Modeling of UCS value of stabilized pond ashes using adaptive neuro-fuzzy inference system and artificial neural network

Suthar, Manju

doi:10.1007/s00500-020-04806-x

Cited by 13 publications

(3 citation statements)

References 54 publications

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“…The mapping between 0 and 1 for each data point in the input space is determined by a membership function in the form of a fuzzy curve [85]. Membership in fuzzy sets has different shapes consisting of linear, bell, gaussian, trapezoidal, shoulder curves (left and right), and triangular shapes [103], [104]. In this study, only two types of curves were used, namely the shoulder curve (left and right) and the triangle curve.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Furizal,

Sunardi,

Yudhana

2023

Journal of Robotics and Control

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Work is an activity that takes most of the day to earn a living and improve the standard of living. During work, many people have to work indoors, which can be a less comfortable and unhealthy place if the temperature and humidity are not well controlled. Unsuitable temperature and humidity conditions can negatively affect the health and comfort of workers, as well as interfere with productivity and work quality. However, the problem that often arises is the difficulty of controlling room temperature and humidity effectively, especially in rooms that are closed and do not get air circulation from outside. Therefore, an effective solution is needed to control the temperature and humidity of the room automatically and remotely via the internet. The contribution of this research is to develop an effective and efficient AC control system in controlling room temperature and humidity using Tsukamoto's Fuzzy Inference System (FIS) method and the Internet of Things (IoT). Tsukamoto's FIS is used to produce AC temperature values in room temperature and humidity control as measured by the DHT22 sensor directly integrated with the ESP32 microcontroller. This control system is monitored remotely using IoT concepts through a mobile application interface. The results of this study show that room temperature can be controlled under normal conditions, with an average change of -1.67°C and an overall average temperature of 25.95°C. While the average humidity is at a value of 80.16% which is included in the Wet set. This suggests that humidity cannot be controlled under normal conditions, so it still requires further development. In addition, it is also necessary to further investigate the effectiveness of the tool in various sizes and more complex layouts of rooms.

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Section: Fuzzy Logicmentioning

confidence: 99%

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Furizal,

Sunardi,

Yudhana

2023

Journal of Robotics and Control

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“…Bell shape and trapezoidal MFs are widely used in the engineering literature (e.g. Mottahedi et al 2018;Suthar 2020). B/D and X B graphs indicate two subgroups (Fig.…”

Section: Fundamentals Of Anfis Modelmentioning

confidence: 99%

Prediction of flyrock throw distance in quarries by variable selection procedures and ANFIS modelling technique

Hudaverdi

2021

Preprint

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This study suggests application of variable reduction procedures for flyrock prediction. It was aimed to create robust and non-complex predictive models. Eleven operational blast parameters and rock mass properties were measured in an aggregate quarry. Dominant parameters for flyrock occurrence were determined by multivariate statistical methods. Two parallel ANFIS models were developed for flyrock prediction. The first ANFIS model was constructed based on the results of stepwise regression. Burden-hole diameter ratio, in-situ block size and specific charge are the input parameters of ANFIS 1. The second ANFIS model was created based on the results obtained by factor analysis. Burden-hole diameter ratio, bench height- burden ratio, number of holes and charge weight are used as input parameters for ANFIS 2. The calculated mean absolute percentage errors are lower than eight percent for the ANFIS predictions. The median absolute errors are lower than 5 meters. The study also investigates alternative accuracy measures to evaluate forecasting performance. Standardized errors, normalized errors and Nash–Sutcliffe Efficiency (NSE) were found to be useful for model validation. It is concluded that more than a single model can be created for a specific site. Pre-statistical analysis for variable reduction increases performance of the predictive models. Burden appeared to be a significant parameter for flyrock throw.

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“…The human brain and its neurons serve as the foundational elements of the ANN model. This model is an AI technique that has been applied in numerous civil engineering projects. − This technique often combines many layer systems, including an input layer, one or more hidden layers, and an output layer. Weights, transfer functions, and biases link the hidden layer to the other layers .…”

Section: Introductionmentioning

confidence: 99%

Slag and Bagasse Ash: Potential Binders for Sustainable Rigid Pavement

Boora,

Rani,

Suthar

et al. 2023

ACS Omega

Self Cite

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The current study tries to cut carbon emissions by using various waste materials in place of cement, including sugarcane bagasse ash (SCBA), ground granulated blast furnace slag (GGBFS), and ladle furnace slag (LFS), individually and in a combined form also, which has not been studied yet. In the same context, effort was made to utilize the maximum amount of waste materials as the replacement of cement to create a sustainable environment. Besides this, another aim is checking the performance of these waste materials as binding materials with respect to compressive strength for sustainable rigid pavement construction without activating them or using any activating solution. For this purpose, the compressive strength test is done for GGBFS, LFS, and SCBA, and later on, the artificial neural network (ANN) technique is also used to check the novelty of results in a broad way. For the same purpose, M40 grade concrete was made by incorporating different selected waste materials in a varying proportion ranging from 0 to 35%. Based on the results obtained from the compressive strength test for different curing periods, i.e., 7, 14, and 28 days, it was observed that the GGBFS, LFS, and SCBA can be utilized individually up to 15%, respectively. Another observation made from the findings was that the use of LFS and SCBA in the individual form up to 20% was found to be possible as the maximum reduction in strength was found to be up to 2.63%. However, the cumulative impact of all these waste products was also examined. Based on the data, it was concluded that the best outcomes would arise from using these additives in combination to replace cement in the mix by up to 30% (i.e., without compromising the required characteristics of concrete), which will be proved as an aid to the environment and the society also. Besides this, the fluctuation in the compressive strength value of concrete mixes after integrating various waste materials was also examined in order to construct a model using the ANN approach. The model’s outcomes suggest that the ANN model does a good job of forecasting the compressive strength of concrete.

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Modeling of UCS value of stabilized pond ashes using adaptive neuro-fuzzy inference system and artificial neural network

Cited by 13 publications

References 54 publications

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Prediction of flyrock throw distance in quarries by variable selection procedures and ANFIS modelling technique

Slag and Bagasse Ash: Potential Binders for Sustainable Rigid Pavement

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