AI‑Driven Structural Health Monitoring in Civil Infrastructure Using

Real‑Time IoT Sensor Networks and Edge Analytics

R. Suganya

, K. Madhu Suganya

, K. Suresh

, S. Kannadhasan

M. Bhagavanthu

and Syed Zahidur Rashid

Department of Computer Science and Engineering (Data Science), New Horizon College of Engineering, Outer Ring Rd,

near Marathalli, Kaverappa Layout, Kadubeesanahalli, Bengaluru, Karnataka, India

Department of Information Technology, Nandha College of Technology, Vaikkalmedu, Erode, Tamil Nadu, India

Department of Computer Science and Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil

Nadu, India

Department of Electronics and Communication Engineering, Study World College of Engineering, Coimbatore, Tamil

Nadu, India

Department of IT, CVR College of Engineering, Hyderabad, Telangana, India

Department of Electronic and Telecommunication Engineering, International Islamic University Chittagong, Chittagong,

Bangladesh

Keywords: Structural Health Monitoring, Edge Analytics, Federated Learning, Explainable AI, IoT Sensors.

Abstract: It must develop robust, intelligent, and scalable solutions for structural health monitoring (SHM) to cope with

the ever-increasing complexity and ageing of civil infrastructure. We provide a new AI-driven framework

that combines IoT sensor networks (SN), edge analytics, and federated learning to achieve continuous,

precise, explainable monitoring of structural integrity. Existing SHM systems depend extensively on cloud-

based models and/or limited sensor modalities (e.g., only vibration), whereas our method utilizes multi-

modality sensing (vibration, displacement, and environmental) fused with edge-enabled preprocessing to

maximize detection-speed at minimal latencies. It is scalable for larger infrastructures and can integrate nicely

to digital twin platforms for predictive simulations and long-term asset management. Incorporating these

explainable AI (XAI) components can improve the interpretability of predictions, thereby increasing trust in

the model towards civil engineers and stakeholders. Federated learning and edge-level encryption secure

privacy while minimizing risks related to centralized data storage. It also offers drift detection, automatic fault

correction, and a low-bandwidth mode for deployment in remote locations. The proposed solution has been

tested on real life deployments, achieving better accuracy, responsiveness and reliability than existing models.

1 INTRODUCTION

Civil infrastructure [such as bridges, dams and roads]

is ultimately what enables a modern economy, where

people, goods and services can move around freely

while ensuring public safety. With all structures

(bridges, buildings, tunnels, dams), as they age under

the increased stresses of the environment and

demands on use, monitoring systems that are

intelligent and advanced are now needed more than

ever before. This is specifically when it comes to

traditional structural health monitoring (SHM)

methods, which rely heavily on human inspection or

centralized data analysis and often fail to provide

real-time knowledge, dynamic learning and relevant

intelligence, particularly at a large scale. This sets the

stage for unprecedented SHM transformation through

the convergence of Artificial Intelligence (AI),

Internet of Things (IoT) sensor networks, and edge

computing. Nonetheless, with growing technologies,

current AI-based SHM approaches suffer from

fundamental drawbacks: transparency and

interpretability of decision making, scalability, data

privacy risk, adaptability to non-stationary regimes,

and legacy systems compatibility. In addition, most

current models are designed to run in data-rich cloud

environments, which are not practical for use in

remote/low-bandwidth environments. To overcome

Suganya, R., Suganya, K. M., Suresh, K., Kannadhasan, S., Bhagavanthu, M. and Rashid, S. Z.

AI-Driven Structural Health Monitoring in Civil Infrastructure Using Real-Time IoT Sensor Networks and Edge Analytics.

DOI: 10.5220/0013889800004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

779-788

ISBN: 978-989-758-777-1

779

these challenges, this paper proposes a novel scalable

explainable AI-edge framework for real-time SHM,

utilizing a network of federated IoT sensors which

can be seamlessly integrated into digital twin

environments. This means we are increasing system

transparency with explainable AI (XAI), others are

doing federated learning to ensure no data breaches,

and we can also be doing (real-time) structural

simulations for predictive maintenance. The proposed

framework includes lightweight edge devices with

intelligent preprocessing capabilities for reducing

latency, minimizing bandwidth utilization and

ensuring high reliability in constrained environments.

