Civil Structures Research Articles

Quantum‐Based Combinatorial Optimization for Optimal Sensor Placement in Civil Structures

Over the last decade, concepts such as industry 4.0 and the Internet of Things (IoT) have contributed to the increase in the availability and affordability of sensing technology. In this context, structural health monitoring (SHM) arises as an especially interesting field to integrate and develop these new sensing capabilities, given the criticality of structural application for human life and the elevated costs of manual monitoring. Due to the scale of structural systems, one of the main challenges when designing a modern monitoring system is the optimal sensor placement (OSP) problem. The OSP problem is combinatorial in nature, making its exact solution infeasible in most practical cases, usually requiring the use of metaheuristic optimization techniques. While approaches such as genetic algorithms (GAs) have been able to produce significant results in many practical case studies, their ability to scale up to more complex structures is still an area of open research. This study proposes a novel quantum‐based combinatorial optimization approach to solve the OSP problem approximately, within the context of SHM. For this purpose, a quadratic unconstrained binary optimization (QUBO) model formulation is developed, taking as a starting point of the modal strain energy (MSE) of the structure. The framework is tested using numerical simulations of Warren truss bridges of varying scales. The results obtained using the proposed framework are compared against exhaustive search approaches to verify their performance. More importantly, a detailed discussion of the current limitations of the technology and the future paths of research in the area is presented to the reader.

ХХ ҒАСЫРДЫҢ 20-ЖЫЛДАРЫНДАҒЫ ЖЕТІСУДАҒЫ БОСҚЫНДАР ЖӨНІНДЕГІ КЕҢЕСТІК ЕРЕКШЕ КОМИССИЯ ҚЫЗМЕТІ

The fate of the refugees of the first quarter of the 20th century, who participated in the uprisings in Zhetysu and were forced to flee their homeland, is a little-studied topic in the history of political cataclysms in Kazakhstan. The purpose of the proposed article is to reconstruct the activities of the special commission of the Central executive Committee of the Turkestan Republic, whose purpose was the return and accommodation of refugees from the Zhetysui region who left the cordon after the uprising of 1916 and the first years of the establishment of Soviet power in Russia, primarily to Western China. The evolution of the Soviet position in connection with the return of refugees from the policy of retaliation by the authorities of Tsarist Russia for the national liberation uprising in the region and the continued persecution of the indigenous population in the early years of the establishment of Soviet power depended entirely on the internal socio-political and geopolitical situation. In the course of the study, a descriptive method was used that contributes to the formation of a real-life image. The descriptive method allows us to delve into the historical reasons for the formation and periodization of refugee as a phenomenon. As a result of the scientific research, the conditions for the creation, aspects and structure of the activities of the Soviet special commission for the return of refugees were systematized for the first time. The situation of refugees in the host country was examined on the basis of evidence. The socio-economic and political reasons for the measures for the return and placement of refugees, its progress and results are analyzed using important sources from the funds of foreign and domestic republican archives, as well as special funds of regional archives directly related to the object of research. For the first time in Russian historical science, the scientific study analyzes the degree of interaction, the reasons for the contradiction of the position of Soviet civil, military and diplomatic structures on solving the refugee problem in the 20s of the XX century. The results of the research can be used in special courses, textbooks, educational publications on the humanitarian field of higher educational institutions on the problems of social history and migration of the first quarter of the 20th century.

Development of generic AI models to predict the movement of vehicles on bridges

For civil, mechanical, and aerospace structures to extend operation times and to remain in service, structural health monitoring (SHM) is vital. SHM is a method to examining and monitoring the dynamic behavior of essential constructions. Because of its versatility in detecting unfavorable structural changes and enhancing structural dependability and life cycle management, it has been extensively used in many engineering domains, especially in civil bridges. Due to the recent technical developments in sensors, high-speed internet, and cloud computing, data-driven approaches to structural health monitoring are gaining appeal. Since artificial intelligence (AI), especially in SHM, was introduced into civil engineering, these modern and promising methods have attracted significant research attention. In this work, a large dataset of acceleration time series using digital sensors was collected by installing a structural health monitoring (SHM) system on Nibelungen Bridge located in Worms, Germany. In this paper, a deep learning model is developed for accurate classification of different types of vehicle movement on the bridge from the data obtained from accelerometers. The neural network is trained with key features extracted from the acceleration dataset and classification accuracy of 98 % was achieved.

