Physical Inspection Research Articles

Behaviour of geopolymer concrete at elevated temperature- a comprehensive review

In comparison to conventional materials made of concrete, geopolymer materials are sustainable, durable and strong. Geopolymer used should not only be sustainable but also fire-resistant for the necessary amount of time. The properties of geopolymer materials after high-temperature exposure, such as weight loss, thermal expansion, thermal conductivity, mechanical properties, and microstructure are reviewed and discussed in detail in the current study. In addition, the behaviour of Geopolymer concrete (GPC) at high temperatures with different recycled fibre additions as well as additions of crumb rubber is also examined in this article. The use of recycled fibres made from industrial wastes offers additional advantages, including resource conservation, waste reduction, lower costs of processing than normal fibres, and the removal of waste disposal in landfills. The latter offers a sustainable method for recycling tyres and helps slow down the depletion of natural aggregates. From the literature, it is revealed that at 800–1000 °C, compared to the OPC concrete specimens, the GPC samples had less cracking damage and also substantial spalling was seen in the OPC samples, but not in the GPC samples. GPC with recycled fibres has higher crack-resistant characteristics at elevated temperatures. Meanwhile, in a study at 800 °C, rubberized geopolymer showed more cracks compared to control GPC. This work also provides comprehensive reviews on the type of recycled fibres and rubber aggregates, spalling mechanisms, physical inspection and properties of rubberized geopolymer and recycled fibre-reinforced GPC. It also demonstrated the influence of elevated temperature on the characteristics of recycled fibre-reinforced GPC and rubberized geopolymer after high-temperature exposure.

Advancing the marketing‐operations interface in omnichannel retail

Advancing the marketing‐operations interface in omnichannel retail

Deep Learning Based Car Damage Classification and Cost Estimation

Due to the increasing number of people driving cars, the number of insurance claims has also increased. This process involves the manual assessment of the vehicle by an insurance company's service engineer, as well as the physical inspection by a licensed insurance company representative. An end-to-end solution has been proposed that would allow the customer and the insurance company to automate the process of recognizing the damaged area in the vehicles and estimating the cost of the damage. It would allow them to get a better understanding of the condition of the vehicle. For this purpose, A deep learning, Mask Region-based Convolutional Neural Network (Mask RCNN) model was utilized in this work to classify vehicle damages costs. Two Mask RCNN models were utilized, the first one was used to detect the sides of the vehicle, which will affect damage cost estimation and the second was used to find the area of the damage. The Experimental work shows that the proposed model gives reasonable results to estimate the cost of the damage. We achieve an accuracy of 98.5% with the combination of the two Mask RCNN models. And showed that Mask RCNN has a promising result to detect the area of the damage in the car.

Impacts of pavement performance indicators on traffic oriented parameters and road safety

With the passage of time, due to vehicle movement and environmental conditions, the road pavement deteriorates. The accident rate increases if there is no proper maintenance of road deterioration. This study evaluates the safety performance due to the change in pavement performance indicators on the selected roads of Bahawalpur, Punjab Pakistan. Three roads different pavement conditions were selected, based upon their physical inspection, of 5 Km length (each road). The pavement performance indicators used in this study were International Roughness Index (IRI), rutting and cracking. The accident data of last five years (Jan 2015 to April 2019) was used in this study. The pavement performance indicators were measured on site and accident data was taken from emergency service (Rescue 1122). The relationships between pavement performance indicators and accident data were examined. No linear relationship is found between pavement performance indicators and traffic safety, however a parabolic relation is best fitted between these variables. The parabolic models are developed by keeping the pavement performance indicators on independent axis and accident data on dependent axis which can be use by road designers for assessing the value of IRI, rutting and cracking for safe traffic flow. Some values are also suggested which can be used as evaluation criteria that whether the road needs rehabilitation work or not. The suggested values/range for IRI is 1.75-2 m/Km, for rutting 9 – 10 mm and for cracking-Pavement Condition Rating (PCR) 75-80.