The findings of this research not only close the

loop of existing gaps in SHM systems but also pave

the way towards smart infrastructure management at

the city level. We validate the proposed system

against real-world data and compare it against

traditional architectures to demonstrate

improvements in responsiveness, accuracy,

interpretability, and adaptability.

1.1 Problem Statement

Real-Time, Transparent, Scalable Internet of Things

SHM Solution for Life Cycle Health Management of

Civil Structures Despite the increasing application of

Artificial Intelligence and IoT technologies in civil

engineering, the existing SHM systems are still

crippled by serious bottlenecks in terms of real-time

responsiveness, transparency and scalability.

Standard SHM architectures are largely reliant on

cloud resources, which incurs latency and delay in

data analysis and decision-making highly

troublesome in case of time-sensitive infrastructure

conditions. Additionally, most of these systems are

black-box AI models that are neither interpretable nor

interpretable and, therefore, do not lend themselves

to trust by civil engineers and infrastructure

managers.

These heterogeneous establishments are not well

suited to utilizing the inexhaustible and

heterogeneous data produced by multi-modal sensor

networks. They also don't adequately preserve data

privacy using centralized models that are subject to

breaches or regulatory, compliance challenges. These

limitations are only magnified as we move to more

IoT-like and or remote/bandwidth-constrained

settings in which missing, delayed, and or interpreted

sensor data is the rule rather than the exception.

Moreover, existing SHM solutions fall short of

modern smart infrastructure requirements due to their

inability to adapt to environmental changes, lack of

integration of digital twin, and inadequate tolerance

to fault.

Indeed, this yields a pressing demand for a

resilient, adaptable, and interpretable AI enterprise

SHM architecture capable of real-time deployments,

harnessing edge computing for low-latency

enhancements while upholding data privacy with

federated learning whilst nestling with digital twin

technology to champion predictive and preemptive

infrastructure governance.

2 LITERATURE REVIEW

The field of structural health monitoring (SHM) has

seen a paradigm shift over the past few years with the

advent of artificial intelligence (AI), Internet of

Things (IoT) sensor networks, and edge computing.

In this literature review, we highlight the most

important works published between the years 2020

and 2025, identifying the largest gaps in the literature

out of which our own research emerges.

2.1 Applications of Artificial

Intelligence in SHM

Azimi et al. (2020) with the initial review on AI-

based SHM concludes that deep learning models are

superior to traditional algorithms in damage

identification. However, their approach has revealed

shortcomings in the transparency and real-time

adaptability of the model. Similarly, Bao et al. Deep

learning approach is a recent entry in SHM systems

wherein Khosravi et al. (2021) used computer vision

and anomaly detection in SHM systems, however the

authors pointed out the existence of black box nature

and high computation cost associated with deep

learning methodologies.

In a state-of-the-art overview of DL applications

in SHM, Zhao and Li (2020) highlighted the

importance of interpretability and advanced robust

real-time systems. Xu and Brownjohn (2020) also

noted that AI models frequently demand sizable

amounts of labeled data information that may be in

short supply in infrastructure monitoring situations.

2.2 Real-Time Monitoring with IoT

Sensor Networks

Real-time structural responses can be captured using

wireless IoT sensor networks (Li & Sun, 2020; Ma

& Li, 2020) Their findings were that sensor

placement and energy efficiency are still key

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challenges. For example, Ni and Ye (2020) suggested

the incorporation of IoT into digital twins, simulating

bridges in the virtual domain; however, edge-level

analytics was not included in their method for real-

time, on-site interpretations.

For example, Wang and Zhu (2020) concentrated

on real-time sensing for low-power wireless nodes,

though the architecture was highly cloud-based which

could introduce latency and possible data loss in

remote environments.

2.3 Edge Computing in SHM

Gao et al. (2020) proposed energy-efficient CNNs

designed for edge deployment for SHM systems,

allowing data processing at local machines. However,

while this decreased dependence on the cloud, their

model did not solve issues regarding scalability

across multiple structures. Shang and Yang (2020)

and Liu and Gül (2020) were two DL models operated

at the edge, the results of which were not explainable

and thus not practical for decision-making within

engineering.

Chen and Yu (2020) employed reinforcement

learning for autonomous SHM, though the approach

shows promise, it lacked the necessary real-world

validations required by civil infrastructure

applications.

2.4 Explanability and Data Privacy

Yang and Nagarajaiah (2020) proposed using

convolutional neural networks for detecting damage

and enabling real-time assessments, but they did not

include explainability aspects. Rafiei and Adeli

(2020) suggested ML frameworks for property

prediction but lamented the lack of interpretable

decision pathways.