Uncertainty Quantification of the Crack Propagation Behavior in Welded Stiffened Panels Using a Hybrid Framework Integrating Artificial Neural Networks and Finite Element Analysis

Predicting the crack propagation behavior in welded stiffened panels often used in marine, civil, and aerospace structures, has been challenging. The presence of welded stiffeners creates complex stress conditions that need to be properly considered while predicting the crack growth. Another challenge is the presence of significant uncertainties related to the variability in the geometric parameters. In this context, the proper consideration of the uncertainties associated with these parameters is crucial for the accurate prediction of the fatigue service life and for ensuring structural integrity and operational reliability throughout the service life. This paper conducts a sensitivity analysis to evaluate the effect of relevant input parameters, covering the characteristics of the main panel and stiffener characteristics, on the crack propagation behavior. A 3D finite element analysis, an artificial neural network, and an elastic-plastic crack growth model are integrated to predict crack propagation under cyclic loading. The sensitivity assessment approach is illustrated on stiffened panels with T- and L-shape stiffeners subjected to axial tensile fatigue loading. The proposed approach is validated using experimental test data reported in literature.

Application of Digital Twin in Structural Health Monitoring of Civil Structures: A Systematic Literature Review Based on PRISMA

Structural Health Monitoring (SHM) systems are critical for ensuring the safety and longevity of structures. Digital twin technology has emerged as a promising tool to improve the accuracy and efficiency of SHM systems. This paper presents a systematic literature review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) on the applications of digital twin for SHM of civil structures. And this systematic literature review examines the current trends and developments in the application of digital twin technology in SHM systems for structures. The review identified 45 relevant articles published between 2010 and 2023 that covered various applications of digital twin in SHM systems, including buildings, bridges, wind turbines, trains, and gas turbines. Key themes identified from the literature review included the integration of physical models and machine learning algo-rithms, the use of distributed sensor networks, and the importance of real-time data analysis for effective SHM. The findings suggest that digital twin technology has the potential to significantly improve the accuracy and efficiency of SHM systems, but further research is needed to develop more advanced and integrated Digital Twin-Based SHM systems for different types of civil structures.

3D-printing technology for integrating the rehabilitation and monitoring of civil structures with fiber optic

In recent years, 3D printing technologies have become increasingly widespread in the field of architectural restoration of historical buildings and monuments. This is due to 3D printing’s capability to accurately reproduce complex shapes with simplicity. Currently, within this field, 3D printing technology is generally employed for the physical reproduction of decorative/architectural components, while the reproduction of structural elements is still in its early stages. In this context, the paper presents an experimental study on 3D-printed samples made of PLA (Polylactic Acid Material), equipped with fiber optic sensors introduced inside the samples during the printing process. This innovative approach facilitates reintegration interventions while enabling real-time monitoring of structures, marking a significant advancement in the field. However, since the fiber optic is incorporated within the element during the printing process, it is crucial to assess its potential impact on the mechanical properties of the samples and reliability of measurement system itself. Experimental tensile tests are then performed on samples carried out by considering both different printing paths and, moreover, different arrangements of the fiber optic inside the samples. Lastly, the study also examines the impact of artificially induced aging effects to assess their influence on both the mechanical properties of the samples and the reliability of measurements obtained from the fiber optic sensors.

