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Properties Of Concrete

Liquid infiltration is an efficient way to densify carbon nanotubes (CNTs) and was used to strengthen CNT fibres in the method of array spinning. Rather than the volatility, the dipole moment of solvent plays a more important role in determining the densification level. The fibres densified by highly polar but non-volatile solvents such as N , N -dimethylformamide, dimethyl sulphoxide, and N -methyl-2-pyrrolidone were 100–200 MPa stronger than those by ethanol and acetone. Ethylene glycol is the most efficient solvent due to its two polar OH groups and improved the fibre strength to 1.45 GPa. Long chain or cross-linked polymers like polyvinyl alcohol, polyimide, and bismaleimide (BMI) were introduced into CNT fibres by infiltration with aid of polar solvents. These polymers reinforced the fibres significantly, as they can connect non-neighboring CNTs and benefit the load transfer. The strongest CNT/BMI fibre was 2.38 GPa in strength and 110 GPa in modulus.

Enhancement of carbon nanotube ﬁbres using diﬀerent solvents and polymers

To improve the security, robustness and imperceptibility of watermark schemes, a novel watermark scheme is devised by fusing multiple watermark techniques, including lifting wavelet transform, discrete cosine transform, discrete fractional angular transform and singular value decomposition. To solve the false positive problem in various SVD-based watermark schemes, transform domain encryption is utilized and the embedding component of watermark instead of the whole watermark is embedded into the host image. Furthermore, the particle swarm optimization algorithm is used to optimize the scaling factors and the parameter of the improved discrete fractional angular transform. The proposed watermark scheme is tested by several attacks, such as JPEG compression, low-pass filtering, Histogram equalization, contrast enhance, and etc. Simulation results demonstrate that the proposed watermark scheme is superior in the aspects of security, robustness and imperceptibility.

Secure and robust watermark scheme based on multiple transforms and particle swarm optimization algorithm

The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain. 

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Plastic bottles for chilled carbonated beverages as a source of microplastics and nanoplastics.

Ultrasonic testing analysis is a crucial scientific component during the process and interpretation of the ultrasonic detection signal. Focusing on the ultrasonic testing characteristics, the time-invariant spectral analysis method cannot meet the processing requirements of the detection signal fully. Thus, S transform, the time-varying analysis method, was introduced into the ultrasonic testing data processing of the concrete structure. The acoustic wave phase velocity was derived based on the spectrum analysis, and the S transform time-frequency analysis method was established. Finally, based on the method of concrete experimental data set, studies show that the frequency energy spectrum with S transform can realize flexible and effective identification of defects in the concrete structure. Definitely, this analysis method can significantly improve the resolution and practicality of ultrasonic testing.

/pdf/ultrasonic-testing-analysis-of-concrete-structure-based-on-s-2cvhxugwk1.pdf

Ultrasonic Testing Analysis of Concrete Structure Based on S Transform

The conventional design method of concrete mix ratio relies on a large number of tests for trial mixing and optimization, and the workload is massive. It is challenging to cope with today's diverse raw materials and the concrete's specific performance to fit to the development of modern concrete. For innovating the design method of concrete mix ratio and effectively using the various complex novel raw materials, the traditional mix ratio test method can be replaced with the intelligent optimization algorithm, and the concrete performance prediction can be realized rapidly and accurately. The mixed ratio of the rubber fiber concrete was designed with its 28d strength test. Then the range and variance analysis of the orthogonal test results was carried out to determine the optimal mix ratio and its influencing factors. The amount of rubber, rubber particle size, and polymer propylene fiber content and sand content, the four influencing factors, were used as the input variable. The 28d compressive, splitting tensile and flexural strength, three types of concrete strength, were used as output variables to establish a strength prediction model of the rubber fiber concrete based on the extreme learning machine (ELM). For verifying the ELM prediction model's performance, this paper has conducted a comparison experiment between this model and other intelligent algorithm models. The results show that the model has the advantages of high accuracy and high generalization ability compared with other algorithm models such as conventional neural networks. It can be used as an effective method for predicting the concrete performance. The method allows for the innovation and development of concrete mixing technology.

/pdf/prediction-of-rubber-fiber-concrete-strength-using-extreme-1s3ononwlv.pdf

Prediction of Rubber Fiber Concrete Strength Using Extreme Learning Machine

Data interpretation is the crucial scientific component that influences the inspection accuracy of ground penetrating radar (GPR). Developing algorithms for interpreting GPR data is a research focus of increasing interest. The problem of algorithms for interpreting GPR data is unresolved. To this end, this study proposes a sophisticated algorithm for interpreting GPR data with the aim of improving the inspection resolution. The algorithm is formulated by integrating variational mode decomposition (VMD) and Hilbert–Huang transform techniques. With this method, the intrinsic mode function of the GPR data is first produced using the VMD of the data, followed by obtaining the instantaneous frequency by using the Hilbert–Huang transform to analyze the intrinsic mode functions. The instantaneous frequency data can be decomposed into three frequency attributes, including frequency division section, time-frequency section, and space frequency section, which constitute a platform to gain insight into the nature of the GPR data, such that the inspected media components can be examined. The effectiveness of the proposed method on a synthetic signal from a GPR forward model was studied, with the multi-resolution performance being tested. Inspecting the media of a highroad by analyzing the GPR data, with the abnormal characteristics being designated, validated the applicability of the proposed method.

Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition

Low strain detection, an indirect and nondestructive testing method, is one of the main pile integrity testing methods. We propose low strain testing analysis based on a synchrosqueezing wavelet transformation (SST). Through a typical model pile test, the SST is applied to identify pile bottom signal reflection time and to separate signal from noise. It is also compared with the conventional wavelet de-noising and the empirical mode decomposition (EMD) de-noising method. Results show that the SST technique can be used to identify the reflected signal of the pile bottom, achieve signal and noise separation, and improve signal-to-noise ratio. The method has significant advantage in low strain detection signal processing compared to other methods.

/pdf/low-strain-pile-testing-based-on-synchrosqueezing-wavelet-1kt5r46sy0.pdf

Low strain pile testing based on synchrosqueezing wavelet transformation analysis

As the largest organ in human body, skin provides the important sensory channel for humans to receive external stimulations based on touch. By the information perceived through touch, people can feel and gain the properties of objects like weight, temperature, textures, and motion etc. In fact, those properties are nerve stimuli to our brain received by different kinds of receptors in the skin. Mechanical, electrical and thermal stimuli can stimulate these receptors and cause different information to be conveyed through the nerves. Technologies for actuators to provide mechanical, electrical or thermal stimuli have been developed. These include static or vibrational actuation, electrostatic stimulation, focused ultrasound and more. Smart materials such as piezoelectric materials, Carbon nanotubes, shape memory alloys play important roles in providing actuation for tactile sensation. This paper aims to review the background biological knowledge of human tactile sensing in order to give an understanding of how we sense and interact with the world through the sense of touch, as well as the conventional and state-of-the-art technologies of tactile actuators for tactile feedback delivery.

The application of smart materials in tactile actuators for tactile information delivery

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