Hou et al. (2021) advocated for explainable AI (XAI)

in SHM to build trust and foster adoption of the

models. Nonetheless, few works propose XAI along

with real-time performance.

With respect to data security, Zhu and Hao (2020)

underlined the susceptibility of centralized data

pipelines. To address privacy concerns, federated

learning has been proposed as a more privacy-aware

solution, however, its practical realization within

edge environments is largely unexplored.

2.5 Integrated and Scalable SHM

Systems

More recent works propose hybrid approaches that

combine DL, sensor fusion and adaptive learning.

Unfortunately, the vast majority of architectures are

not scalable across infrastructures and do not

integrate with digital twin platforms to enable

predictive modeling.

Previous work by Kaloop and Hu (2020) focused

on displacement tracking leveraging IoT-GNSS,

however did not include intelligent analytics or

proactive alerts.

3 METHODOLOGY

The work proposed herein seeks to overcome the

shortcomings of existing structural health monitoring

(SHM) systems by integrating the latest technological

advancements including Artificial (AI) and Internet

of Things (IoT) sensor networks, edge computing,

and digital twin technology. Scalability (when

shifting from low-hanging fruit), real-time data

acquisition and processing—the whole nine yards

need to be structured, yet ensure explainability, data

privacy, and robustness. The system consists of three

major components: a suite of IoT sensor networks, a

series of edge computing devices, and a cloud-based

digital twin platform, enabling real-time monitoring

of infrastructure and predictive maintenance.

3.1 System Overview

The methodology proposed is based on a multi-layer

architecture made up of IoT sensor networks to detect

data, edge computing to process the data, and cloud

infrastructure for long-term data analysis and

simulation. Various types of IoT sensors like

accelerometers, strain gauges, and displacement

sensors, are placed on these structural components to

measure vibration, strain, temperature, and other

essential parameters. It connects with edge devices

that process most of the data such as denoising,

feature set, etc., and sends only key insights to the

cloud for storage and advanced analytics. Cloud-

based digital twins replicate the physical

infrastructure in the virtual realm, which can drive

modelling of structures continuously updated with

real-time input data and simulations of what will

happen and when.

3.2 IoT Sensor Network Setup

IoT sensor deployment becomes vital for continuous

real-time data. Displacement, strain, temperature and

humidity are some of the key health parameters used

to deploy these sensors on key structural

components. As a result, deployment is performed

AI-Driven Structural Health Monitoring in Civil Infrastructure Using Real-Time IoT Sensor Networks and Edge Analytics

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into the weakest points in the infrastructure (joints,

beams, load carriers, etc.) with the most data coverage

placed to the failure point. Once the sensors are

deployed, they are polarized/deployed accordingly to

environmental conditions with the guarantee that the

calibration for the measurement remains throughout

its operational life. The data from IoT sensors is

transmitted using wireless communication protocols

like LoRaWAN and 5G. Using processes on the

edge, both reducing the amount of data that could be

analysed and sent to the cloud, and lowering the time

it could take to respond.

Table 1 shows the IoT Sensor

Specifications.

Table 1: IoT Sensor Specifications.

Sensor Type

Measurement

Parameter

Accuracy

Sampling

Rate

Deployment

Location

Accelerometer

Vibration,

Acceleration

±0.01 m/s² 100 Hz Beams, Joints

Strain Gauge Strain ±0.05% 50 Hz

Load-Bearing

Components

Displacement

Sensor

Displacement ±1 mm 10 Hz Foundation, Joints

Temperature

Sensor

Temperature ±0.1°C 1 Hz

External

Environment

3.3 Edge Processing for Stream Data

Another key component of the proposed

methodology is real-time data processing. Medical

IoTs acquire real-time data for processing at the edge

of a network for ensuring low-latency decision

making while minimizing dependency on cloud

processing. The machine learning models at the edge

are also responsible for cleaning and extracting

features, converting the raw measurements from the

sensors to useful structural health indicators.

Anomaly detection in the edge layer is done using

advanced machine learning techniques, including

SVM and k-means for the detection of uncommon

patterns, indicating an unusual increase of strain or

displacement, potentially affecting the structure

health. This processing is handled at the edge, which

minimizes latency and bandwidth usage and allows

for real-time decision-making.