Routine monitoring of hydraulic infrastructures using the European Ground Motion Service and other satellite radar sensors

Ensuring the safety and operational efficiency of hydraulic infrastructures is paramount, considering the widespread consequences that damages can inflict on people, communities, and the environment. To mitigate risks and prevent significant losses, continuous surveillance is vital. While some damages might appear minor, they can jeopardize the complete operational reliability of dams, leading to substantial economic losses, especially in energy production and related activities. The rapid growth in 20th-century infrastructure development globally has made security monitoring a necessity for numerous civil structures. Rigorous inspection programs, particularly for reservoir dams, are essential for safeguarding citizens and their properties. However, individually monitoring each dam is often impractical due to the associated costs and time constraints, potentially posing safety risks. Fortunately, satellite-based differential radar interferometry (DInSAR) offers an effective and cost-efficient remote sensing solution. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques, particularly utilizing Persistent Scatterers, have proven successful in monitoring various infrastructures, natural phenomena, and geological activities. MT-InSAR provides precise measurements without the need for fieldwork, utilizing historical SAR image archives dating back to the 1990s. Technological advancements, such as the Sentinel-1 C-band with a six-day revisit time until the end of 2021, have enhanced monitoring capabilities. Additionally, commercial radar images in the X-band and the development of multi-interferometric InSAR techniques have opened new avenues for monitoring. This study showcases the adaptation and application of MT-InSAR for monitoring dams and large ponds constructed with loose materials. By assessing vertical displacements and consolidation rates, the technique identifies potential issues, aiding in further field investigations. Case studies involving dams and large reservoirs in Andalusia illustrate the effectiveness of satellite radar interferometry in monitoring their structural stability from space as a routine practice.

Damage Detection and Condition Assessment of Civil Engineering Structures Using Structural Health Monitoring

Damage identification is critical in evaluating the condition and performance of the existing civil infrastructure. This requires not just routine or critical event-based inspections, but rather a means of continuous monitoring of a structure to provide an assessment of changes as a function of time and an early warning of an unsafe condition using real-time data. Health monitoring plays an important role in the construction and maintenance of the infrastructure. It employs in situ, continuous, or regular measurements and analyses of key structural and environmental parameters under the operating conditions to identify if a damage has occurred, define its location, and estimate its severity, evaluate its consequences on the residual life of the structure also to provide warning of impending abnormal states or accidents to avoid casualties and to inform maintenance and rehabilitation decisions. SHM takes advantage of the new technologies in sensing, instrumentation, communication, and modelling to integrate them into an intelligent system. Structural Health Monitoring for civil structures is becoming increasingly popular in Europe and worldwide because of the opportunities that it offers in the fields of construction management and maintenance. Reduction of inspection costs, research, with the possibility to better understand the behaviour of structures under dynamic loads, seismic protection, observation, in real or near real-time, of the structural response and of evolution of damage, so that it is possible to produce post-earthquake scenarios and support rescue operations, are the main advantages related to the implementation of such techniques.

Seismic Analysis of High-Rise Building using Negative Stiffness Device

Abstract: Natural hazard mitigation is one of the most important issues facing civil engineers today. In structural engineering, one of the constant challenges is to find new and better means of protecting existing and new civil structures from the damaging effects of destructive environmental forces, such as wind, waves and earthquakes. Earthquakes are considered the most destructive environmental forces for civil engineering structures. Seismic forces and displacements in existing structures can be effectively reduced in an approach where the structure is intentionally weakened (stiffness and strength are reduced) and damping is added. However, the approach also results in inelastic excursions and permanent deformation of the structural system during a seismic event. A new concept previously proposed by the authors simulates apparent weakening by incorporating a mechanical system that produces true negative stiffness in the structural system. In doing so, inelastic excursions and permanent deformations may be substantially reduced or eliminated. True negative stiffness means that the force must assist motion, not oppose it as in the case of a positive stiffness spring. A passive device capable of exhibiting true negative stiffness, negative stiffness device (NSD), without external power supply is studied in this project work. A pre-compressed spring is used to generate the force to push the structure and a lever-mechanism is adapted to amplify the generated force.

