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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.

While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom–up (i.e. activity level) or top–down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom–up and top–down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom–up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom–up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called “anchor” nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and efficiency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering efficiency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters. These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network.

Data of operational conditions of structural components, acquired, e.g., in structural health monitoring (SHM), is of great interest to optimise products from one generation to the next, for example, by adapting them to occurring operational loads. To acquire data for this purpose in the desired quality, an optimal sensor placement for so-called shape and load sensing is required. In the case of large-scale structural components, wireless sensor networks (WSN) could be used to process and transmit the acquired data for real-time monitoring, which furthermore requires an optimisation of sensor node positions. Since most publications focus only on the optimal sensor placement or the optimisation of sensor node positions, a methodology for both is implemented in a Python tool, and an optimised WSN is realised on a demonstration part, loaded at a test bench. For this purpose, the modal method is applied for shape sensing as well as a physics-informed neural network for solving inverse problems in shape sensing (iPINN). The WSN is realised with strain gauges, HX711 analogue-digital (A/D) converters, and Arduino Nano 33 IoT microprocessors for data submission to a server, which allows real-time visualisation and data processing on a Python Flask server. The results demonstrate the applicability of the presented methodology and its implementation in the Python tool for achieving high-accuracy shape sensing with WSNs.

Monitoring of air pollutants such as fine particulate matter (PM2.5) supports improved understanding of trends, assessment of exposure, and development of strategies to protect public health. However, determining the optimal placement of pollutant sensors is an ongoing challenge, as different sensor configurations may yield varied insights into regional air quality. In this study, we propose a quantitative framework that integrates a stochastic advection-diffusion (SAD) model with the value of information (VoI) to design a sensor placement strategy for regional PM2.5 monitoring. Predictions from the SAD model are used to compute the VoI based on a cost function that penalizes misclassifications between model-assigned and observed air quality index values. We apply the framework to the Coastal Bend Region of Texas, where increased development is creating new air quality concerns. We compare three weighting strategies: (1) combined weighting, which accounts for both population density and social vulnerability; (2) vulnerability-focused weighting, which emphasizes socially vulnerable communities regardless of population density; and (3) unweighted VoI, which solely focuses on minimizing assignment error without demographic weighting. With a 10-sensor installation, the combined strategy covered areas with 22%–88% higher population density than the other schemes, whereas the vulnerability-focused strategy reduced uncovered social vulnerability by 6%–19%. Prediction accuracy differed by less than 1.5% across strategies. While differences in outcomes are expected given the distinct objectives, the framework adds value by quantifying these trade-offs and enabling a systematic sensor placement strategy that best aligns with policy goals. The proposed framework is scalable to different sensor types and capable of balancing information gain, population exposure, and social vulnerability, providing a versatile tool to guide sensor network design under resource constraints.

Use of a sensor network to provide adequate and reliable information is paramount for accurate damage detection of structures. However, unavoidably, deployed sensors are occasionally subject to failure faults, which, in turn, cause missing information. Placement of multiple backup sensors in a local region could overcome this difficulty and increase the sensor redundancy; however, this approach leads to a sensor clustering problem and higher costs in sensor deployment. Further, model uncertainty is another important issue that should be considered in a sensor network design. Accordingly, this work is dedicated to presenting a framework for optimization of sensor distribution that considers both sensor faults under uncertainty and sensor clustering for vibration-based damage detection. Based on the effective independence method, the first design objective is newly formulated to consider sensor faults under uncertainty. Moreover, a novel index that is universally applicable for any type of structure is proposed to evaluate sensor clustering, which is treated as the second objective. The non-dominated sorting genetic algorithm II is adopted to solve this multi-objective optimization problem, and Monte Carlo simulation (MCS) is employed for uncertainty analysis in the first objective. To reduce computation costs, real performance evaluations in MCS are replaced with Gaussian process regression models. Based on the vibration information achieved from optimized sensors, an optimization-based damage detection process is applied to validate the optimal sensor layout. Three case studies (i.e., a cantilever beam, a laminated composite structure, and a spatial frame) are presented to demonstrate the effectiveness and applicability of the developed framework.

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.