Digitalization and hydroinformatics

Digitalization and hydroinformatics

Development Of A Cloud-Based Condition Monitoring Scheme For Distribution Transformer Protection

Distribution transformers are a necessity to ensure a reliable power supply to consumers and their inability to function properly or even breakdown should be avoided due to the high cost of replacing them. Distribution transformers are large in numbers and randomly distributed in cities and there is a need to accurately monitor their daily/hourly performance. To achieve this, real-time monitoring of the transformer’s health status is proposed rather than the use of the traditional approach involving physical inspection and testing which is slow, tedious and time-consuming. This paper presents a cloud-based monitoring scheme applied to a prototype distribution transformer. A 10kVA, 0.415 kV prototype distribution transformer has been acquired and connected to three residences for data acquisition. A data acquisition system has been developed to monitor and record the parameters of the prototype transformer for 14 days. The parameters, monitored in real-time include load current, phase voltage, transformer oil level, ambient temperature and oil temperature. The acquired real-time data of the transformer is validated with the standard measuring instrument. An algorithm was developed to transmit and log the data to ThinkSpeak cloud server via node MCU (ESP 8266). Results obtained in this study, which can be visualized via the graphical user interface of ThinkSpeak, indicate that the proposed scheme can acquire vital data from the distribution transformers and transmit the information to the monitoring centre.

Investigation and Analysis of Crack Detection using UAV and CNN: A Case Study of Hospital Raja Permaisuri Bainun

Crack detection in old buildings has been shown to be inefficient, with many technical challenges such as physical inspection and difficult measurements. It is important to have an automatic, fast visual inspection of these building components to detect cracks by evaluating their conditions (impact) and the level of their risk. Unmanned Aerial Vehicles (UAV) can automate, avoid visual inspection, and avoid other physical check-ups of these buildings. Automated crack detection using Machine Learning Algorithms (MLA), especially a Conventional Neural Network (CNN), along with an Unmanned Aerial Vehicle (UAV), can be effective and both can efficiently work together to detect the cracks in buildings using image processing techniques. The purpose of this research project is to evaluate currently available crack detection systems and to develop an automated crack detection system using Aggregate Channel Features (ACF)that can be used with unmanned aerial vehicles (UAV). Therefore, we conducted a real-world experiment of crack detection at Hospital Raja Permaisuri Bainun using DJI Mavic Air (Drone Hardware) and DJI GO 4(Drone Software) using CNN through MATLAB software with CNN-SVM method with the accuracy rate of3.0 percent increased from 82.94%to 85.94%. in comparison with other ML algorithms like CNN Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN).

Frequency of Scoliosis among School Going Students Carrying Heavy Bags: A Cross-Sectional Study in Lahore, Pakistan

Objective: Scoliosis is structural change that occurs in a diversity of conditions. Development of the scoliosis during time periods of rapid child growth may consequence in severe abnormality. This survey had the purpose to find out the scoliosis frequency among school going students with age 11-16 years and determine possible associations among scoliosis and bodyweight and the bagpacks weight.
 Method: A Cross-Sectional study was conducted on 374 students with age of 11-16 years old boys and girls were having no abnormality or any MSK deformity was involved in the survey. All students were monitored by Adam's forward bend test and physical inspection test and for this purpose SRS-22r patient questionnaire was used. All data collected via a questionnaire which coded it into SPSS version 26. Data was constituted in configuration of bar charts, graphs and tables.
 Results: This inquiry contains 374 participants, and means weight of students 43.83±11.98, and mean weight of bags 5.21±1.99. The frequency of scoliosis among respondents was 48.66%, 103(27.54%) students were suffering from right Scoliotic hump, and 79(21.12%) have left Scoliotic hump. The frequency of shoulder & scapular asymmetry 73%, uneven hips 19.7%, head deviation 50.8%, body overweight 1.9%, carrying immoderate heavy school bag packs material 4%.
 Conclusion: The scoliosis frequency among the sample of school going students was high. The statistical analysis showed that scoliosis was positively correlated with weight of students with statistically significant (p=0.000). No association was found among frequency of scoliosis and bags weigh.