수처리용 섬유여과기 성능평가인자 도출 및 평가방법개발에 관한 연구

This study investigated the performance evaluation factors for the fiber filtration process in water treatment and researched the performance evaluation items and methods for assessing these factors. South Korea's water supply and sewage policies focus on modernizing civil structures and networks and introducing advanced treatment facilities. Reflecting these policies, the Waterworks Act describes the minimum requirements that water supply facilities must meet to ensure a consistent quality (water quality) of drinking water. Therefore, the Waterworks Act has introduced usage standards for water treatment processes and materials for water supply to manage the quality of drinking water. Firstly, the Ministry of Environment has issued water supply facility standards, which explain the minimum standards for water supply facilities. Secondly, there are hygiene and safety standards (suitability certification) and group standard certification. Standards and certifications are established to evaluate materials and products suitable for water supply facilities. Thus, two criteria are required to apply them to water supply facilities.
 In Republic of Korea, a new concept of water treatment process has been developed that can replace traditional water treatment processes. This involves a fiber filtration process that contracts and relaxes fiber strands, removing turbidity and suspended solids. There is a movement to adopt this process. Such fiber filters can provide better treatment efficiency, use smaller sites, and have faster processing speeds. However, the use of different evaluation methods by manufacturers has prevented consumers from accurately understanding the performance and raised doubts about the reliability of the technology.
 The result of this study is the development of a standardized performance evaluation method, and the derivation of performance evaluation factors for fiber filters. Through standardized performance evaluation factors and methods, an objective performance evaluation of the fiber filtration process can be conducted.

Self-sensing concrete masonry structures with intrinsic abilities of strain monitoring and damage detection

Self-sensing cementitious materials are emerging technologies for Structural Health Monitoring (SHM) of civil structures. Their application for SHM of concrete masonry elements was not investigated in previous studies. The effects of triaxial strain/stress states of masonry joints and units on their self-sensing response are still unknown. Therefore, this work aimed to investigate mechanical and self-sensing properties of masonry elements produced with self-sensing mortar joints, solid concrete bricks and/or hollow concrete blocks fabricated with carbon black nanoparticles. The intrinsic abilities of strain monitoring and damage detection were evaluated in piezoresistive tests of masonry prisms, based on variations in the type of unit, mortar bedding approach, bonding arrangement, relative strength of mortar and units, joint thickness and location of self-sensing regions. These variations did not affect significantly the gauge factor of cementitious sensors embedded into the prism units. In contrast, the gauge factor of self-sensing horizontal or vertical joints was statistically affected by most of these variations. Stages of balance and abrupt increases in fractional changes in electrical resistivity (FCRs) during the plastic regime of the prisms provided an anticipated warming associated with the propagation of vertical and diagonal cracks associated with tensile splitting and crushing failure of units. The strong non-linearity on the electrical output of self-sensing joints provided an evidence of the mortar pore collapse. In conclusion, the self-sensing units and mortar joints were found to be promising alternatives for strain monitoring and damage detection in smart concrete masonry structures.

Development of a procedure for model selection in machine learning-based methods for structural damage detection

Output-only methods based on machine/deep-learning algorithms are highly practical approaches for timely detecting potential damages in civil structures as they directly employ measured vibration signals but do notrequire exact knowledge of input loading nor the service interruption for manual inspection. However, there isno one-size-fits-all model that is optimal for all problems in different perspectives; hence, it is necessary todiscover the advantages as well as drawbacks of different models, then leverage these understandings to selectthe most appropriate model for specific structures in reality. Therefore, this study develops a framework thatfacilitate the model selection by extensively comparing various machine learning-based methods ranging fromrelatively simple ones such as Na¨ıve Bayes to complex ones such as the extreme boosting tree-based ensemblemodel. The framework can provide comparison results include various aspects such as model complexity, training time, detection accuracy, and inference time.

Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks

This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natural frequencies or mode shapes), and output factors that represent the damage situation of elements or regions in a structural system. Unlike many papers in the literature that test damage detection methods only in numerical examples or simple experimental tests, this work also assesses the presented method in a real structure showing that it has potential for applications in real practical situations. Three different cases are evaluated through the methodology: numerical simulations, an experimental lab structure, and a real bridge. Initially, a cantilever beam and a 10-bar truss were numerically analyzed under ambient vibrations with different damage scenarios and noise levels. Afterward, the method is assessed in an experimental beam structure and in the Z24 bridge benchmark. The numerical simulations showed that the methodology is promising for identifying, locating, and quantifying single and multiple damages in a single stage, even with noise in the acceleration signals and changes in the first vibration mode of 0.015%. In addition, the Z24 bridge study confirmed that the damage detection method can localize damage in real civil structures considering only natural frequencies in the input factors, despite a mean difference of 4.08% between the frequencies in the healthy and damaged conditions.

