-sized polygonal rock micro-grains. A rock micro-grain based broken criterion is proposed and a corresponding grain breaking technique is developed. To better represent the contact behavior of rock grain bonds, a cohesive fracture model that considers tensile, shear and compressive behaviors together, is adopted to interpret the failure of rock grain bonds. The developed program is first validated by reproducing biaxial tests of Transjurane sandstone. Finally, the influences of micro-parameters on the rock macroscopic response and failure modes are investigated. The results show that the developed micro-based model can mimic the deformation and failure characteristics of the test closely. A parameter study shows that the grain contact cohesion has significant effects on the model uniaxial compressive strength. The fracture process and failure mode of rock are dependent on the ratio of grain contact shear stiffness to normal stiffness. With the increase of the contact stiffness ratio, the failure mode of rock under uniaxial compression changes from a diffuse pattern to a concentrated shear band.
•A micro-mechanical based NMM is proposed.•Voronoi tessellation technique is used to create the polygonal rock grains.•A rock micro-grain based broken criterion is proposed.•A cohesive fracture model is adopted to model the failure of rock grain bonds.•Influences of micro-parameters on rock macroscopic response are studied.
In order to increase the strength and lower the cost, this study experimentally investigated the effects of hooked-end steel fiber, basalt fiber and calcium sulfate on mechanical performance of ...polyvinyl alcohol (PVA) fiber-based engineered cementitious composite (ECC) containing high-volume fly ash. The uniaxial tensile, compression and four-point bending tests were carried out to characterize the mechanical behavior of each mixture. Scanning Electron Microscopy (SEM) image analysis and X-ray CT scan method were used to study the microstructures and distributions of pores. Test results showed that the ultimate strain capacity increases nearly linearly with the growth of stress performance index except the ECC reinforced with calcium sulfate and PVA. Hybridization with basalt fiber and PVA fiber or steel fiber and PVA fiber is more effective for improving mechanical performance within small deformation. ECC reinforced with calcium sulfate and PVA exhibits improved tensile, compressive and flexural strength while maintaining high ductility. The cost performance considering strength/cost and ductility/cost of hybrid mixtures was also discussed.
•Strength performance PVA-ECC containing high-volume fly ash was improved.•Strength of the PVA-ECC was improved while maintaining the inherent high ductility by the addition of calcium sulfate.•The macro and micro effects of the addition of steel fiber, basalt fiber and calcium sulfate into PVA-ECC was discussed.
In recent decades, self-compacting concrete has slowly gained popularity since its inception due to its unique ability to fill formworks with congested steel reinforcement and with little to no use ...of mechanical compaction required. Due to the environmental impacts associated with the natural aggregates in concrete production, a more sustainable approach in producing self-compacting concrete is to replace natural aggregates with that of recycled concrete aggregates from common construction waste and demolitions. This form of concrete provides a sustainable alternative in minimising the environmental damages associated with the extraction and depletion of natural resources. This experimental research aims to develop information about the fresh and hardened properties of different forms of self-compacting concrete by utilising recycled concrete aggregates in combination with recycled crumb rubber or lightweight scoria aggregates. The fresh properties were investigated in accordance with the guideline provided by the European federation national representing of concrete using the slump flow, T500, and J-ring tests. Hardened properties include 7 and 28 day compressive and tensile strengths, hardened density testing, and compressive stress-strain behaviour at 28 days. Optimal mix design of recycled concrete and crumb rubber aggregates self-compacting concrete are assessed to optimise fresh and hardened properties. The proposed SCC mixes are able to reduce amount of used cement to 40%. Aslo, as the percentage of recycled aggregate replacement increased, developed SCC mixes flowability and passing ability decreased.
Tensile failure behavior of concrete invariably dominates the behavior of concrete specimens as well as structural elements and it is strongly affected by loading rate. The present study focuses on ...the effects of loading rate and heterogeneity of meso-/micro-structure on the failure pattern and the macroscopic mechanical properties of concrete. For simplicity, concrete is regarded as a two-phase composite composed of aggregate and mortar matrix at meso-scale. The damaged plasticity theory combined with strain-rate effect is employed to describe the dynamic mechanical behavior of mortar matrix, and the aggregate phase is assumed to be elastic. The dynamic tensile failure modes of a single-edge notched concrete specimen and the L specimen under different loading rates are numerically investigated. The simulation results indicate that dynamic failure pattern and the direction of crack propagation of concrete have pronounced loading rate sensitivity. With the increase of loading rate, the failure mode of concrete changes from mode-I to mixed mode. The more complex the meso-structure is, the higher the interaction intensity between the meso components has and the more complicated the crack paths are, resulting in a more obvious crack branching behavior. Furthermore, as loading rate increases much more branching cracks generate within concrete and the width of the damaged region increases, implying that the fracture process at relatively high strain rates requires more energy demand to reach failure. And this should be the main reason for the improvement of the dynamic tensile strength of concrete.
