Search Results (21)

The long-term preservation of heritage structures relies on effective Structural Health Monitoring (SHM) systems, where sensor placement is key to ensuring early damage detection and guiding conservation efforts. Optimal Sensor Placement (OSP) methods offer a systematic framework to identify efficient sensor configurations, yet their application in historical buildings remains limited. Typically, OSP is driven by numerical models; however, in the context of heritage structures, these models are often affected by substantial uncertainties due to irregular geometries, heterogeneous materials, and unknown boundary conditions. In this scenario, data-driven approaches become particularly attractive as they eliminate the need for potentially unreliable models by relying directly on experimentally identified dynamic properties. This study investigates how the choice of input data influences OSP outcomes, using the Church of Santa Ana in Seville, Spain, as a representative case. Three data sources are considered: an uncalibrated numerical model, a calibrated model, and a data-driven set of modal parameters. Several OSP methods are implemented and systematically compared. The results underscore the decisive impact of the input data on the optimisation process. Although calibrated models may improve certain modal parameters, they do not necessarily translate into better sensor configurations. This highlights the potential of data-driven strategies to enhance the robustness and applicability of SHM systems in the complex and uncertain context of heritage buildings. Full article

Operational modal analysis (OMA) is widely used for its simplicity and reliance on ambient noise. While commercial OMA software exists, they often limit user control. Some researchers develop their own tools, but independent software tools remain scarce. The number of such independent software is limited, and the development of new ones with enhanced features, better performance, and varied user interfaces would be beneficial to spread the informed use of dynamic identification techniques, leading to more reliable and valuable results for structural engineering applications. This work introduces the new DYMOS software for OMA from ambient vibration test recordings. DYMOS includes various state-of-art algorithms and tools for vibration-based modal identification and for optimal sensor placement (OSP), allowing for customization of analysis parameters and procedures with the aim of reducing the gap between the needs of professional practice and research. Additionally, a new graphical tool is introduced for visualizing results in both buildings and bridges. By using CAD drawings as input, it streamlines model construction, making the process faster, more intuitive, and efficient. The article aims to describe DYMOS and to demonstrate its potential for OMA and OSP in civil engineering through the application on two real case studies dynamically tested. Full article

The structural health monitoring (SHM) of bridge infrastructure has become essential for ensuring safety, serviceability, and long-term functionality amid aging structures and increasing load demands. SHM leverages sensor networks to enable real-time data acquisition, damage detection, and predictive maintenance, offering a more reliable alternative to traditional visual inspection methods. A key challenge in SHM is optimal sensor placement (OSP), which directly impacts monitoring accuracy, cost-efficiency, and overall system performance. This review explores recent advancements in SHM techniques, sensor technologies, and OSP methodologies, with a primary focus on bridge infrastructure. It evaluates sensor configuration strategies based on criteria such as the modal assurance criterion (MAC) and mean square error (MSE) while examining optimisation approaches like the Effective Independence (EI) method, Kinetic Energy Optimisation (KEO), and their advanced variants. Despite these advancements, several research gaps remain. Future studies should focus on scalable OSP strategies for large-scale bridge networks, integrating machine learning (ML) and artificial intelligence (AI) for adaptive sensor deployment. The implementation of digital twin (DT) technology in SHM can enhance predictive maintenance and real-time decision-making, improving long-term infrastructure resilience. Additionally, research on sensor robustness against environmental noise and external disturbances, as well as the integration of edge computing and wireless sensor networks (WSNs) for efficient data transmission, will be critical in advancing SHM applications. This review provides critical insights and recommendations to bridge the gap between theoretical innovations and real-world implementation, ensuring the effective monitoring and maintenance of bridge infrastructure in modern civil engineering. Full article