Evaluating various machine learning algorithms for automated inspection of culverts

One of the most important hydraulic structures that has been overlooked is culverts. Regular maintenance and inspections are required to ensure that these structures are used effectively and safely. Culvert inspection, on the other hand, takes a significant amount of time and resources, and agencies cannot afford to do it on a regular basis. To overcome physical inspection challenges, this study proposed employing machine learning algorithms to predict the condition of Utah's culverts based on historical data. This study assessed multiclass classification algorithms that had not been used before. The classification machine learning algorithms employed in this study are Random Forest, Decision Tree, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network. Also, a combined dataset from four states was used to address Utah's limited culvert inventory data. The final dataset contained 2555 culvert records. Out of the developed prediction models, Random Forest had the highest accuracy of 82%. The findings demonstrated that Random Forests could be used to improve culvert network quality while reducing culvert maintenance expenses for UDOT. The proposed model may be applied to similarly existing culvert inventories located around the nation.

Convolutional Neural Networks for Defect Detection on LV cables

A convolutional neural network (CNN) is a machine learning algorithm that is particularly well-suited for tasks such as object recognition, image captioning, and speech recognition. CNNs are particularly effective at detecting features in images that are not easily observable by other machine learning algorithms, such as defects in manufacturing. By analyzing large collections of images, CNNs are able to find patterns that are indicative of defects. Power cables are an important part of manufacturing, as they allow machines to be operated and communicated with. Recently, great importance has been offered in making electricity generation, transmissions, distribution and storage smart. However, the shift to smart grid should include intelligent methods of detecting the reliability of electrical connections. Several types of electrical cables are used to transmission and distribution of electrical energy. Due to excellent electrical and mechanical properties, cross-linked polyethylene (XLPE) cables are widely used in power systems. Poor manufacturing techniques in the production and installation of cable joints will cause insulation defects. Some suggest, the use of interdigital capacitive (IDC) for online monitoring on XLPE cables. Others suggest The use of a continuous wave (CW) terahertz (THz) imaging technology could help display and detect interior faults in cross-linked polyethylene (XLPE) plates used for power line insulation. In this paper, I developed models which predominantly use locally collected custom dataset to forecast individual power cable physical safety status. The model is aimed at replacing the physical inspection with computer vision and image processing techniques to classify defective power cable from non-defective ones. The project is implemented using the Python programming language, the Tensorflow library, and a Convolutional neural network.The Convolutional Neural Network (CNN)-based method is purposefully chosen and applied in this project for power cable defect classification. The project culminates by recommending the use of same or additional datasets and provide modalities to detect power cable defect from live video.

An attention-based wide and deep CNN with dilated convolutions for detecting electricity theft considering imbalanced data

• There is a serious sample imbalance in the data, which easily leads to a severe bias in the classification model, which in turn affects the classification accuracy. In order to solve the problem of sample imbalance, we proposed a method of using focal loss for model training to enhance the contribution of a small number of samples to model optimization. • Electricity consumption data is correlated in the time dimension. It is necessary to capture a larger receptive field to obtain global information and improve the model's accuracy. Keeping this concern in mind, we used dilated convolution to enlarge the receptive field without loss of information so that each convolution output contains a larger range of information, thereby capturing a larger receptive field and obtaining global information of the input data. • How to effectively integrate the feature expressions obtained by wide CNN and deep CNN is a key factor to improve the performance of the model. To address this problem, we adopt the channel-dimensional adaptive attention module to adaptively fuse the feature expressions obtained by wide CNN and deep CNN to improve the accuracy of model training. For the increasingly serious phenomenon of electricity theft, many researchers are trying to detect it. Traditional detection methods rely on physical inspection, which has low detection efficiency and high cost. However, the rapid application of advanced metering infrastructure (AMI) makes it possible to detect electricity theft via smart meters. In this work, we propose a hybrid improved wide and deep convolutional neural networks (CNN) method to address electricity theft detection on an imbalanced realistic dataset that presents a daily electricity consumption provided by State Grid Corporation of China. Our key contribution is the introduction of a channel-dimensional adaptive attention module concatenated with dilated convolutions. Moreover, we propose focal loss to solve the data imbalance problem. Experimental results demonstrate that compared to other state-of-the-art deep learning classifiers, our improved method has better performance (with 97.08% of MAP@100 and 83.61% of AUC), which verifies the effectiveness of the proposed method.