•We study the effects of loading rate and heterogeneity on concrete failure mode.•We examine the failure modes of a single-edge notched specimen and L specimen.•Much more branching cracks generate with increasing loading rate.•The increase of energy dissipation causes the enhancement of concrete strength.
In the numerical simulations of highly fractured geological formations, discrete approaches are considerably promising and adequate to describe fluid flow in detail. However, the computational ...complexity increases dramatically with a greater number of fractures. This becomes the primary limitation for field-scale applications. In this study, a correlation index is for the first time introduced to evaluate the significance of individual fractures, and an equivalent model is proposed to mimic the original domain with a density-reduced one. By an equivalent permeability factor, the suggested model simplifies computational complexity, but compromises result precision to minor extent. This approach is validated in typical discrete fracture networks generated with stochastic fractal models. Effects of fracture geometry are discussed based on various distribution patterns. This method improves mesh quality when dealing with a fracture-matrix domain. It is also capable of optimizing reservoir design through fast and accurate estimations of gas productivity under different boundary conditions.
•A correlation index is defined to evaluate the significance of individual joints to overall transmissibility.•An equivalent fracture skeleton is derived based on the correlation index.•The effectiveness of equivalent models is discussed in different fracture patterns.•The discontinuity equivalence model is validated in fast and robust simulations of fractured-matrix systems.
•A novel method was proposed for predicting permeability and unconfined compressive strength of pervious concrete.•270 samples were prepared for building the dataset.•Permeable and mechanical ...properties of pervious concrete were elucidated.•Beetle antennae search was firstly used to tune the hyper-parameters of support vector regression.•The support vector regression model tuned by beetle antennae search algorithm has high prediction accuracy.
Pervious concrete is a widely used construction material thanks to its good drainage characteristics. Before application, its most important properties, i.e. the permeability coefficient (PC) and 28-day unconfined compressive strength (UCS) are required to be tested. However, conducting PC and UCS tests with multiple influencing variables is time-consuming and costly. To address this issue, this paper proposed, for the first time, an evolved support vector regression (ESVR) tuned by beetle antennae search (BAS) to accurately and effectively predict the PC and UCS of pervious concrete. To prepare the dataset of the ESVR model, 270 specimens in total were prepared and casted in a controlled environment in the laboratory. The water-to-cement (w/c) ratio, aggregate-to-cement (a/c) ratio, and aggregate size were selected as the crucial influencing variables for the inputs, while PC and UCS were the outputs of this model. The results indicate that both the PC and UCS firstly increased and then decreased with increasing w/c ratio. As the a/c ratio increased, PC increased, while UCS decreased. Moreover, BAS is more reliable and efficient than random hyper-parameter selection for hyper-parameter tuning. A low root-mean-square error (RMSE) and high correlation coefficient (R) indicate a relatively high predictive capability of the proposed ESVR model. The sensitivity analysis (SA) suggests the a/c ratio and aggregate size were the most sensitive variables for UCS and PC, respectively. This pioneering work provides a simple and convenient method for evaluating PC and UCS of pervious concrete.
Polymer matrix composites have generated a great deal of attention in recent decades in various fields due to numerous advantages polymer offer. The advancement of technology has led to stringent ...requirements in shielding materials as more and more electronic devices are known to cause electromagnetic interference (EMI) in other devices. The drive to fabricate alternative materials is generated by the shortcomings of the existing metallic panels. While polymers are more economical, easy to fabricate, and corrosion resistant, they are known to be inherent electrical insulators. Since high electrical conductivity is a sought after property of EMI shielding materials, polymers with fillers to increase their electrical conductivity are commonly investigated for EMI shielding. Recently, composites with nanofillers also have attracted attention due to the superior properties they provide compared to their micro counterparts. In this review polymer composites with various types of fillers have been analysed to assess the EMI shielding properties generated by each. Apart from the properties, the manufacturing processes and morphological properties of composites have been analysed in this review to find the best polymer matrix composites for EMI shielding.
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