Civil Infrastructure Research Articles

Assessment of different optimal sensor placement methods for dynamic monitoring of civil structures and infrastructures

The optimal sensor placement is a critical aspect in the structural health monitoring of structures, as the number and layout of sensors directly impact the quality and usefulness of collected data. When developing a monitoring system, it is of paramount importance to use the minimum number of sensors to gain the maximum amount and quality of information. However, in the case of a limited number of sensors, their location on the structure becomes crucial. This study aims to assess the potential of several optimal sensor placement methods for the development of cost-efficient and well-dimensioned dynamic structural health monitoring systems to be implemented in a wide range of civil engineering structures and infrastructures. Indeed, the construction stock consists of a great variety of civil engineering structures and infrastructures that significantly differ from each other in terms of typology, historical period, construction techniques, materials, etc. Some of the most widely used optimal sensor placement methods are selected and applied in real case studies, and the obtained sensor configurations are discussed and compared. The results illustrated in this paper will contribute to defining good practice in the optimal sensor placement procedure for typical civil engineering structures and infrastructures.

Soil improved with a hybrid alkali-activated cement from waste stone wool and OPC

The use of OPC as a construction material is currently being reconsidered owing to the generation of greenhouse gases during production. Geopolymers or alkali-activated cement (AAC) have been proposed as partial replacements because of their excellent chemical and mechanical properties, which are equal to or superior to those of OPC. The use of these alkaline types of cement in soil stabilization has also gained significant interest in the academic community because of the possibility of it being derived from industrial waste. In this study, clay soil stabilized with AAC using waste stone wool fiber (SW) as a precursor and stabilized with hybrid alkali-activated cement (HAAC) using SW and OPC was prepared and subsequently evaluated mechanically and chemically after 28 days of curing. The results showed a significant increase in soil strength with both stabilization processes. The maximum achieved unconfined compressive strength (UCS) in the soil stabilized with AAC was 0.9 MPa (15 % SW), while the strength achieved with HAAC was 1.7 MPa (10.5 % SW - 4.5 % OPC). The California Bearing Ratio (CBR) of the latter combination was also determined, finding a value of 133 %, well above that of the soil without treatment (3.32 %). Through XRD, the A-type zeolite was identified as a product of the alkaline reaction, whereas the formation of N-A-S-H and C-A-S-H gels was observed via SEM. EDS mapping showed an increase in the atomic percentages of Si and Al in the soil specimens with HAAC, indicating the formation of Si-O-Si and Si-O-Al bonds. In addition to the potential application of this material in civil infrastructure related to soils (e.g., embankments and wall fillings), a sustainable construction material using industrial waste SW with a lower percentage of OPC is proposed.

Seismic vulnerability assessment of tunnels in algeria: An innovative hierarchical approach

Tunnels are crucial components of civil infrastructure, serving as vital transportation routes for the public. In Algeria, where many tunnels are located in seismic zones, they face considerable risks from earthquakes, which can potentially impair their operational integrity. The susceptibility of these tunnels to seismic hazards underscores the need for robust assessment frameworks to evaluate their vulnerability. This study introduces a novel hierarchical framework for assessing tunnel vulnerability, drawing on global seismic data and experience. By integrating the Analytic Hierarchy Process (AHP) with the Vulnerability Index (VI), we categorized tunnels into three vulnerability levels. This approach enabled the development of seismic scenarios to forecast their performance during future earthquakes. The methodology was applied to numerous tunnels across Algeria, yielding insightful results that align with observations from post-seismic assessments. By systematically analyzing various influencing parameters, including geological conditions, structural design, and operational factors, the study enhances our understanding of tunnel resilience in seismic contexts. Ultimately, this research contributes to proactive risk management strategies for tunnel infrastructure, aiming to mitigate potential damages and ensure their continued functionality in earthquake-prone regions like Algeria. By refining our assessment methods and predictive capabilities, we strive to bolster the resilience of critical urban infrastructure against seismic risks.

