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To evaluate the temperature-dependency of the Young’s and shear moduli of concrete after exposure to moderately elevated temperatures using the non-destructive impulse excitation technique (IET).

The study involved heating the concrete up to 225 °C and measuring the dynamic Young’s and shear moduli using the non-destructive technique of impulse excitation, which measures the natural vibration frequency from a mechanical impulse received by an acoustic sensor. The effects of temperature on the dynamic Young’s and shear moduli were analysed and the importance of the spatial variability of the measured values was also verified.

The study found that even moderately elevated temperatures (below 225 °C) resulted in a significant permanent reduction in the Young’s modulus of concrete (reduction in the range of 23%–36% for the maximum temperature considered in this research) as well as a modest and permanent reduction in the shear modulus of around 6%. It was also observed that spatial variability of the mechanical properties of concrete plays an important role in the measured values; higher dispersion of the results was found for the values of the Young’s and shear moduli of concrete measured along the height of the beam. The non-destructive test method used in this study was found to be extremely useful in the investigation of heat-related damage in concrete structures for its ease of use, low time consumption and accuracy. The results were consistent with the published literature.

This study provides important insights into the temperature-dependent behaviour of the dynamic Young’s and shear moduli of concrete and highlights the significance of proper consideration of the spatial variability of the measured values. The use of a non-destructive test method for continuous acoustic testing during heating and cooling proved to be effective, and the findings contribute to the fields of materials science and civil engineering in understanding the effects of elevated temperatures on concrete properties. The findings confirm that IET can be easily used to gather important information in the condition assessment and rehabilitation of concrete structures after a fire event. Further studies to foster the application of this technique to real structures are suggested.

This study aims to collect real-time, in situ data from polymer melt extrusion (ME) 3D printing and use only the collected data to non-destructively identify printed parts that contain defects.

A set of sensors was created to collect real-time, in situ data from polymer ME 3D printing. A variance analysis was completed to identify an “acceptable” range for filament diameter on a popular desktop 3D printer. These data were used as the basis of a quality evaluation process to non-destructively identify spatial regions of printed parts in multi-part builds that contain defects.

Anomalous parts were correctly identified non-destructively using only in situ collected data.

This methodology was developed by varying the filament diameter, one of the most common reasons for print failure in ME. Numerous other printing parameters are known to create faults in melt extruded parts, and this methodology can be extended to analyze other parameters.

To the best of the authors’ knowledge, this is the first report of a non-destructive evaluation of 3D-printed part quality using only in situ data in ME. The value is in improving part quality and reliability in ME, thereby reducing 3D printing part errors, plastic waste and the associated cost of time and material.

The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status.

This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results.

Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness.

This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

One of the often-employed building constituents in the construction sector is concrete, which involves hydration of cement, leading to the generation of carbon footprints during its production. Also, massive amount of natural aggregate is illegally mined, which poses serious environmental issues along with ecological misbalance. Researchers are in continuous search of appropriate substitutes to mitigate those challenges and develop innovative concrete mix. Consequently, depletion of natural resources, the disturbances to the environmental and ecological imbalance will reduce. The purpose of this study is to develop a Portland Slag Cement based novel sustainable concrete incorporating Alccofine and Recycled Refractory Brick as fractional replacement of cement and fine aggregate, respectively and evaluate its destructive, non-destructive and microstructural properties.

M25 grade of concrete adopting 0.45 water-binder proportion, with diverse percentage of Alccofine as fractional substitution of cement and 20% of recycled refractory brick (RRB) as fine aggregate, has been cast and evaluated for diverse mechanical strength following a curing of 7, 14 and 28 days. Scanning electron microscopic analysis has been carried out to study the microstructural changes in the specimens.

Supplementary use of Alccofine enhanced normal compressive strength of sustainable concrete mix blended with Portland Slag Cement by a large amount at all levels of 7, 14 and 28 days of curing. Test results indicated development of a favourable high-strength sustainable concrete mix by substituting cement with Alccofine.

This manuscript has demonstrated the possibility of developing sustainable concrete blends by incorporating Alccofine 1203 and RRB as partial replacement of Portland Slag Cement and natural fine aggregate, respectively. The strength and potential of concrete incorporating RRB for wider and special application in adverse environmental conditions having higher thermal gradient, as RRB is a valuable waste from high temperature kiln and furnaces. Alccofine 1203 has been included in the concrete mix as an alternative to Portland Slag Cement to improve the mechanical strength properties and durability of concrete intended for adverse environmental application.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

Artificial fruit ripening is hazardous to mankind. In the recent past, artificial fruit ripening is increasing gradually due to its commercial benefits. To discriminate the type of fruit ripening involved at the vendors’ side, there is a great demand for on-sight ethylene detection in a nondestructive manner. Therefore, this study aims to deal with a comparison of various laboratory and portable methods developed so far with high-performance metrics to identify the ethylene detection at fruit ripening site.

This paper focuses on various types of technologies proposed up to date in ethylene detection, fabrication methods and signal conditioning circuits for ethylene detection in parts per million and parts per billion levels. The authors have already developed an infrared (IR) sensor to detect ethylene and also developed a lab-based setup belonging to the electrochemical sensing methods to detect ethylene for the fruit ripening application.

The authors have developed an electrochemical sensor based on multi-walled carbon nanotubes whose performance is relatively higher than the sensors that were previously reported in terms of material, sensitivity and selectivity. For identifying the best sensing technology for optimization of ethylene detection for fruit ripening discrimination process, authors have developed an IR-based ethylene sensor and also semiconducting metal-oxide ethylene sensor which are all compared with literature-based comparable parameters. This review paper mainly focuses on the potential possibilities for developing portable ethylene sensing devices for investigation applications.

The authors have elaborately discussed the new chemical and physical methods of ethylene detection and quantification from their own developed methods and also the key findings of the methods proposed by fellow researchers working on this field. The authors would like to declare that the extensive analysis carried out in this technical survey could be used for developing a cost-effective and high-performance portable ethylene sensing device for fruit ripening and discrimination applications.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.

This study aims to analyze the factors, evaluation techniques of the durability of existing railway engineering.

China has built a railway network of over 150,000 km. Ensuring the safety of the existing railway engineering is of great significance for maintaining normal railway operation order. However, railway engineering is a strip structure that crosses multiple complex environments. And railway engineering will withstand high-frequency impact loads from trains. The above factors have led to differences in the deterioration characteristics and maintenance strategies of railway engineering compared to conventional concrete structures. Therefore, it is very important to analyze the key factors that affect the durability of railway structures and propose technologies for durability evaluation.

The factors that affect the durability and reliability of railway engineering are mainly divided into three categories: material factors, environmental factors and load factors. Among them, material factors also include influencing factors, such as raw materials, mix proportions and so on. Environmental factors vary depending on the service environment of railway engineering, and the durability and deterioration of concrete have different failure mechanisms. Load factors include static load and train dynamic load. The on-site rapid detection methods for five common diseases in railway engineering are also proposed in this paper. These methods can quickly evaluate the durability of existing railway engineering concrete.

The research can provide some new evaluation techniques and methods for the durability of existing railway engineering.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.