Prediction of displacement in Reinforced concrete based on artificial neural networks using sensors

Sivasuriyan, Arvindan; Vijayan, Dhanasingh Sivalinga

doi:10.1016/j.measen.2023.100764

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…Moreover, the ADXL345's fast response and high accuracy make it valuable for achieving stable flight dynamics in drones [62]. It has also found extensive use in SHM for detecting structural changes, predicting failures, and advancing SHM techniques [63][64][65][66]. The study by Rehman et al [67] demonstrated the superior capabilities of the ADXL345 accelerometer compared to a commercial accelerometer in terms of accuracy and reliability.…”

Section: Sensor Node and Gateway Devicementioning

confidence: 99%

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

Nilnoree,

Taparugssanagorn,

Kaemarungsi

et al. 2024

Applied Sciences

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This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications

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Section: Sensor Node and Gateway Devicementioning

confidence: 99%

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

Nilnoree,

Taparugssanagorn,

Kaemarungsi

et al. 2024

Applied Sciences

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“…ANNs are designed to perform functions, such as processing information, learning from it, recognising patterns, and predicting outcomes, similarly to how the biological nervous system functions in living organisms. By mimicking the human brain's basic operations, ANNs can solve complex problems, and they have numerous applications in natural language processing, image reading, and data prediction (see Figure 1a for reference) [15][16][17]. The structure of an ANN is a multilayer structure (input layer, hidden layer, and output layer) with interconnected neurons; a specific weight coefficient is assigned to links between neurons.…”

Section: Artificial Neural Network Morphologymentioning

confidence: 99%

“…The findings emphasise the pertinence of SHM for evaluating structural integrity while highlighting the ANN's capacity to assess damages beyond conventional methods. This underscores its practicality as an alternative tool for intricate structural evaluations in real-world scenarios [17].…”

Section: Introductionmentioning

confidence: 98%

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Ahmad,

Elahi,

Barbhuiya

2023

Materials

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This research aims to conduct a comparative analysis of the first crack load, flexural strength, and shear strength in reinforced concrete beams without stirrups. The comparison is made between the conventional model developed according to the current design code (ACI building code) and an unconventional approach using Artificial Neural Networks (ANNs). To accomplish this, a dataset comprising 110 samples of reinforced concrete beams without stirrup reinforcement was collected and utilised to train a Multilayer Backpropagation Neural Network in MATLAB. The primary objective of this work is to establish a knowledge-based structural analysis model capable of accurately predicting the responses of reinforced concrete structures. The coefficient of determination obtained from this comparison yields values of 0.9404 for the first cracking load, 0.9756 for flexural strength, and 0.9787 for shear strength. Through an assessment of the coefficient of determination and linear regression coefficients, it becomes evident that the ANN model produces results that closely align with those obtained from the conventional model. This demonstrates the ANN’s potential for precise prediction of the structural behaviour of reinforced concrete beams.

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Prediction of displacement in Reinforced concrete based on artificial neural networks using sensors

Cited by 2 publications

References 24 publications

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

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