Compilation of Consolidation Properties Data of Champlain Sea Clay from Ottawa Region

Estimation of consolidation settlements in fine-grained soils due to various civil infrastructure loads is traditionally based on results derived from consolidation tests performed on undisturbed soil samples, combined with the data of other soil properties. In many geotechnical engineering applications, consolidation settlements are also estimated using empirical consolidation parameters derived from basic soil properties. This approach relies on correlations from the literature to bypass the time-consuming and expensive sampling techniques, laboratory testing, and other associated expenses. However, these correlations may not provide reasonable consolidation settlement estimations as these correlations are typically developed without considering the influence of stress history, geology, salinity of pore water, gradation, soil fabric, and chemical properties of the soils. This is especially true for Champlain Sea clay deposits from Eastern Ontario region of Canada that are typically with heterogeneous site conditions and exhibit spatial variability of soil properties. In this paper, data from the published literature and industrial and government reports on sensitive Champlain Sea clays were gathered for the Ottawa region. The data collection and clean-up methodology towards enhancing the reliability of the gathered data is comprehensively discussed. The summarized data from this study can be used with a greater degree of confidence towards developing reliable correlations in the estimation of consolidation settlements in geotechnical engineering practice.

Advancement of data-driven SHM: A research paradigm on AE-based switch rail condition monitoring

The past ten years have witnessed the tremendous progress of structural health monitoring applications in civil infrastructures. This is particularly embodied in railway engineering. The increasing train speed brings greater challenges to safety and ride comfort, and the primary theme of maintenance has been gradually altered from offline inspection to online monitoring. Rail operators must get an in-time warning of potential structural defects before critical failure takes place. It is more favourable that the rail operators can take hold of the real-time status of the key components and infrastructures in railway systems. This paper summarizes a long-term research series by the authors’ research team on online monitoring of rail tracks at turnout areas utilizing acoustic emission-based sensing technique, and more importantly, successively advancing signal processing methods and data-driven analysing frameworks, covering Bayesian inference, convolutional neural networks, transfer learning and task similarity analysis. The proposed algorithms tackle noise interference brought by wheel-rail impacts, great uncertainties in an open environment, and insufficiency of monitoring data, and realize comprehensive monitoring of rail tracks in turnout areas from basic crack detection to regressive condition assessment step-by-step.

Structural Damage Identification Using Autoencoders: A Comparative Study

Structural health monitoring (SHM) ensures the safety and reliability of civil infrastructure. Autoencoders, as unsupervised learning models, offer promise for SHM by learning data features and reducing dimensionality. However, comprehensive studies comparing autoencoder models in SHM are scarce. This study investigates the effectiveness of four autoencoder-based methodologies, combined with Hotelling’s T2 statistical tool, to detect and quantify structural changes in three civil engineering structures. The methodologies are evaluated based on computational costs and their abilities to identify structural anomalies accurately. Signals from the structures, collected by accelerometers, feed the autoencoders for unsupervised classification. The latent layer values of the autoencoders are used as parameters in Hotelling’s T2, and results are compared between classes to assess structural changes. Average execution times of each model were calculated for computational efficiency. Despite variations, computational cost did not hinder any methodology. The study demonstrates that the best fitting model, VAE-T2, outperforms its counterparts in identifying and quantifying structural changes. While the AE, SAE, and CAE models showed limitations in quantifying changes, they remain relevant for detecting anomalies. Continuous application and development of these techniques contribute to SHM advancements, enabling the increased safety, cost-effectiveness, and long-term durability of civil engineering structures.

Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks

Abstract Physics-based multi-scale corrosion simulation plays a vital role in predicting the evolution of pitting corrosion on large civil infrastructure such as miter gates, contributing to a model-informed structural health monitoring (SHM) strategy for risk-based asset health management. The physics-based analysis, however, may not accurately reflect the underlying true physics due to various uncertainty sources and needs to be updated using Bayesian inference methods based on observations to make the prediction closer to field observations. However, traditional Bayesian inference requires the evaluation of a likelihood function, which is often unavailable due to the complex model architecture and various surrogate models used in the analysis. Therefore, likelihood-free inference approaches are required for the updating of the multi-scale corrosion simulation models. This paper meets this need by proposing a conditional invertible neural network (cINN)-based Bayesian model updating method for an existing corrosion simulation model. We first train an cINN model based on simulated observations generated from a high-fidelity forward corrosion analysis model. A convolutional neural network (CNN)-based feature extraction algorithm is then employed to extract key features from corrosion images. After that, the extracted corrosion features from CNN is used as inputs of the cINN model to directly obtain posterior distributions of uncertain corrosion model parameters without evaluating the likelihood function. The results show that the proposed cINN-based model updating approach can provide more accurate inference results with a reduced computational cost in comparison to the classical approximate Bayesian computation (ABC) approach.