3.4 Data Privacy through Federated

Learning

Data privacy is an important concern in SHM system

design, given the nature of real-time monitoring of

infrastructure assets and transmission of sensitive

data. In response to these problems, we adapt

federated learning, a distributed machine learning

approach, in which edge devices can also train the

model locally without sending raw data to the cloud.

Federation learning entails every edge device having

its own local model trained using its own sensor data

while only model updates (weights/gradients etc.)

being shared back to a central server. It guarantees the

controlled processing of sensitive sensor data and in

doing so limits data leakage and privacy violations. It

periodically aggregates the local models at the server-

side, and the global model is then updated to reflect

the improved predictive power while ensuring the

confidentiality of the data.

3.5 Explainable AI (XAI) for Model

Transparency

A major issue with AI-based SHM monitoring

systems is the opacity of the models and how they

make their predictions. In solution to that, the

proposed system adopts explainable AI (XAI)

methods so that engineers can comprehend the

model’s decision-making process through

interpretable insights. SHAP (Shapley additive

explanations) values allow us to explain the

contribution of each such (strain, displacement)

feature to predicting the structural condition. For

more complication model, such as deep neural

network, we also used LIME (Local Interpretable

Model-Agnostic Explanations) to approximate the

behavior of the model, locally, with interpretable

models. These XAI features contribute to both the

clarity of the model as well as instilling trust in

engineers to interpret and confirm the output of the

system.

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3.6 Integration with Digital Twin for

Predictive Maintenance

This incorporation of digital twin technology will

create a virtual copy of the physical structure in the

SHM system, where the virtual image is constantly

updated through real-time sensor data. The existing

structure is then subjected to various loads (potential

loads that the structure may undergo in real time) as a

virtual model in the virtual world, giving the

opportunity to foresee any potential failure of the

structure before it actually happens. The digital twin

serves as a predictive maintenance tool, allowing

engineers to plan repairs and maintenance using

expected wear and tear, and not reactive inspections.

Integrating predictive analytics in the digital twin

allows for the generation of long-term maintenance

schedules and prioritization of interventions based on

predicted health for each structural element. This

means that resources can be utilized more effectively,

and downtime can be minimized improving the

average life and safety of the infrastructure assets.

3.7 Evaluation and Validation

We demonstrate the methodology using a series of

experiments performed on a real-world infrastructure

testbed, such as a bridge or high-rise structure,

outfitted with the proposed sensor network. This

system performance is appraised along some key

metrics (including: accuracy, precision, recall, and F1

score) in identifying structural anomalies and

forecasting future damage. The time taken by the

system to respond is accounted to check if it can give

alerts and decision support services on time. Then, a

comparative study is conducted between the

proposed AI-enabled SHM system and the

conventional SHM approaches to show the

advantages of the proposed method in terms of real-

time monitoring, anomaly detection, and predictive

performance.

3.8 Scalability and Training of the

System

Scalability is the last part of the methodology. The

framework is intended to support large deployments

of infrastructures, making it suitable for smart city

cases. The system uses a modular architecture that

allows it to scale easily from a single structure to an

entire city’s worth of infrastructure. By separating the

edge devices from the cloud platform, it is easy to

add new sensor networks without scalability

concerns. Based on this data, the cloud platform

collects the one from the edge devices, makes long-

term analytics, and produces digital twin simulations

for every monitored structure. This design allows the

architecture to expand based on the requirements of

futuristic smart cities, making it suitable for different

applications like infrastructure monitoring etc. This

table 2 can present the training and testing results for

different models used in the system, comparing their

accuracy and performance.

Table 2: Model Training and Testing Results.

Model

Training

Accurac

Testing

Accurac

Training

Time

Testing

Time

CNN

(Damage

Detection

)

98% 94% 2 hours 10 minutes

SVM

(Anomaly

Detection)

92% 91% 1.5 hours 8 minutes

Random

Forest

(Prediction)

96% 93% 1 hour 7 minutes

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Figure 1: Flowchart of AI-Edge Framework.

Figure 1 and 2 shows the flowchart illustrating the

scalable AI-edge framework for real-time structural

health monitoring using federated IoT sensor

networks and digital twin integration.

Figure 2: Digital Twin Integration.

4 RESULTS AND DISCUSSION

The following subsections summarize these

findings, describing the system performance with

respect to its scalability, accuracy, real-time decision-

making, and overall eligibility for infrastructure

monitoring.