Investigation of optimum tuned mass damper parameter according to stroke capacity

During major earthquakes, civil structures may collapse due to vibration that has a frequency close to the frequency content of the structure. Because of this, control systems have also been proposed for building structures. These systems can be active ones that are controlled by electronic devices or passive ones that are tuned mechanical systems. Passive tuned mass dampers (TMDs) include mass, stiffness element and damping element and these are tuned around the frequency of the structure. For optimum tuning and the complex nature of the mathematics under random vibrations, metaheuristic algorithms are needed to be used. In the presented study, TMDs are optimized via Jaya algorithm. The control system was optimized for displacement minimization of the structure. Additionally, the stroke amount of the system was limited. The stroke capacity factor was investigated for wide limits between 0.5 and 4 for normalized stroke according to the maximum displacement of the structure. The investigation was done for a single degree of freedom structure for a general conclusion. It is observed that the stroke limit did not affect performance and optimum parameters after 2.75. The small values of the stroke limit have significantly different optimum period. <w:LsdException Locked

Real-Time Integration of Identification and Semiactive Optimization Control for Mass Damper-Building Combined Systems under Known/Unknown Seismic Excitations

The integration of structural identification and vibration optimal control has been studied. Since the semiactive optimization vibrational control of civil structures needs to be implemented by massive control devices such as mass dampers, it is necessary to investigate the real-time integration of identification and semiactive optimization vibration control for mass damper-building combined systems. However, there is a lack of such studies in the literature. In this paper, a methodology is presented for real-time integration of identification and semiactive optimization vibration control of the mass damper-building combined system under known/unknown seismic excitations. For the combined system under known seismic excitations, the identification is implemented by the extended Kalman filter (EKF) using only partial structural acceleration responses. The identified structural state and parameters of mass damper-building systems are integrated in real time for the optimal control of systems by the linear-quadratic regulator (LQR) control algorithm and the Hrovat semiactive optimization control strategy via semiactive optimization mass dampers (SAMD). Then, it is extended to the scenario of unknown seismic excitations. The partially measured structural acceleration responses are absolute ones in this case, so the generalized extended Kalman filter with unknown input (GEKF-UI) developed by the authors is used to identify the structural input-state parameters of the mass dampers-building combined systems. The identification results are also integrated in real time for the semiactive optimization control of the combined system via SAMD. Two numerical simulation examples are used to test the proposed integration methods. It is shown that the proposed integration methods can reach almost the same optimal control effects as the conventional semiactive optimization control with known parameters of the mass damper-building combined systems under known/unknown seismic excitations.

Algebraic PI observer for velocity and displacement in civil structures from acceleration measurement

This study aims to deal with the velocity and displacement estimation problem in civil structures through acceleration measurements. Nowadays, different schemes have been proposed to solve this problem, however, most proposed observer schemes generally require the Luenberger structure and sometimes any base transformation. In contrast, this paper provides a novel algebraic Proportional Integral (PI) observer based on the Algebraic Observability Condition (AOC), which allows the representation of the velocity and displacement terms as a function of the acceleration measurements, does not need a coordinate transformation, and uses proportional and integral correction of the estimation error. Moreover, a simple criterion for the observer gains selection is provided in order to ensure asymptotic convergence which leads to the fact that the gains can be assigned without using any numerical method to calculate them. An advantage of this observer is that it can be easily implemented for practical purposes. Finally, the effectiveness and accuracy of this estimation method are demonstrated experimentally via a reduced-scale five-story building prototype.