Optimal sensor placement (OSP) for arch dams is important to ensure their long-term service, but the evolution of structural states and material properties is less considered at present. This paper considers the effects of cracks, contraction joints, and elastic modulus zoning degradation of dam bodies, proposing an OSP framework based on an improved artificial hummingbird algorithm (IAHA). First, considering the compressibility of reservoir water, a finite element model of the arch dam–reservoir–foundation system is established. Second, by introducing improved circle chaotic mapping and Levy flight, IAHA is proposed. Then, a method for selecting the optimal number of sensors (ONS) based on modal assurance criterion (MAC), fitness values, and maximum singular value ratio (S) criteria is proposed. Finally, an OSP framework for arch dams with cracks is constructed and verified through a concrete arch dam. The final sensor placement is carried out for the current state of this arch dam after 45 years of operation, and the ONS is selected to give the results of the spatial location of the sensors. The results indicate that the OSP performance of the arch dam based on IAHA is the best, with MAC-MAX, MAC-AVE, MAC-RMS, and S values of 0.1521, 0.1069, 0.5478, and 1.8591, respectively, showing the best performance among the selected algorithms. The method of selecting the ONS based on MAC, fitness values, and S criteria is reasonable and feasible, considering that the changes in structural states and material properties have varying degrees of influence on the number and spatial location of sensors. The research results of this paper can provide effective technical support for the health diagnosis of arch dams with cracks and provide references and new ideas for structural health monitoring. Full article

There are hundreds of various sensors used for online Prognosis and Health Management (PHM) of LREs. Inspired by the fact that a limited number of key sensors are selected for inflight control purposes in LRE, it is practical to optimal placement of redundant sensors for improving the diagnosability and economics of PHM systems. To strike a balance between sensor cost, real-time performance and diagnosability of the fault diagnosis algorithm in LRE, this paper proposes a novel Optimal Sensor Placement (OSP) method. Firstly, a Kernel Extreme Learning Machine-based (KELM) two-stage diagnosis algorithm is developed based on a system-level failure simulation model of LRE. Secondly, hierarchical diagnosability metrics are constructed to formulate the OSP problem in this paper. Thirdly, a Hierarchy Ranking Evolutionary Algorithm-based (HREA) two-stage OSP method is developed, achieving further optimization of Pareto solutions by the improved hypervolume indicator. Finally, the proposed method is validated using failure simulation datasets and hot-fire test-run experiment datasets. Additionally, four classical binary multi-objective optimization algorithms are introduced for comparison. The testing results demonstrate that the HREA-based OSP method outperforms other classical methods in effectively balancing the sensor cost, real-time performance and diagnosability of the diagnosis algorithm. The proposed method in this paper implements system-level OSP for LRE fault diagnosis and exhibits the potential for application in the development of reusable LREs. Full article

In this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. The approach utilizes six established Optimal Sensor Placement (OSP) methods to generate a Pareto front, which is systematically analyzed and archived through Grey Relational Analysis (GRA) and Fuzzy Decision Making (FDM). This comprehensive analysis demonstrates the proposed approach’s superior performance in determining sensor placements, showcasing its adaptability to structural changes, enhancement of durability, and effective management of the life cycle of structures. Overall, this paper makes a significant contribution to engineering by leveraging advancements in sensor and information technologies to ensure essential infrastructure safety through SHM systems. Full article

Increasingly often, due to the high sensitivity level of diagnostic systems, they are also sensitive to the occurrence of a significant number of false alarms. In particular, in structural health monitoring (SHM), the problem of optimal sensor placement (OSP) is appearing due to the need to reach a balance between performance and cost of the diagnostic system. The applied approach of considering nondominated solutions allows for adaption of the system parameters to the user’s expectations, treating this optimization problem as multi-objective. For this purpose, the NSGA-II algorithm was selected for the determination of an optimal set of parameters in the OSP problem for the detection of delamination in composite structures. The objectives comprise minimization of type-I and type-II errors, and number of sensors to be placed. The advantage of the proposed approach is that it is based on experimental data from the healthy structure, whereas all cases with a presence of delamination were acquired from numerical experiments. This makes it possible to develop a customized SHM system for the arbitrary location of damage. Full article

