Development of Adaptive Neuro-Fuzzy Inference System to Predict Concrete Compressive Strength

Predicting the compressive strength of concrete is one of the complex problems in civil engineering because different parameters and factors must be considered. There is several research that have predicted the compressive strength of normal concrete using neuro-fuzzy systems. However, little research has been done to predict the strength of high strength concrete. Recently, machine learning techniques such as artificial neural networks (ANNs), fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS) are becoming extensively established in predicting complex problems. ANFIS has the advantages of both ANNs and fuzzy systems and is most suitable in engineering complicated applications. This study focuses on the development of ANFIS in predicting the compressive strength of high strength concrete. A total of 550 experimental datasets of concrete were used in this research. Each dataset was consisting of six input variables that were water, cement, fine and coarse aggregates, silica fume, and superplasticizer. The compressive strength of high strength concrete was considered as the output of the ANFIS model. In this study, 440 datasets were assigned as training datasets and 110 datasets were considered as testing sets to verify the ANFIS model. The mean square error (MSE) for the training set was 0.00573, and 0.00647 for the testing datasets. The ANFIS model was able to quickly predict the concrete compressive strength with high accuracy. Also, in this research, a sensitivity analysis was applied to study the contribution of input parameters to predict the compressive strength of concrete

Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach

Acne is a prevalent skin condition affecting millions of people globally, impacting not just physical health but also mental well-being. Early detection of skin diseases such as acne is important for making treatment decisions to prevent the spread of the disease. The main goal of this project is to develop an Android mobile application with deep learning that allows users to diagnose skin diseases and also detect the severity level of skin diseases in three levels: mild, moderate, and severe. Most of the deep learning methods require devices with high computational resources which hardly implemented in mobile applications. To overcome this problem, this research will focus on lightweight Convolutional Neural Networks (CNN). This study focuses on the efficiency of MobileNetV2 and Android applications that are used in this project to detect skin diseases and severity levels. Android Studio is used to create a GUI interface, and the model works perfectly and successfully by using TensorFlow Lite. The skin disease images of acne with severity levels (mild, moderate, and severe) achieve 92% accuracy. This study also demonstrated good results when it was implemented on an Android application through live camera input

CFD Based on The Visualisation of Aortic Valve Mechanism in Aortic Valve Stenosis for Risk Prediction at The Peak Velocity

Aortic valve disease plays a crucial role in the development of cardiovascular disease (CVD), leading to increased rates of mortality and morbidity. Two diseases, aortic valve regurgitation and aortic valve stenosis are known to occur in the aortic valve. However, aortic valve stenosis is gaining attention due to its severe impact on the patient. The malfunction of the aortic valve might be affected by blood flow, which leads to stenosis. This study aims to investigate the blood flow re-circulation on the aortic valve in different stenotic regions when the blood’s velocity reaches the pick flow of the time in the systole phases. Four different models of aortic valve stenotic are designed using computer-aided design (CAD) software. The computational fluid dynamics (CFD) approach governed by the Navier-Stokes equation is imposed to identify the characteristics of the blood backflow at the left ventricle. Several hemodynamic factors are considered, such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI) and relative residence time (RRT). The blood flow characteristic is expected to be chaotic, especially at the highest percentages of aortic valve stenosis, presenting the worst condition to the heart. This finding supports healthcare providers in foreseeing the deterioration of the patient’s condition and opting for aorta valve surgery replacement

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

The term “indoor navigation system” pertains to a technological or practical approach that facilitates the navigation and orientation of individuals within indoor settings, such as museums, airports, shopping malls, or buildings. Over several years, significant advancements have been made in indoor navigation. Numerous studies have been conducted on the issue. However, a fair evaluation and comparison of indoor navigation algorithms have not been discussed further. This paper presents a comprehensive review of collective algorithms developed for indoor navigation. The in-depth analysis of these articles concentrates on both advantages and disadvantages, as well as the different types of algorithms used in each article. A systematic literature review (SLR) methodology guided our article-finding, vetting, and grading processes. Finally, we narrowed the pool down to 75 articles using SLR. We organized them into several groups according to their topics. In these quick analyses, we pull out the most important concepts, article types, rating criteria, and the positives and negatives of each piece. Based on the findings of this review, we can conclude that an efficient solution for indoor navigation that uses the capabilities of embedded data and technological advances in immersive technologies can be achieved by training the shortest path algorithm with a deep learning algorithm to enhance the indoor navigation system

A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System

Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models

Enhancement of fexural modulus and strength of epoxy nanocomposites with the inclusion of functionalized GNPs using Tween 80

In this work, epoxy nanocomposite was prepared with the inclusion of unfunctionalized as-received GNPs (ARGNPs) and functionalized GNPs using surfactant Tween 80 (T80GNPs) in the epoxy resin using a mechanical stirrer. ARGNPs were used as it is, while T80GNPs were prepared through the adsorption of surfactant onto GNPs’ surface using a sonication procedure in an ultrasonic bath. Characterization of nanoparticles using SEM shows that ARGNPs indicated a softer image representing a thinner layer of graphene stacks compared to T80GNP which has a tangible solid-looking image resulting from the sedimentation during the process of fltration. Elementally, both ARGNPs and T80GNPs were found to contain carbon, oxygen, and sulfur, as indicated by the EDX spectrum, with the C/O ratio for T80GNPs being 34.7% higher than that for ARGNPs, suggesting the adsorption of Tween 80 molecules on the GNPs after functionalization. FTIR spectroscopy confrms the attachment of Tween 80 molecules on GNPs surface with T80GNPs spectrum indicated higher peak intensity than ARGNPs. Flexural testing demonstrated that the addition of 0.9 wt.% ARGNPs and 0.9 wt.% T80GNPs to the epoxy increased the modulus of the nanocomposites to 72.1% and 82.6%, respectively, relative to neat epoxy. With the same amount of particle content, both nanocomposites showed increased strength, with ARGNPs and T80GNPs exhibiting strengths of 70.5% and 87.8%, respectively, relative to neat epoxy

