Long-term Structural Monitoring Research Articles

Long-term monitoring of population structure: Alpine short-lived perennials on the verge of stability

Long-term monitoring of population structure: Alpine short-lived perennials on the verge of stability

Long-term monitoring of water quality and phytoplankton community structure of Lake Manzala, Mediterranean Coast of Egypt

Coastal wetlands play a crucial role in supporting biodiversity, providing valuable ecosystem services, and contributing to the resilience of coastal ecosystems, making the preservation and restoration of these wetlands essential for sustainable development in coastal regions. This study focuses on Lake Manzala, a coastal wetland located on the Mediterranean Coast of Egypt, highlighting the significance of conserving and managing this unique environment. The objective of this research was to evaluate the water quality and phytoplankton structure of Lake Manzala and establish an updated long-term ecological database for the region, specifically evaluating the effectiveness of development plans that were carried out in 2017. Surface water samples were collected seasonally at eleven sites between 2010 to 2022. The findings revealed that prior to the development plans, the phytoplankton abundance in Lake Manzala exhibited high levels of eutrophication, characterized by increased abundance and species richness. The dominant phytoplankton classes in Lake Manzala were Bacillariophyceae, Chlorophyceae, and Cyanophyceae. Prior to development plans, they accounted for 46.5% and 45.0% respectively. Post-development, Bacillariophyceae increased to 62.8%, while Chlorophyceae decreased to 25.1%. Dinophyceae increased from 1.3% to 9.04%, while Cyanophyceae decreased from 6.1% to 1.6%. Based on the Trophic State Index for chlorophyll a, Lake Manzala underwent a shift from predominantly hypertrophic to eutrophic conditions. The study explored the relationship between biological factors and environmental conditions using principal component analysis, cluster analysis, and the modified water quality index (WQI). The results indicated positive signs of improvement in Lake Manzala during the post-development phase, as it transitioned from a poor to a moderate state. This research emphasizes the need for integrated land and water management approaches. By informing policy direction and development, this research underscores the importance of preserving and restoring ecosystems for the long-term well-being of both local communities and the global environment.

Leveraging Multi-Temporal InSAR Technique for Long-Term Structural Behaviour Monitoring of High-Speed Railway Bridges

The effective monitoring of railway facilities is crucial for safety and operational efficiency. This study proposes an enhanced remote monitoring technique for railway facilities, specifically bridges, using satellite radar InSAR (Interferometric Synthetic Aperture Radar) technology. Previous studies faced limitations such as insufficient data points and challenges with topographical and structural variations. Our approach addresses these issues by analysing displacements from 30 images captured by the X-band SAR satellite, TerraSAR-X, over two years. We tested each InSAR parameter to develop an optimal set of parameters, applying the technique to a post-tensioned PSC (pre-stressed concrete) box bridge. Our findings revealed a recurring arch-shaped elevation along the bridge, attributed to temporal changes and long-term deformation. Further analysis showed a strong correlation between this deformation pattern and average surrounding temperature. This indicates that our technique can effectively identify micro-displacements due to temperature changes and structural deformation. Thus, the technique provides a theoretical foundation for improved SAR monitoring of large-scale social overhead capital (SOC) facilities, ensuring efficient maintenance and management.

Metamaterials based passive wireless sensor for concrete structures

—Increasing attention has been payed to wireless passive sensors because of the ever-increasing demand for long-term and accurate monitoring of chloride ion penetration and structure of concrete infrastructures. Extraordinary properties of metamaterials results in applications in sensors. In this study, a wireless passive SRR metamaterials based sensor system was designed and manufactured which was used to detect Cl− concentration and thickness of concrete. CST was used for simulation and observation of the performance of metamaterials sensors in X-band (8–12 GHz). High Q and sensitivity sensors were designed by continuously adjusting the dimensions of the SRRs. X-band microstrip antenna was designed by HFSS and the sensing performance of the proposed sensor system is simulated and experimental measured. The results demonstrate the feasibility of the wireless passive sensor system. The system was used to test Cl− and thickness of concrete structure and the experimental results indicate the theoretical significance and practicality.