Failure Analysis of Tubing Collapse in a Gas Well

The tubing used in a gas well rarely collapses and fails during applying annulus pressure. In this study, the failure causes of tubing collapse were analyzed by means of data verification, macroscopic observation, magnetic particle inspection, physical and chemical inspection, optical microscopy, and tubing collapse test. Mechanical analysis of the string and full‐scale physical simulation test simulating downhole working conditions. Finally, the verification analysis of the collapse test is carried out by the finite element analysis (FEA). The results showed that (1) the physical dimension, physical and chemical properties, and collapse resistance of this batch of tubing met the requirements of the tubing ordering technical standard. (2) Assuming that the well packer slip was unsealed and could slide freely, the mechanical theoretical analysis of collapsed tubing string and collapse test under simulated working condition load was carried out, which reproduced the load when the tubing collapsed. It can be seen from this that the packer did fail. (3) The FEA calculation results showed that when the external pressure was greater than 30.75 MPa, it would inevitably lead to collapse failure in case of packer unsealed. In conclusion, the root cause for the collapse failure of the 105th underground tubing string was that the packer lost its sealing function, resulting in an abnormal axial load. While under the action of external pressure, the tubing was overloaded and collapsed. It is recommended to carry out verification tests on the material performance of packer slip, the dimensional changes of packer tool outer diameter and inner diameter under actual well conditions, the creep behavior of packer seal, and the performance of shear pin under actual working conditions, especially in the well containing H2S, so as to prevent the pressure leakage of gas well annulus caused by packer unsealing and the reoccurrence of such downhole string collapse accidents. The first collapse test under simulated working condition load is conducted in this paper. Analyzing the collapse failure work and putting forward suggestions to effectively prevent similar failures from happening again are of great significance to the oilfield.

Photovoltaic Cell Panels Soiling Inspection Using Principal Component Thermal Image Processing

Intended for good productivity and perfect operation of the solar power grid a failure-free system is required. Therefore, thermal image processing with the thermal camera is the latest non-invasive (without manual contact) type fault identification technique which may give good precision in all aspects. The soiling issue, which is major productivity affecting factor may import from several reasons such as dust on the wind, bird mucks, etc. The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7% in the county, such as India, to more than 25% in middle-east countries country, such as Dubai, Kuwait, etc. This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place. Hence, in this method, solar panels can be verified by working without disturbing production operation and it will save time and price of recognition. India ranks 3rd worldwide in the usage use age of Photovoltaic (PV) panels now and it is supported about 8.6% of the Nation’s electricity need in the year 2020. In the meantime, the installed PV production areas in India are aged 4–5 years old. Hence the need for inspection and maintenance of installed PV is growing fast day by day. As a result, this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis (PCTA) on MATLAB Environment.

Predicting Operators’ Fatigue in a Human in the Artificial Intelligence Loop for Defect Detection in Manufacturing

Quality inspection, typically performed manually by workers in the past, is now rapidly switching to automated solutions, using artificial intelligence (AI)-driven methods. This elevates the job function of the quality inspection team from the physical inspection tasks to tasks related to managing workflows in synergy with AI agents, for example, interpreting inspection outcomes or labeling inspection image data for the AI models. In this context, we have studied how defect inspection can be enhanced, providing defect hints to the operator to ease defect identification. Furthermore, we developed machine learning models to recognize and predict operators’ fatigue. By doing so, we can proactively take mitigation actions to enhance the workers’ well-being and ensure the highest defect inspection quality standards. We consider such processes to empower human and non-human actors in manufacturing and the sociotechnical production system. The paper first outlines the conceptual approach for integrating the operator in the AI-driven quality inspection process while implementing a fatigue monitoring system to enhance work conditions. Furthermore, it describes how this was implemented by leveraging data and experiments performed for a real-world manufacturing use case.