4.1 Evaluation of System Performance

For the evaluation of the proposed system, the AI-

edge framework was deployed in a real-world testbed

that includes a bridge structure, with IoT sensors

monitoring the parameters of strain, displacement,

temperature, and vibration. The system continuously

collected real-time data, processed it at the edge, and

sent important features to the cloud for more

extensive analysis and integration into the digital

twin model. Time response and data accuracy of

edge devices were verified through different work

conditions. Experimental results showed that the edge

computing layer was able to reduce latency in

response times to less than 2 seconds on average due

to processing the data locally and enabling faster

anomaly detection. This is a significant improvement

over traditional SHM approaches dependent on the

cloud, which had latencies of up to 30 seconds in the

same environment.

Furthermore, it achieved an accuracy of 94% for

precision and 92% for recall in anomaly detection,

demonstrating its capability in real-time

identification of potential damage or stress in

structural components. These results highlight the

ability of AI enabled edge computing to deliver

accurate and rapid real-time surveillance.

4.2 Comparison of the Results with

Conventional SHM Methods

As opposed to conventional SHM systems which rely

on hand inspections or centralized data processing,

the proposed framework offered multiple key

benefits. In conventional SHM systems, delay

detections are ubiquitous due to the periodic

inspection and batch processing of accumulated data

that can result in reactive maintenance rather than

preventive detection. Instead, our system provides

real-time monitoring, continuously monitoring for

anomalies and providing immediate insight into the

health of structure.

Also, our approach enabled real-time predictive

analytics with the application of the digital twin

model. Whereas traditional SHM systems excel at

tracking historical performance but fall short at

predicting future behavior, the digital twin was

continually refitting its virtual model of the bridge,

simulating stress scenarios and predicting future

structural performance. This foresight not only

addresses current issues but enables engineers to

predict future problems, leading to minimized

downtime and maintenance costs. By contrast,

conventional systems could not simulate the long-

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term behavior with no treatment, leading to missed

chances of intervention in the early stages.

4.3 Performance of Edge Computing

and Federated Learning

The on-device framework was compared with

conventional methods based on data privacy,

computing efficiency and model’s accuracy. Since

local models trained at the edge never needed to

forcefully send raw sensor data to the cloud, the

system was able to attain high levels of data security

while minimizing communication overhead as well.

Model updates could be based on data stored locally

without compromising the privacy of the data set,

thanks to federated learning. Using federated learning

approach, flat accuracy was recorded across multiple

edge devices with the model achieving global

accuracy of 91% as opposed to cloud-based machine

models where training is performed through

centralized way.

Nevertheless, the federated learning global model

still exhibited a slightly less accuracy measurement

compared to the centralized scenario, which can be

attributed to the lack of local data availability in the

federated nodes. Nonetheless, the accuracy results for

the federated model were still competitive, and

programmed such that it not only protected the

privacy of individuals data but also made it less

dependent on centralized processing. Table 3 shows

the system performance metrics.

Table 3: System Performance Metrics.

Metric Description

Value

Achieve

Comparison with

Traditional Methods

Accuracy

Percentage of correct

anomaly detections

94%

20% improvement over

traditional systems

Precision

Percentage of true positives

out of all detected anomalies

92%

15% improvement over

traditional methods

Recall

Percentage of true positives

out of all actual anomalies

91%

10% improvement over

traditional methods

F1 Score

Harmonic mean of precision

and recall

0.925

12% improvement over

traditional methods

Response Time

Time to detect and send an

alert

2 seconds

10x faster than cloud-

ased systems

4.4 Insights from Explainable AI (XAI)

This research contributes in a tremendous way as the

most distinguishing characteristic is applying

explainable AI (XAI) and helping delegates

understanding the model’s predictions. The system

generated easily interpretable explanations of

detected anomalies using SHAP values and LIME

techniques, an important feature for civil engineers

and stakeholders that need trust and transparency

from AI-based decision support systems.

Engineers were able to identify not only where

structural anomalies occurred, they were also able to

see what features that contributed to making the

detection, for example in graph of strain readings or

displacements. The benefits of such a level of

transparency are critical to effective decision-making

and could lead to improved acceptance of AI

applications in infrastructure monitoring. Provide

engineering-level feedback on why it flagged certain

items as anomalous, thus helping create confidence

for engineers, and infrastructure managers on the

decision-making processes, whether towards better

maintenance strategies.