Predicting the Dynamic Behaviour of a Concrete Dam using Statistical and Machine Learning Models

Operational Modal Analysis is a reliable methodology for the assessment of civil engineering structures, allowing for the accurate definition of their dynamic behavior. Additionally, since it does not require the use of artificial excitation, it becomes a cost-effective choice for the performance of singular tests, as well as a consistent option for the long-term continuous monitoring of structures. Nevertheless, the modal characteristics of structures are affected by environmental and operational conditions, concealing the variations that could emerge due to abnormal behavior. With respect to concrete dams, factors such as temperature and the level of water in the reservoir exert a pronounced influence in the evolution of natural frequencies, increasing data variability and camouflaging the behavior that would be discerned under stable conditions. In this context, the current study seeks to examine the ability of statistical and machine learning tools to mitigate the effects of external conditions on the modal properties of concrete dams, specifically natural frequencies. To achieve this objective, the efficiency of methods incorporating measurements of variables impacting the structure, such as Multiple Linear Regressions and Neural Networks, is compared to that of tools not needing these inputs, as is the case of Principal Components Analysis and the Minimum Mean Square Error estimator. Experimental data obtained during the continuous dynamic monitoring of a concrete dam in Portugal is used as a case study.

Mitigation of unmeasured environmental and operational variability in long-term bridge health monitoring by unsupervised statistical learning

Environmental and operational variations present a significant challenge in the long-term health monitoring of bridge structures due to their potential to cause serious confounding influences and wrong decisions. These influences may lead to false alarms of damage during normal operation of a civil structure or, conversely, mask actual damage, resulting in a failure to correctly signal a warning. Consequently, a critical task in structural health monitoring, particularly for bridges, is to mitigate the negative impact of environmental variability. An initial approach is to measure the most influential environmental and operational factors and then apply statistical tools to eliminate their effects. However, this approach may not be effective under all conditions. Therefore, the primary objective of this paper is to propose an innovative unsupervised statistical learning method that addresses the challenges posed by environmental and operational variability by considering unmeasured factors. The proposed method involves three steps: data clustering, unsupervised feature selection, and novelty detection. The initial step utilizes k-means clustering to categorize features (modal frequencies) into five types of clusters, reflecting the influence of five environmental and operational factors on bridge structures; that is, temperature, humidity/rain/snow, wind speed/direction, traffic, and damage. These clustered features are then processed through an unsupervised feature selection algorithm based on reconstruction independent component analysis to extract new features. These reduced features are subsequently employed in a novelty detection model, developed using the Mahalanobis-squared distance, to analyze the environmental and operational effects. Real dynamic data of a steel arch bridge is utilized to verify the proposed method. Results demonstrate that this method can effectively handle the demanding issue stemming from unmeasured environmental and operational variability conditions.

The effects of precursory velocity changes on earthquake nucleation and stress evolution in dynamic earthquake cycle simulations

Seismic velocity changes in earthquake cycles have been observed over a wide range of timescales and may be a good indicator of the onset of future earthquakes. Understanding the effects of precursory velocity changes right before seismic and slow-slip events could potentially elucidate the onset and timing of fault failure. We use numerical models to simulate fully dynamic earthquake cycles in 2D strike-slip fault systems with antiplane geometry, surrounded by a narrow fault-parallel damage zone. By imposing S-wave velocity changes inside fault damage zones, we investigate the effects of these precursors on multiple stages of the seismic cycle, including nucleation, coseismic, postseismic, and interseismic stages. Our modeling results show a wide spectrum of fault slip behaviors including fast earthquakes, slow-slip events, and variable creep. One primary effect of the imposed velocity precursor is on the earthquake nucleation phase, and earlier onset of precursors causes earthquakes to nucleate sooner with a smaller nucleation size that is not predicted by theoretical equations. Furthermore, such precursors affect the nucleation of dynamic earthquakes and slow-slip events. Our results highlight the importance of short- and long-term monitoring of fault zone structures for better assessment of regional seismic hazard.

Experimental comparison of three automatic operational modal analysis algorithms on suspension and floating bridges

Automatic operational modal analysis is necessary for long-term monitoring of structures when using modal information. Many algorithms have been proposed to accomplish this task; two examples are the fully automatic algorithm by Reynders et al. in 2012 and the semi-automatic algorithm by Kvåle and Øiseth in 2020; however, few in-depth direct comparisons exist. This work compares the two algorithms mentioned above with a new fully automatic algorithm recently developed by the authors, inspired by the best functioning elements from algorithms published since 2008 and following the standard three-step framework. The comparison is performed using 3600 experimental datasets from three real bridges – the Hardanger and Hålogaland suspension bridges and the Bergsøysund floating pontoon bridge – where the detections made by the algorithms are compared to the pre-defined reference modes of the bridges. The new algorithm (Dederichs 2023) performs equally to the Kvåle 2020 algorithm in correct detections and better than the Reynders 2012 algorithm. The two best algorithms detect the easily excited structural modes in over 90 % of the datasets. The Reynders 2012 algorithm is shown to have a more cautious approach to mode detections, making few correct detections and fewer errors. A sensitivity analysis of the criteria to compare detected modes to reference modes shows that this does not impact the comparison’s conclusions.