Detection of damage in offshore jacket structure using artificial neural network

With industrial growth over the years, geologists have begun to search for offshore sources of oil and natural gas. Most offshore oil-production platforms are jacket type. Offshore jacket structures consist of welded steel space frame. Tubular pipes are opted as they reduce hydrodynamic loads making them highly durable structures. However due to natural phenomenon like cyclones, earthquakes, etc. these structures can get adversely damaged. Apart from that the structure also degrades due to corrosion and fatigue due to the severe conditions it is always exposed to. In many cases physical inspection of the structure for checking damages is not possible due to unfavourable onsite conditions or lack of trained professionals. Hence there is a need to develop alternatives solutions. In recent years much research has been done in the application of Artificial Neural Networks (ANN) in Civil Engineering. ANN are computation systems that are inspired by biological neural systems. This paper aims at creating an ANN to identify damage in an offshore jacket structure using the modal parameters. The training set for the ANN is obtained by using a finite element software. The ANN is then tested using a test set and then it will be used to predict the structural damage in an offshore jacket structure.

Sexual dysfunction is more common among men who have high sperm DNA fragmentation or teratozoopermia

Men in couples that have experienced pregnancy loss have a higher risk of sexual dysfunction. Semen quality impairment is common in men of couples with pregnancy loss. The objective of this article is to evaluate the differences in the incidence of male sexual dysfunction in a cohort of pregnancy loss couples with different types of semen quality impairment. A cross-sectional analysis of 426 men who attended our outpatient clinic for couples’ pregnancy loss, those without genetic abnormalities were included in the final analysis covering June 2021 to October 2021. The patients were divided into 5 groups according to type of semen quality impairment: normozoospermia group (group normal; N = 161), high sperm DNA fragmentation group (group high-SDF; N = 87), isolated asthenozoospermia group (group iAstheno; N = 45), isolated teratozoopermia group (group iTerato; N = 44), and ≥ 2 abnormal sperm parameters group (group multiple; N = 89). All subjects underwent a complete physical inspection, including palpation of the male genitalia and semen analysis. Validated assessment tools for erectile dysfunction (the International Index of Erectile Function -IIEF-5) and anxiety (the seven-item Generalized Anxiety Disorder Scale- GAD-7) were also used. Men with high sperm DNA fragmentation and isolated teratozoopermia were associated with increased erectile dysfunction risk when compared with normozoospermic men, with an OR of 2.75 [1.49–5.09; p = 0.001] and 2.44 [1.22–5.31; p = 0.024], respectively. It is interesting to note that there was no difference in prevalence of erectile dysfunction between Group iAstheno and Group normal (20.0% vs. 18.0%; OR = 1.24 [0.52–2.97]; P = 0.625). More than half (50.6%) of the participants in Group high-SDF reported sexual intercourse less than once per week, much more than those in the normozoospermia group (23.2%, p < 0.05), followed by Group iTerato (44.4%) and Group multiple (46.1%). GAD-7 scores increased slightly but significantly among groups when compared with Group normal. Not surprisingly, GAD-7 scores remained higher in Group high-SDF. In males of pregnancy loss couples, men with high sperm DNA fragmentation and teratozoopermia suffer from a higher incidence of erectile dysfunction. This phenomenon is not significant in men with isolated asthenozoospermia. Proper counseling and treatment of impaired semen quality are warranted.

Recent Progress in Development of Carbon-Nanotube-Based Photo-Thermoelectric Sensors and Their Applications in Ubiquitous Non-Destructive Inspections.

The photo-thermoelectric (PTE) effect in electronic materials effectively combines photo-absorption-induced local heating and associated thermoelectric conversion for uncooled and broadband photo-detection. In particular, this work comprehensively summarizes the operating mechanism of carbon nanotube (CNT)-film-based PTE sensors and ubiquitous non-destructive inspections realized by exploiting the material properties of CNT films. Formation of heterogeneous material junctions across the CNT-film-based PTE sensors, namely photo-detection interfaces, triggers the Seebeck effect with photo-absorption-induced local heating. Typical photo-detection interfaces include a channel-electrode boundary and a junction between P-type CNTs and N-type CNTs (PN junctions). While the original CNT film channel exhibits positive Seebeck coefficient values, the material selections of the counterpart freely govern the intensity and polarity of the PTE response signals. Based on these operating mechanisms, CNT film PTE sensors demonstrate a variety of physical and chemical non-destructive inspections. The device aggregates broad multi-spectral optical information regarding the targets and reconstructs their inner composite or layered structures. Arbitrary deformations of the device are attributed to the macroscopic flexibility of the CNT films to further monitor targets from omni-directional viewing angles without blind spots. Detection of blackbody radiation from targets using the device also visualizes their behaviors and associated changes.