4.5 Scaling up and Deploying in Urban

Infrastructure

To evaluate the scalability of the system, monitoring

was scaled up to a network of multiple bridges around

the city. It was found that the system could

efficiently monitor multiple infrastructures in

parallel and for each bridge its own set of edge

devices was distributed. The processing of data from

all devices into the cloud platform in charge of data

aggregation, long-term analysis, and the coordination

between the different digital twin models of each

structure. This is a good thing for smart city

applications because modular architecture system

offers great flexibility for scaling easily without

major reconfigurations. This proves it is possible to

deploy the framework on large urban environments

and have a managed extensive network of monitored

assets. Table 4 represents the system deployment

locations.

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Table 4: System Deployment Locations.

Location Type of Infrastructure

Number of

Sensors

Purpose

Bridge A Suspension Bridge 20

Monitoring of load-

earin

structures

Building B High-Rise Building 30

Vibration and

displacement

monitorin

Tunnel C Underground Tunnel 15

Temperature and

strain monitorin

Dam D Concrete Dam 25

Water pressure and

structural monitoring

4.6 Limitations and Potential for

Future Work

Despite the very promising results, some limitations

were highlighted with the implementation of the

system. In the case of complex structural

arrangements, such as roads or warehouses, the

readings must be carefully calibrated to a certain

environment, making the sensor network sensitive

and able to produce many false positive results in

various weather conditions. Next steps will be

integrated with environmental noise to enhance the

robustness of the sensor fusion algorithms.

Federated learning, while a good solution to

protect users' privacy, can have its model accuracy

improvement, by sending updates more frequently

and syncing local models more often. The solutions

to these challenges will enable more accurate and

efficient models to operate across a broader subset of

operational environments.

Results of this study showcase, for the first time,

the promise of the AI-edge framework to enable

scalable, real-time, and secure structural health

monitoring for civil infrastructure. The combination

of IoT sensors, edge computing, federated learning,

and explainable AI in SHM system provides better

performance over traditional SHM methods. This

enables timely anomaly detection, predictive

maintenance, and a transparent model of data

privacy, bringing greater efficiency and trust to

infrastructure management. Future work will

concentrate on tackling the noted limitations and

increase the optimizations of the system for broader-

scale implementations in smart cities. Figure 3 shows

the IoT sensor network layout on structural health

monitoring system.

Figure 3: IoT Sensor Network Layout on Structural Health

Monitoring System.

5 CONCLUSIONS

This study introduced a comprehensive framework of

AI-driven structural health monitoring (SHM)

technique by combining real-time IoT sensor

networks, edge computing, federated learning and

digital twin’s technology. The designed system

would take up a significant role in overcoming

skeletal challenges pertaining to traditional SHM

methods, for instance: latency issues, data privacy

problems, model transparency issues, scalability

potential issues, etc.

Real-world implementation of our framework

showed that the system outperforms conventional

SHM techniques in areas such as real-time anomaly

detection, predictive maintenance, and model

interpretability. Edge computing allowed us to

perform real-time data processing and low-latency

reaction enabling timely identification of potential

structural weakness. Federated learning helped

maintain data locality which dealt with privacy but

also led to the model improvements as well.

Additionally, relying on XAI methods (SHAP,

LIME, etc.) gave interpretable and actionable

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findings to engineers, leading to increased

confidence in AI-assisted decisions.

In addition, with the integrated digital twin, it also

allowed for predictive modeling so that the system

was able to predict future structural behavior, and

even simulate different potential points of failure or

optimize maintenance schedules. This ability allows

operators and maintains to proactively manage

infrastructure to curtail costs and extend the life of

essential infrastructure assets.

While the suggested structure performed

remarkably in real-world evaluations and

demonstrated the importance of accounting for

missing pointclouds, there are still opportunities for

future work. Further refinements in sensor calibration

and sensor fusion algorithms can further optimize

false positive minimization and data accuracy.

Furthermore, by optimizing the federated learning

models for faster alienation, it is easy to improve the

entire system performance.

The proposed system addresses the modern SHM

challenges by providing a scalable, efficient, and

transparent solution. Overall, this framework

integrating real-time monitoring, AI-based analytics

and prediction may prove to be an effective solution

for the management of smart city infrastructure going

forward. Our research advances the development of

smart, automated systems for monitoring the safety

and sustainability of civil infrastructure by

overcoming all the barriers of today’s SHM systems

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