The current state of the problem of numerical investigation of metal structure refusal based on dynamic monitoring

The article considers the critical role of long-term dynamic monitoring of building structures in ensuring their safety and stability. The importance of studying the structural behavior of truss structures after local failures is emphasized, which is key to the development of effective monitoring methods. The value of vertical deflections and modal frequencies as indicators of the general behavior of structures is pointed out, as well as the high costs associated with traditional monitoring methods requiring a large number of sensors. It is also emphasized that modern engineering practice does not have universal monitoring methods that would take into account all the features of construction structures, especially spatial structures with a complex design and a variety of elements. Authors calls for the development of new approaches and technologies to improve risk monitoring and management, which can prevent catastrophic consequences, as was the case with Viadotto Polchevera and other structural collapses. Attention is also drawn to current research that uses dynamic monitoring to inform the design of civil structures, including updating finite element models based on measured in-service performance. It is highlighted that long-term monitoring can provide valuable information about structural behavior, which allows for a better assessment of the condition of the structure and the prediction of potential defects. It is emphasized that a local failure can lead to the progressive destruction of the entire structure, which makes monitoring extremely important to prevent such incidents. The article concludes with conclusions about the need to establish management and maintenance procedures to maximize the life cycle of structures and obtain optimal return on investment.

Synergistic design of mechanical properties between implantable sensor and concrete based on three-dimensional refined numerical model

With the ongoing advancement in the construction and development of intelligent infrastructures, there has been a significant integration of sensors within structures. Portland Cement Concrete (PCC) pavement, being a predominant form of transportation infrastructures, necessitates comprehensive analysis due to its critical role. Given that flexural strength is a paramount design criterion for PCC pavements, concrete beams are selected as the primary focus for this study. Initially, a sophisticated three-dimensional numerical model is constructed. This model intricately incorporates the concepts of concrete damaged plasticity (CDP) and the cohesive zone model (CZM) to examine the interaction between the sensor and the concrete beam. The veracity of the numerical model is rigorously validated through methodical three-point bending tests, ensuring its reliability and accuracy. Subsequently, an innovative method for the synergistic design and assessment of the mechanical properties of the implantable sensor in conjunction with the concrete component is introduced. This methodology is underpinned by a novel synergistic performance index system. The system is stratified into different layers, with synergy degree as the target layer, and bearing performance, deformation coordination, and damage control as the criterion layer. In the final phase of the research, the study delves into the impacts of various factors such as size parameters, packaging materials, and positioning of the sensor on its synergistic performance with the concrete. The insights derived from this research are poised to offer valuable guidance for the integrated synergistic design of sensors. This is envisioned to facilitate optimal integration, ensuring long-term and precise monitoring of concrete structures, thereby contributing significantly to the field of intelligent infrastructure construction and maintenance.

Damaging effects of earth fissures on high-speed railway bridges: Insights from physical experiments and numerical simulations

Earth fissure is a typical geological phenomenon related to surface rupture. With the rapid development of high-speed railways (HSRs) in recent years, earth fissure disasters pose a serious challenge to the construction and subsequent safe operation of HSRs. Here, we aimed to investigate the damaging effect of earth fissure activities on HSR bridges spanning the earth fissure zone. Taking the Shuixiu earth fissure of Da-Xi HSR as the prototype, a large-scale laboratory physical experiment with geometric model scale 1:20 was carried out to reveal the deformation and failure characteristics of HSR bridges under earth fissure activity. In addition, the damage influence range and damage mode of HSR bridges crossing earth fissures are further determined by numerical simulation. The results show that the earth fissure activity mainly has damaging effects on the structure of HSR bridges near the earth fissure zone, which is mainly manifested as uneven settlement of reinforced concrete pile, bending deformation of box girder and cracking of track slab. The width of the influence zone of Shuixiu earth fissure on the HSR bridge is determined to be 32.4 m. Moreover, the pile-end pressure on the hanging wall side of the earth fissure gradually decreases with the earth fissure activity, while that on the footwall side increases significantly. Most importantly, it is emphasized that a practical long-term monitoring and early warning system of HSR bridge structures should be established to prevent the hazards caused by earth fissure activities. The above understanding will benefit to the design, construction and operation of Da-Xi HSR line, and also provide significant guidance and reference to the construction of HSR bridges in other earth fissure prone areas.