Optimal sensor placement (OSP) is one of the essential factors affecting the accuracy of health management, particularly in health monitoring driven by mode information. A novel OSP method based on active learning is proposed to effectively capture modal shapes for Structural Health Monitoring (SHM). First, the optimal Latin Hypercube Sampling is carried out to generate initial sensor positions, and the corresponding amplitudes of modal shapes at these positions are obtained by a frequency response function. Subsequently, data-driven models are built to be treated as virtual sensors to reconstruct the integrated modal shapes of the structure, and the accuracies of the results are calculated. Then, considering the distribution of the input sensor position, an improved reliability-based expectation improvement function (IREIF2) is applied to find the optimal sensor positions by optimizing the parameters of the probability density function in IREIF2. Finally, the position and response of the optimal sensor are used to update the data-driven models for more accurate modal shape reconstruction, and the accuracies are calculated to determine whether the OSP process continues. Once the accuracies meet the desired criteria, the optimal sensor positions are also obtained. The superiority of the proposed method is verified by the comparisons with other OSP methods, and different case studies are also used to prove the proposed method can realize OSP for SHM.

A bilayer optimization strategy is proposed in this research in order to improve the efficiency in the process of optimal sensor placement aiming at decreasing the uncertainty in identification of parameters. Firstly, the surrogate model between structural parameters and responses is established to improve the solution efficiency of uncertain parameters. Secondly, a particle swarm optimization algorithm based on spatial coordinates is proposed for effective optimal sensor placement. Finally, this research proposes an efficient solution strategy for optimal sensor placement with uncertainty, i.e., the proposed coordinate-based particle swarm optimization method is utilized for outer layer optimization, and surrogate model is used to solve the interval boundaries of structural parameters as an inner layer optimization method. The optimization results aiming at redundancy index of rectangular plate based on the proposed optimization algorithm and existing algorithms are compared. The mean value of optimization results of proposed method is 29.7% higher than the mean value of optimization results of GA. The proposed optimization strategy is verified by numerical example and an experimental work. The results of single objective optimization and multi-objective optimization are given, respectively. The computational efficiencies of the traditional method and the proposed optimization method are compared. The optimization efficiency of the proposed optimization method is four orders of magnitude higher than that of the traditional method. The proposed strategy provides a feasible idea for improving the efficiency of large-scale sensor layout optimization under uncertainty.

This study presents a hybrid sensor placement methodology that combines criterion-based candidate selection with advanced optimization algorithms. Four established selection criteria—modal kinetic energy (MKE), modal strain energy (MSE), modal assurance criterion (MAC) sensitivity, and mutual information (MI)—are used to evaluate DOF sensitivity and generate candidate pools. These are followed by one of four optimization algorithms—greedy, genetic algorithm (GA), particle swarm optimization (PSO), or simulated annealing (SA)—to identify the optimal subset of sensor locations. A key feature of the proposed approach is the incorporation of constraint dynamics using the Udwadia–Kalaba (U–K) generalized inverse formulation, which enables the accurate expansion of structural responses from sparse sensor data. The framework assumes a noise-free environment during the initial sensor design phase, but robustness is verified through extensive Monte Carlo simulations under multiple noise levels in a numerical experiment. This combined methodology offers an effective and flexible solution for data-driven sensor deployment in structural health monitoring. To clarify the rationale for using the Udwadia–Kalaba (U–K) generalized inverse, we note that unlike conventional pseudo-inverses, the U–K method incorporates physical constraints derived from partial mode shapes. This allows a more accurate and physically consistent reconstruction of unmeasured responses, particularly under sparse sensing. To clarify the benefit of using the U–K generalized inverse over conventional pseudo-inverses, we emphasize that the U–K method allows the incorporation of physical constraints derived from partial mode shapes directly into the reconstruction process. This leads to a constrained dynamic solution that not only reflects the known structural behavior but also improves numerical conditioning, particularly in underdetermined or ill-posed cases. Unlike conventional Moore–Penrose pseudo-inverses, which yield purely algebraic solutions without physical insight, the U–K formulation ensures that reconstructed responses adhere to dynamic compatibility, thereby reducing artifacts caused by sparse measurements or noise. Compared to unconstrained least-squares solutions, the U–K approach improves stability and interpretability in practical SHM scenarios.