Condition Assessment Data Research Articles

Data augmentation using conditional generative adversarial network (cGAN): applications for sewer condition classification and testing using different machine learning techniques

ABSTRACT The increasing availability of condition assessment data highlights the challenge of managing data imbalance in the asset management of aging infrastructure. Aging sewer pipes pose significant threats to health and the environment, underscoring the importance of proactive management practices to enhance asset maintenance and mitigate associated risks. While machine learning (ML) models are widely employed to model the complex deterioration process of sewer pipes, they face performance limitations when trained on imbalanced condition grade data. This paper addresses this issue by proposing a novel approach using conditional generative adversarial network (cGAN) for data augmentation. By generating synthetic data for minority classes, the skewed distribution of the sewer dataset is balanced, facilitating more robust and accurate predictive models. The utility of the proposed method is evaluated by training different ML classifiers, including neural network (NN), decision tree, quadratic discriminant analysis, Naïve Bayes, support vector machine (SVM), and K-nearest neighbor. Quadratic discriminant, Naïve Bayes, NN, and SVM classifiers demonstrated improvement. The cGAN-based data augmentation method also outperformed two other data imbalance handling techniques, random under-sampling, and cost-sensitive NN. Consequently, data generated by cGAN can effectively aid asset management by developing proactive classifiers that accurately predict pipes at a high risk of failure.

On population-based structural health monitoring for bridges: Comparing similarity metrics and dynamic responses between sets of bridges

Bridges are valuable infrastructure assets that are challenging and expensive to maintain. State-of-the-art data-based bridge SHM solutions look to use bridge response data for condition assessment and damage detection. Data-based SHM methods can be limited in their application as they require large datasets to train models effectively, and most bridges lack the available data for the approaches to work. Further, it would be expensive and unrealistic to collect the required datasets to employ data-based methods to entire bridge networks. Recently, a population-based structural health monitoring (PBSHM) approach was proposed that seeks to leverage the data available for SHM problems by pooling together similar structures with their datasets. The PBSHM approach could be valuable in bridge SHM, enhancing the datasets available for ‘populations of bridges. The PBSHM approach for assessing the similarity of bridges has been considered before and was shown to be useful for identifying similar and different bridge types. However, no data were considered in the previous work, and similarity metrics were only qualified using engineering judgement. For the PBSHM approach to be useful in bridge SHM, there is still a need to check that ‘similar’ bridges have similar responses for transfer learning to be feasible. This paper expands upon previous work and provides originality by investigating if bridges identified as similar also exhibit similar responses. The PBSHM derived similarity metrics convey the topological similarity between structures, and mode shapes are identified as being a topologically sensitive bridge response. Therefore, a modal test campaign is carried out for a set of six real bridges, and Operational Modal Analysis is used to identify modal responses from each of the bridge decks. The Modal Assurance Criterion is used to evaluate the similarity between the mode shapes from pairs of bridges and is subsequently compared to the similarity metrics evaluated between those bridges. The similarity metrics were found to be reflective of the similarities identified between the respective bridges’ mode shapes for bridges of the same and different types. The significance of this finding is that it is an important step towards validating the PBSHM comparison approach for identify similar structures where transfer learning might be attempted.

Causal relationship between seasonal variation in surface roughness and dust and sandstorm occurrence in arid and semi-arid Mongolia

In this study, the causal relationship between seasonal variations in surface conditions and DSS generation was investigated through the integrated use of the C-band backscatter coefficient (J 0) of the Sentinel 1 microwave satellite from the optical satellite sensor (Terra/MODIS). Because of the very high reflectance of the background soil in sparsely vegetated bare land, it has been difficult to rely solely on optical satellite data for accurate surface condition assessment in previous studies. Therefore, we used SAR and optical data to represent the spatial variability of backscatter coefficients for different surface conditions in the Gobi Desert and Steppe areas, and employed the C-band backscatter coefficient from the Sentinel-1 microwave satellite as a key parameter. The results show that in the Gobi Desert region, the correlation between the seasonal variation of backscatter coefficients and NDVI is highly significant, with a maximum correlation coefficient in fall (R=0.645, p<0.001) an inverse correlation in summer (R=-0.216, p<0.001), indicating little seasonal variation between NDVI and backscatter coefficients. In conclusion, surface roughness (σ0) is relatively low and stable from winter to the following spring and increases with plant growth in summer. In the Gobi Desert region, surface roughness changes little throughout the year, but in the steppe region, surface roughness increases with vegetation growth during the rainy season, which suppresses the generation of DSS.