Sine Cosine Based Harris Hawks Optimizer: A Hybrid Optimization Algorithm for Skin Cancer Detection Using Deep Stack Auto Encoder

Skin cancer is becoming major problems due to its tremendous growth. Skin cancer is a malignant skin lesion, which may cause damage to human. Hence, prior detection and precise medical diagnosis of the skin lesion is essential. In medical practice, detection of malignant lesions needs pathological examination and biopsy, which is expensive. The existing techniques need a brief physical inspection, which is imprecise and time-consuming. This paper presents a computer-assisted skin cancer detection strategy for detecting the skin lesion in skin images using deep stacked auto encoder. Sine Cosine-based Harris Hawks Optimizer (SCHHO) trains deep stacked auto encoders. The proposed SCHHO algorithm is designed by combining Sine Cosine Algorithm (SCA) and Harris Hawks Optimizer (HHO). The identification of skin lesion is performed on each segment, which is obtained by sparse-Fuzzy-c-means (FCM) algorithm. Statistical features, texture features and entropy are employed for selecting the most significant feature. Mean, standard deviation, variance, kurtosis, entropy, and Linear Discriminant Analysis (LDP) featured are extracted. SCHHO-Deep stacked auto-encoder outperformed other approaches with 91.66% accuracy, 91.60% sensitivity, and 91.72% specificity.

A lubricated stagnation point flow of nanofluid with heat and mass transfer phenomenon: Significance to hydraulic systems

The improved thermal association of heat transfer is considerably observed due to interaction of nanoparticles in recent days. The lubrication phenomenon with heat and mass transfer effects plays a key role in the hydraulic systems. In current research, the thermal impact of nanofluid over a lubricated stretching surfaces near a stagnation point analytical has been studied. A thin layer of lubricating fluid with a variable thickness provides lubrication. The inspection of thermophoresis and Brownian motion phenomenon is illustrated via Boungrino model. The analytical finding of refurbished boundary layer ordinary differential equations is obtained by a reliable and proficient technique namely variational iteration method (VIM). The Lagrange Multiplier is a potent tool in proposed technique to reduce the computational work. In addition, a numerical comparison is presented to show the effectiveness of this study. The range of flow parameters is based on theoretical flow assumptions. Physical inspection of involved parameters on velocities, temperatures, concentrations, and other quantities of interest when lubrication is presented. The current results present applications in polymer process, manufacturing systems, heat transfer and hydraulic systems.

Formulasi dan Uji Stabilitas Fisik Sediaan Lulur Krim Ekstrak Daun Pandan Wangi (Pandanus amaryllifolius Roxb.)

Scrub cream is a pharmaceutical preparation which function is to remove dead skin cells, moisturize and brighten the skin with the help of scrub ingredients. One of the natural ingredients used as raw material for scrub cream is fragrant pandan (Pandanus amaryllifolius Roxb.). The aim of this study was to make a cream scrub preparation from pandan extract with various concentrations of 3%, 3.5% and 4%. This research was conducted experimentally. Preparation testing includes organoleptic test, homogeneity test, pH test, irritation test, spreadability test, stability test, and emulsion type test. The results of the physical quality inspection of the scrub cream from pandan Wangi extract showed that the preparation was homogeneous, had an M/A emulsion type, had a pH value of 6.1-6.5, did not cause irritation and did not change during storage for 4 weeks except for the preparation with a concentration of 3.5% because the preparation underwent a change in the breakdown of the emulsion in the preparation on storage starting from the 3rd week. From the research that has been done, it can be concluded that Pandanus amaryllifolius Roxb extract can be formulated as a scrub cream with a variation of concentration of 3%; 3.5% and 4% and does not irritate the volunteers' skin.