Structural health monitoring (SHM) is critical to maintaining safe and reliable civil infrastructure, but the optimal design of an SHM sensing system, i.e., optimal sensor placement (OSP), remains a complex challenge. Based on the existing literature, this paper presents a comprehensive review of OSP strategies for SHM. It covers the key steps in OSP, from evaluation criteria to efficient optimization algorithms. The evaluation criteria are classified into six groups, while the optimization algorithms are roughly categorized into three classes. The advantages and disadvantages of each group of methods have been summarized, aiming to benefit the OSP strategy selection in future projects. Then, the real-world implementation of OSP on bridges, high-rise buildings, and other engineering structures, is presented. Based on the current progress, the challenges of OSP are recognized; its future development directions are recommended. This study equips researchers/practitioners with an integrated perspective on state-of-the-art OSP. By highlighting key developments, persistent challenges, and prospects, it is expected to bridge the gap between theory and practice. Full article

As ageing structures and infrastructures become a global concern, structural health monitoring (SHM) is seen as a crucial tool for their cost-effective maintenance. Promising results obtained for modern and conventional constructions suggested the application of SHM to historical masonry buildings as well. However, this presents peculiar shortcomings and open challenges. One of the most relevant aspects that deserve more research is the optimisation of the sensor placement to tackle well-known issues in ambient vibration testing for such buildings. The present paper focuses on the application of optimal sensor placement (OSP) strategies for dynamic identification in historical masonry buildings. While OSP techniques have been extensively studied in various structural contexts, their application in historical masonry buildings remains relatively limited. This paper discusses the challenges and opportunities of OSP in this specific context, analysing and discussing real-world examples, as well as a numerical benchmark application to illustrate its complexities. This article aims to shed light on the progress and issues associated with OSP in masonry historical buildings, providing a detailed problem formulation, identifying ongoing challenges and presenting promising solutions for future improvements. Full article

In recent decades, structural health monitoring (SHM) has gained increased importance for ensuring the sustainability and serviceability of large and complex structures. To design an SHM system that delivers optimal monitoring outcomes, engineers must make decisions on numerous system specifications, including the sensor types, numbers, and placements, as well as data transfer, storage, and data analysis techniques. Optimization algorithms are employed to optimize the system settings, such as the sensor configuration, that significantly impact the quality and information density of the captured data and, hence, the system performance. Optimal sensor placement (OSP) is defined as the placement of sensors that results in the least amount of monitoring cost while meeting predefined performance requirements. An optimization algorithm generally finds the “best available” values of an objective function, given a specific input (or domain). Various optimization algorithms, from random search to heuristic algorithms, have been developed by researchers for different SHM purposes, including OSP. This paper comprehensively reviews the most recent optimization algorithms for SHM and OSP. The article focuses on the following: (I) the definition of SHM and all its components, including sensor systems and damage detection methods, (II) the problem formulation of OSP and all current methods, (III) the introduction of optimization algorithms and their types, and (IV) how various existing optimization methodologies can be applied to SHM systems and OSP methods. Our comprehensive comparative review revealed that applying optimization algorithms in SHM systems, including their use for OSP, to derive an optimal solution, has become increasingly common and has resulted in the development of sophisticated methods tailored to SHM. This article also demonstrates that these sophisticated methods, using artificial intelligence (AI), are highly accurate and fast at solving complex problems. Full article

The implementation of condition monitoring for damage identification and the generation of a reliable digital twin are essential elements of preventive conservation. The application of this promising approach to Cultural Heritage (CH) sites is deemed truly beneficial, constituting a minimally invasive mitigation strategy and a cost-effective decision-making tool. In this light, the present work focuses on establishing an informative virtual model as a platform for the conservation of the monastery of Santa Maria de Salzedas, a CH building located in the north of Portugal. The platform is the first step towards the generation of the digital twin and is populated with existing documentation as well as new information collected within the scope of an inspection and diagnosis programme. At this stage, the virtual model encompasses the main cloister, whose structural condition and safety raised concerns in the past and required the implementation of urgent remedial measures. In the definition of a vibration-based condition monitoring strategy for the south wing of the cloister, five modes were identified by carrying out an extensive dynamic identification. Nonetheless, significant challenges emerged due to the low amplitude of the ambient-induced vibrations and the intrusiveness of the activities. To this end, a data-driven Optimal Sensor Placement (OSP) approach was followed, testing and comparing five heuristic methods to define a good trade-off between the number of sensors and the quality of the collected information. The results showed that these algorithms for OSP allow the selection of sensor locations with good signal strength. Full article