Data Anomaly Detection through Semisupervised Learning Aided by Customised Data Augmentation Techniques

Structural health monitoring (SHM) systems may suffer from multiple patterns of data anomalies. Anomaly detection is an essential preprocessing step prior to the use of monitoring data for structural condition assessment or other decision making. Deep learning techniques have been extensively used for automatic category classification by training the network with labelled data. However, because the SHM data are usually large in quantity, manually labelling these abnormal data is time consuming and labour intensive. This study develops a semisupervised learning-based data anomaly detection method using a small set of labelled data and massive unlabelled data. The MixMatch technique, which could mix labelled and unlabelled data using MixUp, is adopted to enhance the generalisation and robustness of the model. A unified loss function is defined to combine information from labelled and unlabelled data by incorporating consistency regularisation, entropy minimisation, and regular model regularisation items. In addition, customised data augmentation strategies for time series are investigated to further improve the model performance. The proposed method is applied to the SHM data from a real bridge for anomaly detection. Results demonstrate the superior performance of the developed method with very limited labelled data, greatly reducing the time and cost of labelling efforts compared with the traditional supervised learning methods.

Traffic Sign Retroreflectivity Condition Assessment and Deterioration Analysis Using Lidar Technology

Traffic sign retroreflectivity is critical for nighttime visibility, an important factor in driver safety. Current methods of sign retroreflectivity assessment are expensive, time-consuming, dangerous, or subjective. There is an urgent need to explore an alternative method that is cost-effective, safe, objective, and can be operated during daytime or nighttime. One such method is mobile lidar. However, a methodology utilizing lidar cloud data for practical retroreflectivity condition assessment is still lacking because of the inability to numerically correlate lidar retro-intensity readings to the retroreflectivity standard set by the Manual on Uniform Traffic Control Devices (MUTCD). In addition, there is also a need to explore sign deterioration behavior using real-world lidar data. In this study, we (1) propose a practical, categorical traffic sign condition assessment using lidar data; (2) establish a preliminary correlation between the retro-intensity and retroreflectivity readings to determine the minimum retro-intensity thresholds for condition assessment of different sheeting types and colors; (3) validate the accuracy of the assessment by comparing it with standard nighttime visual inspection outcomes; (4) demonstrate the practical implementation through a feasibility study at Georgia Interstate 285; and (5) reveal the retro-intensity deterioration trends using historical lidar cloud data. The results show that the proposed methodology can reliably yield results comparable to manual measurements, potentially reducing sign retroreflectivity condition assessment effort, increasing the transportation agency’s productivity, and filling gaps where manual assessment is not possible. Additionally, the retro-intensity deterioration trends can help transportation agencies to understand the long-term behavior of sign retro-intensity and predict the optimal timing for sign replacement.

Detecting and testing multiple change points in distributions of damage-sensitive feature data for data-driven structural condition assessment: A distributional time series change-point analytic approach

Structural change detection is a core component of structural health monitoring (SHM). One appealing aspect of SHM is that structural changes can be detected by detecting the changes in extracted damage-sensitive features (DSFs). The feature changes of the data are usually attributed to the changes of the underlying distributions; thus, developing reliable change-point detectors for automatically detecting and testing various potential changes in the distributions of DSF data are particularly beneficial for automatic structural health diagnosis. However, methodological researches devoted to detecting change points for probability distributions are rare either in statistics or engineering literature, and state-of-the-art approaches mainly focus on single change-point detection and have restrictive assumptions on the distributions or the types of change points. This motivates us to develop a more flexible multiple change-point detection method for the distributions of DSF data employed for data-driven structural condition assessment. To accommodate a large dataset, the DSF data are summarized by a distributional time series that consists of probability density functions (PDFs) estimated from blocks of DSF data. Then, detecting the distributional changes for the massive DSF data can be phrased as detecting the changes in the resulting distributional time series with much less data objects. To overcome the obstacle that the PDF-valued data are special functional data residing in nonlinear abstract spaces, this study employs two transformations to convert the PDFs into more tractable vector spaces. Then, by using the linear structures possessed by the vector spaces, a nonparametric multiple change-point detection method is presented for the distributional time series based on the techniques of functional principal component analysis and E-Divisive data segmentation. The proposed method possesses various nice features, such as being capable for multiple change-point detection, having fewer restrictive assumptions and scalability to large datasets. Moreover, it also shows superiority in revealing the modes of distributional variations. An application to distributional change detection involved in cable condition assessment for a long-span cable-stayed bridge demonstrates the applicability and versatility of our proposal, and some interesting results are also discovered.

Fusing Expert Knowledge with Monitoring Data for Condition Assessment of Railway Welds.