AIoT-enabled decentralized sensor fault diagnosis for structural health monitoring

Structural health monitoring (SHM) systems collect and analyze data recorded from civil infrastructure to detect changes in the structural condition and to predict potential damage. However, sensor faults in SHM systems may lead to erroneous structural assessments, thus to inefficient maintenance operations, which could jeopardize the integrity of the structures. Artificial intelligence (AI) algorithms based on analytical redundancy have proven being effective foundations to implement sensor fault diagnosis but are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. Moreover, operating with centralized servers increases the likelihood of single points of failure, which compromises the robustness of SHM systems. To overcome the limitations and to enhance the robustness of SHM systems, this paper proposes a decentralized approach towards fault-tolerant SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Fault diagnosis algorithms based on AI are embedded into sensor nodes deployed for SHM, using Internet of Things technologies to be computed at the edge of civil infrastructure, avoiding large amounts of data to be sent to central servers. In addition, the sensor nodes embed algorithms that collect, process, and analyze the sensor data to assess the condition of the structures being monitored. The result is a smart (i.e., decentralized, fault-tolerant, and AIoT-based) SHM system that is calibrated in a laboratory experiment and then deployed on a bridge for field validation. It is expected that the approach presented in this paper will improve the robustness of SHM systems in assessing the structural condition of civil infrastructure.

Implementation and validation of robot-enabled embedded sensors for structural health monitoring

In the past decades, structural health monitoring (SHM) has matured into a viable supplement to regular inspections, allowing to execute repair and maintenance work at early stages of structural damage, thus resulting in relatively low rehabilitation costs. With the advent of wireless technologies and advancements in information and communication technologies, civil infrastructure has been increasingly instrumented with wireless sensor nodes to record, analyze, and communicate structural data relevant to SHM. However, in harsh environments, wireless sensor nodes may be damaged and fail to operate. A promising alternative to wireless sensor nodes is to embed sensors directly into concrete. In this paper, a sensor system for embedment into concrete is proposed, able to self-assess the structural data recorded from the concrete. Moreover, the data and information provided by the sensors are collected by robotic systems via RFID data transfer in an attempt to reduce the amount of human interaction and to overcome the limitations with respect to communication range and power supply. In laboratory experiments, the sensor systems are embedded into a concrete structural element, validating the capabilities of the sensors to record and to analyze the structural data. Furthermore, as will be shown in the paper, power will be supplied on-demand by legged robots that are deployed to collect the data and information, which is provided by the sensors for persistent storage. In summary, this study corroborates that the embedded sensors represent a crucial step towards intelligent, self-assessing concrete.

Structural Health Monitoring on Road Bridge: Application Cases of Optical Strand Sensor in JAPAN

In Japan, many of the major civil infrastructures such as road bridges, railways and tunnels was built during our high-growth period from the mid-1950s to the early 1970s. The number of these structures that has been more than 50 years old after construction, has been rapidly increasing in recent years. In this context, expectations for Structural Health Monitoring (SHM) have been rising to optimize its management and maintenance to extend its lifespan and ensure the safety of users. Although a variety of IoT sensors are developing and their precision is improving day by day, SHM still has crucial challenges, e.g., if it can really identify critical damages to structures, determine quantitative assessment of the serviceability limit, evaluate the safety of a structure quickly after major events such as earthquakes, or so on. In order to achieve our original goals, it is essential to accumulate practical applications of SHM by dedicating a specific sensor solution to a specific issue. The authors have introduced a unique and competitive Optical Strand Technology developed by OSMOS Groupe in France and have been providing the service of SHM mainly on civil infrastructures for more than 20 years in Japan. This paper presents some application cases of SHM to road bridges by means of Optical Strand Sensor which provide for different types of purposes and future prospects.

