Analysis on the Behaviour of Stiffened and Unstiffened Steel Plate Shear Walls with Enhanced Performance

Verma, Isha; Setia, Saraswati; Berwal, Parveen; Goel, R. A.; Singh, Vishwakarma

doi:10.1088/1757-899x/804/1/012035

Cited by 2 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fast evolution of the Internet of Things (IoT) in radar, wireless interaction, and trade IoT is enhancing more presumed to be the next-creation option of control [38]. Many researchers [7,8,10,11,21,33,35,38,39] have used the application of IOT for water quality observing systems and concluded that parameters like pH and TDS for different types of water-salty, mud, drinking, and tap water-showed varied results. The Internet of Things is crucial for increasing trade effectiveness and superiority while cutting trade costs and supplies.…”

Section: Ai In Water Quality Monitoringmentioning

confidence: 99%

“…Image and audio identification, natural language processing, and financial modelling are just a few of the uses for ANNs. They have been particularly successful in tasks like object recognition and speech recognition, where they have achieved human-level performance in some cases [11,14,33,40,44]. However, training ANNs may be computationally costly and requires a huge quantity of data to attain decent performance.…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

“…Treatment of contaminants before they enter surface waterways is an alternative strategy. This might entail filtering contaminants and enhancing water quality utilizing a variety of methods, including sediment basins, wetlands, and artificial treatment wetlands [32,33]. Pollutants can often be avoided by using source control measures, such as covering storage places and reducing industrial discharges [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence for Surface Water Quality Evaluation, Monitoring and Assessment

Rana,

Kalia,

Boora

et al. 2023

Water

Self Cite

View full text Add to dashboard Cite

The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.

show abstract

Section: Ai In Water Quality Monitoringmentioning

confidence: 99%

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence for Surface Water Quality Evaluation, Monitoring and Assessment

Rana,

Kalia,

Boora

et al. 2023

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…These models provide a means to simulate and analyze the behaviour of groundwater systems, allowing stakeholders to make informed decisions regarding resource allocation and sustainable management practices. [7][8][9][10][11] Study focused on developing a conceptual model to represent the regional groundwater flow. This model formed the basis for constructing a groundwater flow model in the basin using MODFLOW processing.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Modelling of Jamwa Ramgarh using Visual MODFlow Flex

Sharma,

Gupta,

Ravanan

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

A steady state finite difference model, MODFLOW, is developed to quantify groundwater in Jamwa Ramgarh, Jaipur (Dt) Rajasthan, using Groundwater data from 06 observation wells. ArcGIS was used to generate various shapefiles required as input files in Visual MODFlow Flex. The findings indicate that the calculated contours of groundwater levels closely correspond to the observed contours. The sustainable utilization and management of groundwater resources pose significant challenges for many countries worldwide. In recent years, groundwater modelling has emerged as an effective approach to tackle these challenges. Various modelling software tools are available to simulate groundwater flow, with MODFLOW being widely used to investigate the interactions between surface water and groundwater and to develop models for specific study areas. MODFLOW, a renowned groundwater modelling software, enables researchers and water resource professionals to understand and analyze the complex dynamics of groundwater systems. By utilizing MODFLOW, it becomes possible to assess the interactions between surface water and groundwater, evaluate the impacts of pumping or recharge activities, and make informed decisions regarding the sustainable management of groundwater resources. The utilization of MODFLOW and similar modelling software plays a crucial role in advancing our understanding of groundwater systems and supporting effective groundwater resource management practices.

show abstract

Analysis on the Behaviour of Stiffened and Unstiffened Steel Plate Shear Walls with Enhanced Performance

Cited by 2 publications

References 9 publications

Artificial Intelligence for Surface Water Quality Evaluation, Monitoring and Assessment

Artificial Intelligence for Surface Water Quality Evaluation, Monitoring and Assessment

Groundwater Modelling of Jamwa Ramgarh using Visual MODFlow Flex

Contact Info

Product

Resources

About