Sensor measurements diagnose and evaluate the structural health state. A sensor configuration with a limited number of sensors must be designed to monitor sufficient information about the structural health state. The diagnosis of a truss structure composed of axial members can begin with a measurement by the strain gauges attached to the truss members or by the accelerometers and displacement sensors at the nodes. This study considered the layout design of the displacement sensors at the nodes for the truss structure by using the effective independence (EI) method based on the mode shapes. The validity of the optimal sensor placement (OSP) methods depending on their synthesis with the Guyan method was investigated by the mode shape’s data expansion. The Guyan reduction technique rarely affected the final sensor design. A modified EI algorithm based on the strain mode shape of the truss members was presented. A numerical example was analyzed, showing that the sensor placements were affected depending on the displacement sensors and strain gauges. Numerical examples illustrated that the strain-based EI method without the Guyan reduction method has the advantage of reducing the number of sensors and providing more data related with the displacements at the nodes. The measurement sensor should be selected when considering structural behavior, as it is crucial. Full article

One of the objectives of structural health monitoring (SHM) is to maximize the information while keeping the number of sensors, and consequently the cost of the sensor system, to a minimum. Besides, the sensor configurations must be robust in the sense that the feasibility of small errors inherent to the process must not lead to large variations in the final results. This paper presents novelties regarding the robustness evaluation to model and measurement errors of four of the most influential optimal sensor placement (OSP) methods: the modal kinetic energy (MKE) method; the effective independence (EFI) method; the information entropy index (IEI) method; and the MinMAC method. The four OSP methods were implemented on the Streicker Bridge, a footbridge located on the Princeton University Campus, to identify five mode shapes of the bridge. The mode shapes, obtained in a FE model’s modal analysis, were used as input data for the OSP analyses. The study indicates that the MKE method seems to be the most suitable method to estimate the optimal sensor positions: it provides a relatively large amount of information with the lowest computational time, and it outperforms the other three methods in terms of robustness in the usual range of number of sensors. Full article

This study presents iterative optimal sensor placement (OSP) techniques using the modal assurance criterion (MAC) and the effective independence (EI) algorithm. The algorithms use the proper orthogonal mode (POM) extracted from the frequency response functions (FRFs) of dynamic systems within a wide range of frequencies. The FRF-based OSP method proposed in this study has the merit of reflecting dynamic characteristics, unlike the mode shape-based method. Evaluating the MAC values and the EI indices at each iteration, the DOFs of low contribution to the objective function of candidate sensor DOFs are deleted from master DOFs and moved to slave DOFs. This process is repeated until the sensor number corresponds with the master DOFs. The validity of the proposed methods is illustrated in an example, the sensor layouts by the proposed methods are compared, and the layout inconsistency between the MAC and the EI techniques is analyzed. Full article

In-service prestressed concrete box girder bridges have received increasing attention in recent years due to a large number of bridges reaching decades in service. Therefore, the ageing of infrastructure demands the development of robust condition assessment methodologies based on affordable technology such as vehicle-induced vibration tests (VITs) in contrast with more expensive existing technologies such as tests using hammers or shakers. Ambient vibration tests (AVTs) have been widely used worldwide, taking advantage of freely available ambient excitation sources. However, the literature has commonly reported insufficient input energy to excite the structure to obtain satisfactory modal identification results, especially in long-span concrete bridges. On the other hand, the use of forced vibration tests (FVTs) requires more economic resources. This paper presents the results of field measurements at optimally selected locations in VITs consisting of a 32-ton truck and a springboard with a height of 50 mm. AVTs using optimal sensor placement (OSP) provide similar results to VITs without considering OSP locations. Additionally, the VIT/AVT cost ratio is reduced to 2 since a shorter data collection time is achieved within a one-day (8 h) test framework, which minimizes temperature effects, thus leading to improvements in AVT identification results, especially in vertical modes. Full article