Comparison between acoustic emission sensors and piezoelectric patches for damage detection in concrete beams

Acoustic emission (AE) sensing is an established technique to monitor crack nucleation and propagation in different types of structural elements and for different damage scenarios. Although effective for crack monitoring in civil infrastructure, off-the-shelf AE sensors can be expensive, discouraging their installation in large numbers for assessing the global structural performance or monitoring multiple assets in the infrastructure network. For this reason, this paper explores the use of piezoelectric patches as an alternative low-cost solution to AE sensors. These patches are considerably cheaper, do not require external power supply and can be easily attached to existing structural elements or embedded into new structures. Moreover, thanks to the direct and inverse piezoelectric effect, the same set of patches can act both as sensors and actuators, meaning that they can be used to “passively” monitor crack propagation over time, as well as to locate and characterize damage in ageing structures at a certain time by using the patches as emitters and receivers of waves. In this framework, this paper proposes a comparison between traditional AE sensors and piezoelectric patches. The comparison is performed on a set of three concrete beams subjected to four-point bending loading, each equipped with two grids of sensors, i.e. 8 AE sensors and 8 patches. The beams are tested under increasing monotonic loading, from zero-load uncracked condition up to collapse.

Characterisation Techniques for Bituminous Binder

Bituminous binders, often referred to as asphalt, play a pivotal role in the construction and maintenance of flexible pavements. As the demand for durable and sustainable infrastructure continues to rise, a thorough understanding of the physical and rheological properties of bituminous binders becomes imperative. This manuscript presents a comprehensive review of various mechanical tests used to assess bituminous binder properties, including penetration (Pen), softening point (SP), penetration ratio (PR), penetration index (PI), ductility tests and absolute and kinematic viscosities. These mechanical tests serve as fundamental tools for evaluating binder consistency, hardness, ductility and flow behaviour. SP and Pen measurements are employed to grade bituminous binders based on their response to temperature. Additionally, PR and PI provide valuable insights into binder performance under varying environmental conditions. Ductility tests offer critical information about a binder’s ability to elongate without breaking, a crucial factor in withstanding the stresses imposed by traffic. In parallel, the manuscript underscores the significance of understanding the rheological properties of bituminous binders. Dynamic shear rheometry (DSR) is introduced as a method for characterising complex modulus (G*), fail temperature and phase angle (δ) behaviour under different temperature and loading conditions. These tests are well established in the USA based on Strategic Highway Research Program (SHRP) specifications. Their relevance in the Indian context is discussed, with recent inclusion of rheological parameters in the Bureau of Indian Standards (BIS) specification. The manuscript highlights the role of instrumental analysis techniques, including Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR) and scanning electron microscopy (SEM), in uncovering the molecular and microstructural aspects of bituminous binders. FTIR aids in identifying functional groups within the binder, elucidating chemical composition and aging effects, while NMR provides insights into molecular mobility and heterogeneity. SEM is instrumental in revealing the binder’s microstructure and its interaction with aggregates. In conclusion, this manuscript offers a comprehensive analysis of both mechanical and rheological testing, emphasising their significance in evaluating bituminous binder properties. It also stresses the growing importance of instrumental analysis techniques, particularly for chemically modified bitumen. The primary aim is to provide a valuable resource for researchers, engineers and practitioners in the field of civil engineering and infrastructure development.

Case Study: Lightweight and Emergency Monitoring of a Post-disaster Bridge

In the realm of civil engineering, the health and integrity of infrastructural facilities are of paramount importance. Particularly in the aftermath of disasters, prompt and accurate structural assessments are crucial to ascertain the safety and functionality of structures. To this end, there is a pressing need for the design of such systems that can be rapidly deployed and provide accurate and reliable data in emergency situations. This paper presents a novel lightweight emergency monitoring system (LEMS) specifically tailored for a continuous rigid bridge in Anhui Province that had previously caught a fire, aimed at facilitating prompt post-disaster structural evaluations. We primarily aim to outline the design principles for such lightweight emergency monitoring system intended for post-disaster evaluations and to report the monitoring conditions and results gathered from years of data collection. Through the analysis of collected data, several significant insights have been derived, underscoring the effectiveness of the proposed LEMS in accurately representing the post-disaster structural behavior of the bridge, which provides invaluable insights for further inspections and evaluations. It contributes tangibly to the field of civil engineering and infrastructure maintenance by introducing a novel approach in the design and implementation of emergency monitoring systems specifically for post-disaster scenarios.