Automated operational modal analysis for civil engineering structures with compressed measurements

Introducing compressive sensing (CS) to modal analysis enables modal identification approaches to process compressed measurements, which enhances the efficiency in both data transmission and analysis for long-term monitoring of civil engineering structures. Nevertheless, the existing methods normally require users’ judgment in mode selection, and thus they cannot realize automated modal analysis. To bridge this gap, this study proposes an automated CS-based operational modal analysis (OMA) method, namely Automated Sparse Decomposition (ASD). A mode selection strategy is proposed to gradually eliminate noise and false modes and thus realize automated modal identification based on compressed measurements. A search strategy with both extensive and intensive search stages is proposed to realize higher frequency search resolution with low computational costs. Additionally, an automated mode shape identification process is designed to enhance accuracy. To demonstrate the effectiveness of the proposed method, experimental data on an offshore wind turbine model and real-world monitoring data from an in-service cable-stayed bridge are analyzed. The results show that the identification accuracy of the proposed ASD is comparable to state-of-art CS-based methods. Due to its superior performance in both efficiency and accuracy, the proposed method has the potential to become a promising online modal analysis tool.

Monitoring of Shallow-Grooved Tramway Crossings

The article presents selected results and the sequencing of research into the project "Long-Term Monitoring of Rail Structures in Tramway Crossings with a Focus on Shallow-Grooved Crossings Aimed at the Optimization of their Maintenance and Noise Reduction", which was running with support from the Technology Agency of the Czech Republic within the Zeta Project (project registration No. TJ04000257) in 2020-2022. The project was elaborated jointly with the Prazska strojirna a.s. Company in cooperation with the Prague Public Transit Company (hereinafter PPTC). The project's objective was to optimize the maintenance of tramway tracks with a focus on shallow-grooved crossings in rail structures taking into account the noise emission levels during the life cycle of these crossings.

Parametric identification of Timoshenko-beam model for shear-wall structures using monitoring data

The feasible modeling of the shear-flexure coupling behavior of a shear wall is desired and needed for long-term structural health monitoring of shear-wall structures. To address this issue, a Timoshenko-beam finite-element model is proposed for referencing a shear-wall structure. A parametric identification framework along with a particle swarm optimization is developed for updating the target model using the vibration data. Numerical examples are analyzed with particular focuses on the selection of objective functions, and the potential effects of ground motions, sensor allocation, model selection, and measurement noise. Results indicate the preference of adopting the acceleration-based errors as the objective function, the robustness of identified optimal parameters regarding ground motion and sensor allocation, and the importance of choosing an appropriate model for referencing the structure. The accuracy of the proposed model is further validated using the data obtained from a Walnut Creek building under eight earthquakes from years 2015 to 2021. The averages of normalized root-mean-square acceleration prediction errors of the proposed model, along the longer and the shorter sides of the building, are 0.480 and 0.767, respectively. Comparisons with the shear-beam model, the Euler-Bernoulli-beam model, and the parallel connection of these two models further show that the Timoshenko-beam model outperforms other candidate models in predicting the response of the examined building.

Integrated Structural Monitoring Method of Photogrammetry and Fiber Bragg Grating Sensors With Two Cases Study

This study proposes a novel monitoring method that combines photogrammetry and fiber Bragg grating (FBG) sensors, which provide overall shape measurement and high-precision strain sensing, respectively. A system was built and tested on a cantilever structure and a gantry crane model; it used two industrial cameras and six or 11 FBG sensors pasted along the beam. Formulas are derived and finite-element simulations of beam strain are presented, along with theoretical derivations of the relationship between image-point displacement (IPD) and 3-D spatial displacement. A series of monitoring experiments were conducted, combining FBG sensing and photogrammetry to monitor the beam under various loads. The strain and deflection curves of the beam based on FBG sensors and photogrammetry were obtained and compared. The bendings measured by photogrammetry were consistent with the strains measured by FBG sensors. To describe the relationship between the beam’s displacements at the marked points monitored by photometry and local strains, an H matrix is proposed. The proposed system allows real-time and long-term monitoring of critical structures, thereby increasing their safety.