A COMPREHENSIVE STUDY PROPOSING THE GUIDELINES FOR REDUCING THE C&D WASTE IN THE CITY OF NAGPUR

"Construction and demolition waste" “means building materials, debris, and rubble resulting from the construction, remodeling, repair, and demolition of any civil structure (CPCB, Ministry of Environment, Forests & Climate Change, 2016).” The data about various categories of cities was approximated based on insights derived from the secondary literature. In 2016, the metropolitan areas in India generated an excess of 150 million metric tons of Construction and Demolition (C&D) waste. (CPCB, Ministry of Environment, Forests & Climate Change, 2016), with small and medium-sized cities accounting for about half of the total. This research examines the current evolution of standards in Nagpur city, analyzes the current situation, and proposes guidelines to improve the situation. With a focus on developing comprehensive and effective strategies, this study seeks to address the unique challenges posed by C&D waste in the urban context of Nagpur. By analyzing existing practices, understanding the specific waste generation dynamics, and proposing standardized guidelines, this research aims to contribute to sustainable and streamlined C&D waste management, enhancing environmental responsibility and urban well-being in Nagpur. A questionnaire survey conducted among practicing and reputable builders in the Nagpur region has provided valuable insights and analysis to guide specialized recommendations for reducing C&D waste production. Ultimately, this study seeks to educate decision-makers in the industry, key players, and environmental activists to promote C&D waste management and enhance urban life in Nagpur.

Deformation prediction of large-scale civil structures using spatiotemporal clustering and empirical mode decomposition-based long short-term memory network

Structural deformation prediction is important for maintaining the serviceability and safety of civil infrastructure. However, current deep learning-based single prediction models face difficulties in accurately tracking deformation evolution of large-scale civil structures with numerous diverse monitoring data. In this study, a novel deformation prediction framework is proposed based on a spatiotemporal clustering algorithm and an empirical mode decomposition (EMD)-based long short-term memory (LSTM) network. Experiments on a large excavated foundation pit show that the proposed framework has a prediction error of RMSE less than 0.4 mm, outperforming LSTM models without using spatiotemporal clustering or EMD. Such high-precision predictions at many different monitoring locations demonstrate its promising application on large-scale structures. More deformation prediction experiments are required in the future to further validate the capability and performance of the proposed framework.

Time Series Analysis Methodology for Damage Detection in Civil Structures

Structural health monitoring (SHM) methodologies employing data-driven techniques are becoming increasingly popular for detection of structural damage at the earliest stage possible. With measured vibration signals from the structure, time series modeling methods provide quantitative means for extracting such features that can be utilized for damage diagnosis. In this study, one-step prediction error of an autoregressive (AR) model over a data set is used as damage indicator. In particular, the difference between the prediction of the AR model that is fit to the measured acceleration signal obtained from the intact structure and actual measured signals collected for different damage states of the structure are interrogated for diagnosis purposes. More specifically, the standard deviation of the residual error is employed to locate the damaged region. Singular-value decomposition (SVD) is employed to find the optimal order for an AR model created using the impulse responses of the system. Numerical simulations are carried out using the impulse responses acquired from a four-story frame structure contaminated with additive noise including single and multiple damaged elements. The results of the simulations demonstrate that the method can be effectively employed to detect and locate damage. The performance of the proposed procedure are further demonstrated using the impact data acquired from a reinforced concrete frame for real applications.

Numerical analysis of 3d truss bridge using ANSYS

Trusses are typically lighter and more cost-effective than alternative structural support systems and are used in a variety of structures including vehicle frames, mechanical equipment, buildings, bridges, and towers. A truss structure takes advantage of the inherent geometric stability of the triangle to evenly distribute weight and to handle changing tension and compression. This project deals with the analysis of a 3D truss bridge which is proposed to be constructed connecting the two platforms of the Tripunithura railway station, with three different deck materials glass fibre reinforced concrete (GFRC), concrete and steel. Analysis and design of the proposed structure is carried out using ANSYS (Workbench) 2022 R2 software. ANSYS is an analysing software that is used for mechanical product designs and civil structure designs. It uses computer based numerical techniques to solve problems. This software helps to engineer and design complex, highly nonlinear and large models. It enables to design and manage 3D structures in steel, concrete and glass fibre reinforced concrete (GFRC). Structural performance of 3D truss bridges with three different deck material in terms of axial force, shear stress and deformation are compared in order to find which combination of materials will give better performances. Dynamic analysis is performed to examine the frequency and modes of vibrations caused by the movement of trains.