Acoustic Emission Detection in Noisy Environments using Linear Prediction

In non-destructive testing, acoustic emission testing is an established technique with numerous applications in academia and the industry. In nearly all applications, the detection of relevant acoustic emission signals in continuous structure-borne sound recordings is a crucial step that forms the basis for subsequent analyses and should therefore neither miss any relevant acoustic emissions nor detect too many irrelevant events. That is because, in its most prevalent form, acoustic emission testing utilizes ultrasound signals that require large amount of storage, which might be an economically limiting factor especially for structural health monitoring of large civil infrastructures. In these monitoring applications, the signal-to-noise ratio is also expected to be worse compared to more controlled laboratory conditions, as they can be often found, for example, in materials research. We therefore propose the use of linear prediction, a time series forecasting technique, to extract relevant acoustic emissions from continuous recordings. The proposed methodology utilizes the residual between the signal predicted from a linear combination of previous time steps and the actual measurement to detect any anomalous events. Since the linear predictor can be initialized using the environmental noise only, no specific knowledge of the acoustic emission signals of interest is required beforehand and hence can automatically adapt to each measurement environment. We compare our approach with a simple amplitude-based detection as it is commonly implemented in commercially available acoustic emission systems. Especially for low signal-to-noise ratios, an increase in the area under the receiver operating characteristic curve of up to 0.7 compared to the amplitude-based detection is found.

Diagnostics and Prognostics of Pitting Corrosion in Large Civil infrastructure Using Multi-scale Simulation and Machine Learning

Corrosion, particularly pitting corrosion, represents a prominent form of deterioration in large infrastructure, characterized by its gradual and continuous capacity-reducing nature. As structures age, the need for assessing and monitoring corrosion status becomes increasingly crucial for system life-cycle management and optimal maintenance. However, the intricacies of the corrosion process pose challenges to effective diagnosis and prognosis. These challenges include the insufficient integration of mechanical stress into existing corrosion simulations, the computational intensity of most physics-based corrosion simulations hindering probabilistic studies and real-time predictions, and the difficulty in detecting pitting corrosion due to its localized nature, making it impractical to measure individual pits. This paper introduces a comprehensive prognosis and diagnosis framework applied to a miter gate case study. The framework seamlessly integrates multi-scale simulation, including mesoscale Phase-Field (PF) simulation and macroscale structural analysis, with Machine Learning (ML) methods to enable real-time corrosion diagnosis and prognosis. It facilitates efficient structural health monitoring (SHM) through a digital twin (DT) for corrosion damage. The core components of this framework encompass the development of multiscale simulation for simulating pitting corrosion in large structures, the construction of ML-based surrogate models to expedite simulations, image-based diagnosis, and the updating engine for prognosis.

On the dynamics of the elastic concrete systems reinforced by advanced nanocomposites

This work investigates the vibrational properties of doubly curved elastic concrete panels reinforced with multi-hybrid nanocomposites under in-plane loading. These cutting-edge materials use the advantages of several nanocomposites to produce greater performance by integrating many kinds of nanofibers and nanotubes to boost mechanical qualities. The study focuses on the effects of these reinforcements on the dynamic behavior of doubly curved panels, which are a typical structural component in contemporary engineering applications including civil infrastructure, automotive, and aerospace. The study investigates the natural frequencies and dynamic stability of the panels under in-plane stress circumstances using a thorough analytical method. The complicated geometry and material heterogeneity are modeled using mathematical modeling, which offers comprehensive insights into the impact of nanocomposite reinforcements. The findings show that, in comparison to conventional concrete panels, multi-hybrid nanocomposites considerably increase stiffness characteristics and vibrational performance. The results highlight the potential of these novel materials to transform structural component design and application, resulting in more robust and effective engineering solutions. By improving our knowledge of hybrid nanocomposite behavior under dynamic stresses, our study opens the door to a wider use of these materials science and engineering principles in materials science and engineering.

Applications of mathematical morphology operators in civil infrastructures

Civil infrastructures require a permanent attention and maintenance from the moment of commissioning to moment of demolition. One important aspect which is mandatory to be taken into consideration is crack detection. Cracks can appear during the lifetime of the civil infrastructure and require specialized personal for assessment. Depending of the civil infrastructure, this operation can require specialized skills (such as climbing). To overcome this issue with regards to specialized manpower, image processing is used. Nowadays, images can be easily acquired using an unmanned aircraft vehicle system known also as a drone. The main advantages of a drone for civil infrastructure image acquisition are: i) it can be operated at different heights, ii) rapid data collection, iii) cost and time savings, iv) user-friendly interface.The main purpose of our paper resides in improving the accuracy of the pre-trained neural networks when noisy images are used in civil works. Throughout our research, we used a dataset which contains three classes of images: with cracks, without cracks and with noise. To remove the noise presented in images mathematical morphology operators (MMO) are used. Our results reveal that using opening operator filter on a dataset of images which present civil infrastructure cracks outperform the results obtained using only pre-trained neural networks. In addition, the computation time is reduced.