BIM-based structural health monitoring and early warning for heritage timber structures

An early warning system in the structural health monitoring (SHM) of an engineering system ensures its survival with protective measures. However, most existing SHM systems have problems in dealing with a large amount of collected data and difficulties in identifying the damaged locations with little or no capability to visualize the problems. Building Information Modelling (BIM) technology has great advantages in information integration, visualization and secondary development, and its application to SHM can help solve these problems. A BIM-based management platform is developed in this paper with multi-dimensional BIM model with multi-source information integration for more relevant and convenient protection of heritage timber structures. A dual-indicator early warning mechanism for long-term static monitoring of structures is proposed. A strategy to backtrack the cause leading to the warning raised is also proposed. The effectiveness of the above system is illustrated with studies on a Chinese heritage timber structure.

Employing vision-based sensing for long-term monitoring

Despite the crucial role of structural health monitoring (SHM), traditional contact-based sensors are costly and lacking automation. Vision-based sensors have recently emerged as a potential substitute, due to their non-contact nature and low cost. However, investigations lack validating their long-term performance. This study assesses vision-based sensing for robust and efficient long-term displacement monitoring. Additionally, the suitability of various machine learning (ML) algorithms in automatically extracting information from the generated data are examined. A first experiment examines the capability to monitor ambiently excited structures over long periods. The parameters assessed are: a) noise and drift (e.g., related to environmental changes such as temperature, humidity, and lighting); and b) obtainable displacement resolution related to ambient excitation. Then, a second experiment examines the capability to monitor dynamically excited structures over long periods (via the employment of an artificial excitation, e.g., an eccentrically loaded mass motor). Here, the following parameters assessed are the: a) frequency bandwidth range; b) frequency resolution; c) displacement resolution related to dynamic excitation; and d) maximum number monitored points for given computational resources. Finally, for processing the generated data, the following ML algorithms are examined: a) Artificial Neural Networks (ANNs); b) support vector machines (SVMs); c) decision trees; and d) Convolutional Neural Networks (CNNs). Concerning long-term monitoring, the paper found that non-contact sensing had comparable accuracy with traditional contact-based sensors. About the ML models, the CNNs were found to be advantageous. Whilst this study was carried out on a small-scale structure, the potential of vision-based sensing for long-term monitoring of full-scale structures was highlighted.

Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images.

The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher–student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications.

Application of FBG Sensor to Safety Monitoring of Mine Shaft Lining Structure.

The use of fiber Bragg grating (FBG) sensors is proposed to solve the technical problem of poor sensor stability in the long-term safety monitoring of shaft lining structures. The auxiliary shaft of the Zhuxianzhuang coal mine was considered as the engineering background, and a test system implementing FBG sensors was established to monitor the long-term safety of the shaft lining structure. Indoor simulation testing revealed that the coefficient of determination (r2) between the test curves of the FBG sensor and the resistance strain gauge is greater than 0.99 in both the transverse and vertical strains. Therefore, the FBG sensor and resistance strain gauge test values are similar, and the error is small. The early warning value was obtained by calculation, according to the specific engineering geological conditions and shaft lining structure. The monitoring data obtained for the shaft lining at three test levels over more than three years reveal that the measured vertical strain value is less than the warning value, indicating that the shaft lining structure is currently in a safe state. The analysis of the monitoring data reveals that the vertical strain increment caused by the vertical additional force is approximately 0.0752 με/d. As the mine drainage progresses, the increasing vertical additional force acting on the shaft lining will compromise the safety of the shaft lining structure. Therefore, the monitoring must be enhanced to facilitate decision-making for safe shaft operation.

Spatial relationships of trees in middle taiga post-pyrogenic pine forest stands in the European North-East of Russia

Information on the structural organization of forest stands obtained on sample plots is the basis for long-term monitoring of post-fire pine forest structure and dynamics in the European North-East. These data can be used as a marker of native pine stands of the European taiga. Here, we studied vertical and horizontal structure in the post-pyrogenic pine forests of Vacciniosum, Vaccinioso-cladinosum and Myrtillosum site types in the boreal forest of the Komi Republic. The type of horizontal structure of uneven-aged forest stands changed with age from grouped to random one. Large trees were randomly distributed on the plot. We observed the weak aggregation of undergrowth trees (natural tree regeneration) in stands at distances of 2–6 m. Undergrowth individuals were characterized by group distribution at smaller distances than 1–2 m. Spatial relationships between large, small and codominant trees demonstrated random distribution in most cases. Undergrowth individuals did not show any competitive relations. However, we revealed a “taking off” effect between mature trees of pine and pine undergrowth. The direction of the displacement of tree crown centre projections relative to the bases of their trunks was ambiguous. The shift of the crown space towards the maximum solar radiation was detected in a thinned stand with old age and big size of trees. In other site types, no one-sided orientation of the tree crown development emerged.