Correlation analysis on crash factor in Surabaya-Manyar toll road

Crash investigators can create crash-solving and preventive goals by knowing crash's elements, such as the number, type, factor, and element. Human factors, vehicle, road conditions, and environment are the four types of elements that influence the risk of a crash. Surabaya-Manyar Toll Road has been the main link between Surbaya and Jakarta since 1993. There were 149 crashes between 2014 and 2018, with seven fatalities. An investigation and prevention strategy might be devised to improve safety on the road. This study analyzed and discussed the correlation between crash number and volume, factor, time occurred, and road length analysis. Data on the volume of vehicles and frequency of accidents on the Surabaya-Manyar Toll Road between 2014 and 2018 were gathered through a collaborative effort between the Highway Patrol division of the East Java Regional Police and PT. Margabumi Matraraya, the toll road's management company. The result showed that the vehicle factor, human factor, time 06.00-18.00, and daily average were all significantly correlated to the crash number (0.933, 0.505, 0.984, and 0.078), while the road factor, environment factor, and time 19.00-05.00 were not significantly correlated to the crash number (-.0539, 0.616, and -0.519). These implications prompted the following analysis of preventive and action in order to determine the primary factor influencing the number of crashes, which has a strong association to be investigate

Life cycle assessment and economic analysis of Reusable formwork materials considering the circular economy

Economic development and population growth have impacted on fossil-based energy consumption, contributing to environmental pollution. Adopting circular economy research is more pressing than ever to ease pressure on the environment and the economy. Evaluating the best construction materials is not new. To date, many researchers have assessed materials using various criteria. Formwork differs from other construction materials in terms of serviceability and reusability. These materials may be reused multiple times (from 7 to around 50 times). This raises the question of which material is the best from a sustainability perspective. In this paper we have evaluated four of the most widely-used formwork materials used in the construction of buildings in Malaysia. These include plastic, steel, plywood and timber. Evaluations of life cycle assessment (LCA), embodied energy, and life cycle cost (LCC) were conducted from cradle to cradle. For a single use of formwork, timber is best in all categories except human non-carcinogenic toxicity. However, when 50 reuses are considered for the same wall a completely different result arises. In the environmental category, steel formwork produces the lowest emissions and impact in all categories except global warming potential (GWP). Plastic formwork has the lowest carbon emissions. In terms of embodied energy and cost, plastic formwork presents the best option being approximately 20% lower than steel formwork. Because of the inconsistency in the results for LCA, embodied energy, and LCC for 50-cycles of usage, a multi-criteria decision-making (MCDM) tool was used to normalize the results. The MCDM shows that plastic formwork is an ideal choice for sustainability among the alternatives considered

Organic-inorganic PTAA-SiGe transparent optical materials performance analysis for photo device applications

The SiGe materials has currently received a lot of interest due to its application for the advancement of optoelectronics and related sensor technologies. Its promising stability, and band gap-dependent performance for both bulk and nano-crystalline properties are vital as optical materials. To investigate the electrical performance of SiGe active materials based photo device, the spin coated organic p-materials contact is developed on sputtered SiGe on Quartz and ITO glass substrates. Both Si0.8Ge0.2 and Si0.9Ge0.1 films greater than 85 % visible band transparency are predicted that the deposited SiGe is nano-crystalline nature. It is also revealed from absorption-based band gap, AFM grain size and XRD analysis. The transmittance of SiGe thin film is increased with the microstrain of the films as a result, better opto-electrical performance is displayed. Ge composition though slightly makes variation of lattice constant and strain effect however, relatively lower transmittance films greater current density is exhibited. A higher rectifying ratio for lower transparent SiGe material deposited on ITO glass substrate is shown in the dark. Transparency and optoelectrical performance viewpoint white light illuminated PTAA/Si0.8Ge0.2 is shown better on Quartz substrate whereas the dark analysis PTAA/Si0.9Ge0.1 is realized more favorable on ITO glass substrate

The oil sorption behaviour investigation of Kapok (Ceiba pentandra (L.)) fiber

As oil exploration and production activities have risen globally, water contamination from oil spills and the discharge of other oily wastewaters has emerged as one of the primary environmental concerns. Thus, Kapok fiber is considered in this study as it is known as one of the most effective method for cleaning up and collecting oil spills where Kapok is a natural cellulosic fiber with unique characteristics. A critical investigation was conducted to study the potential of kapok fiber as sorbent material, also analyze the surface properties of kapok fiber for the ability of kapok fiber to absorb oil and investigate the sorption mechanisms of kapok fiber. Therefore, the surface properties of kapok fiber were analyzed using SEM, FTIR, TGA and contact angle. To investigate the selectivity nature and the sorption capacity of 5 g kapok fiber, different types of oil and different apparent viscosity were used. The types of oil used are gear oil (low viscosity), vegetable oil-based cooking oil (medium viscosity) and waste oil (high viscosity). Kapok fiber was able to absorb all types of oil, with wasted oil absorbing the most about 17.88 g.g-1. Scanning electron microscopy (SEM) was used to examine the morphology of raw kapok fiber. In this study, kapok fiber was shown to have a porous hollow lumen structure and a waxy coating on the surface. Other than that, for the contact angle analysis, kapok fiber had high water contact angle up to 130˚. The water droplet was stood on the kapok fibers surfaces before and after absorption with contact angles ranging from 130˚ to 145˚. In contrast, the oil droplet had disappeared from the surfaces of kapok fiber within a few second