Investigation of Issues in Data Anomaly Detection Using Deep-Learning- and Rule-Based Classifications for Long-Term Vibration Measurements

Structural health monitoring (SHM) systems are widely used for civil infrastructure monitoring. Data acquired from the SHM systems play an important role in assessing structural integrity and determining further maintenance activities. Considering that sensors in the SHM systems are installed in a harsh environment for long-term measurements, some sensors can malfunction and produce faulty data. As a large amount of measured data is often desired to be automatically processed and can adversely affect structural assessments, identifying such abnormal data is important. This paper provides critical investigations of the automated detection of data anomalies using existing deep-learning-based classification in conjunction with a simple rule-based approach. The issues investigated in this study include (1) the presence of ambiguous data that cannot be categorized as an anomaly class in the literature, (2) information loss during the conversion of time-series data into images for the deep-learning-based approach, and (3) additional issues, such as misclassification by trained models and requirements of the threshold section in the rule-based approach. The results of these key investigations can be utilized to develop an effective anomaly detection process.

A Low-Cost Sensing Solution for SHM, Exploiting a Dedicated Approach for Signal Recognition.

Health assessment and preventive maintenance of structures are mandatory to predict injuries and to schedule required interventions, especially in seismic areas. Structural health monitoring aims to provide a robust and effective approach to obtaining valuable information on structural conditions of buildings and civil infrastructures, in conjunction with methodologies for the identification and, sometimes, localization of potential risks. In this paper a low-cost solution for structural health monitoring is proposed, exploiting a customized embedded system for the acquisition and storing of measurement signals. Experimental surveys for the assessment of the sensing node have also been performed. The obtained results confirmed the expected performances, especially in terms of resolution in acceleration and tilt measurement, which are 0.55 mg and 0.020°, respectively. Moreover, we used a dedicated algorithm for the classification of recorded signals in the following three classes: noise floor (being mainly related to intrinsic noise of the sensing system), exogenous sources (not correlated to the dynamic behavior of the structure), and structural responses (the response of the structure to external stimuli, such as seismic events, artificially forced and/or environmental solicitations). The latter is of main interest for the investigation of structures' health, while other signals need to be recognized and filtered out. The algorithm, which has been tested against real data, demonstrates relevant features in performing the above-mentioned classification task.

Enhancing Concrete Durability: Sustainable Ternary Blends Incorporating Agricultural and Industrial Wastes

<p>The demand for sustainable concrete in civil infrastructure operations grows by the day, resulting in the use of agricultural and industrial byproducts and controlling high-cost nanomaterials. Ternary blended concrete is an excellent alternative to conventional concrete for long-term development by lowering carbon footprint. Ternary blended concrete is a green and sustainable concrete made from three separate source components to make a binder. The fundamental benefit of ternary mixed concrete is its densely packed particles of various shapes and sizes, resulting in superior characteristics. This study discusses experimental studies that were conducted to assess the durability properties of regular ordinary Portland cement (OPC) concrete and ternary blended concrete containing 1% nano-SiO2 and industrial or agricultural byproducts. Concrete durability is defined as its capacity to sustain a safe degree of serviceability and various environmental exposure circumstances without requiring considerable repair or rehabilitation during its service life. Conventional concrete is prone to cracking because of its low tensile and durability properties. Six potential mixes were investigated in this work: three blends based on industrial byproducts (fly ash, ground granulated blast furnace slag, and metakaolin) and three mixes based on agricultural byproducts (rice husk ash, corncob ash, and sugarcane bagasse ash). This study investigated the durability characteristics such as water permeability, sorptivity, resistance to sulphate, acid attack, and alkaline attack. The experimental test findings were determined to be well within limitations when compared to the requirements specified in the standard/literature. The study also shows that ternary blended concrete with regulated nano-SiO2 enhances durability metrics significantly. Furthermore, energy dispersive spectrophotometry (EDS) measurements show dense phases with high proportions of Si atoms in the substrate's penetration layer. As a result, ternary mixed concrete is strongly advised in structural repair works.</p>