Monitoring information can facilitate the condition assessment of railway infrastructure, via delivery of data that is informative on condition. A primary instance of such data is found in Axle Box Accelerations (ABAs), which track the dynamic vehicle/track interaction. Such sensors have been installed on specialized monitoring trains, as well as on in-service On-Board Monitoring (OBM) vehicles across Europe, enabling a continuous assessment of railway track condition. However, ABA measurements come with uncertainties that stem from noise corrupt data and the non-linear rail-wheel contact dynamics, as well as variations in environmental and operational conditions. These uncertainties pose a challenge for the condition assessment of rail welds through existing assessment tools. In this work, we use expert feedback as a complementary information source, which allows the narrowing down of these uncertainties, and, ultimately, refines assessment. Over the past year, with the support of the Swiss Federal Railways (SBB), we have assembled a database of expert evaluations on the condition of rail weld samples that have been diagnosed as critical via ABA monitoring. In this work, we fuse features derived from the ABA data with expert feedback, in order to refine defection of faulty (defect) welds. Three models are employed to this end; Binary Classification and Random Forest (RF) models, as well as a Bayesian Logistic Regression (BLR) scheme. The RF and BLR models proved superior to the Binary Classification model, while the BLR model further delivered a probability of prediction, quantifying the confidence we might attribute to the assigned labels. We explain that the classification task necessarily suffers high uncertainty, which is a result of faulty ground truth labels, and explain the value of continuously tracking the weld condition.

CONDITION PREDICTION OF SANITARY SEWERAGE PIPELINE SYSTEMS USING MULTINOMIAL LOGISTIC REGRESSION

It is important for the sanitary sewerage pipeline system to be in a good condition for providing safe conveyance of the wastewater from households, businesses, and industries to the wastewater treatment plants. Water utilities inspect sewer pipelines to decide which segments of the sanitary sewer pipes need renewal or replacement. This process of inspecting the sewer pipes is described as condition assessment. The objective of this paper is to develop Multinomial Logistic Regression (MLR) to predict the condition rating of sanitary sewerage pipelines using inspection and condition assessment data. MLR model was developed from the City of Dallas's data. The MLR model was built using 80% of randomly selected data and validated using the remaining 20% of data. The significant physical factors influencing sanitary pipes condition rating included diameter, age, pipe material, and length. Soil type was the environmental factor that influenced sanitary sewer pipes condition rating. The accuracy of the performance of the MLR was found to be 75%. This developed model will help the policymakers and sanitary sewer utility managers to predict sanitary sewer pipes condition rating that enables to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.

Fusion of NDE Scanning and Photogrammetric Data for Concrete Bridge Decks and Girders Condition Assessment

The first case history would present 3-D results of Impact Echo Scanning and Ground Penetrating Radar (GPR) Scanning plus Spectral Analyses of Surface Waves tests of post-tensioned concrete bridge girders. The NDE results were overlaid on photogrammetric images of visible corrosion/cracking damage to the concrete bridge girders of an aging bridge due to de-icing salts and a marine environment. Cart-based scanning with GPR and Impact Echo and Spectral Analyses of Surface Waves NDE methods is commonly being conducted of both bare concrete decks and asphalt overlaid decks to map both top and bottom delaminations and depth of concrete cover. The data is being used in conjunction with chlorides and carbonation testing of concrete cores to recommend repairs and evaluate remaining service lives for bridges. Photogrammetric images of bare concrete in particular are being used to visibly compare areas of spalling, delamination and deck patching for improved interpretation, visualization and mapping of internal deck delaminations and other concrete damage with this data fusion approach to provide more accurate surface and full-depth images of bridge deck conditions.

Developing Bridge Deterioration Models Using an Artificial Neural Network

The condition of a bridge is critical in quality evaluations and justifying the significant costs incurred by maintaining and repairing bridge infrastructures. Using bridge management systems, the department of transportation in the United States is currently supervising the construction and renovations of thousands of bridges. The inability to obtain funding for the current infrastructures, such that they comply with the requirements identified as part of maintenance, repair, and rehabilitation (MR&R), makes such bridge management systems critical. Bridge management systems facilitate decision making about handling bridge deterioration using an efficient model that accurately predicts bridge condition ratings. The accuracy of this model can facilitate MR&R planning and is used to confirm funds allocated to repair and maintain the bridge network management system. In this study, an artificial neural network (ANN) model is developed to improve the bridge management system (BMS) by improving the prediction accuracy of the deterioration of bridge decks, superstructures, and substructures. A large dataset of historical bridge condition assessment data was used to train and test the proposed ANN models for the deck, superstructure, and substructure components, and the accuracy of these models was 90%, 90%, and 89% on the testing set, respectively.

Development and Comparison of Prediction Models for Sanitary Sewer Pipes Condition Assessment Using Multinomial Logistic Regression and Artificial Neural Network

Sanitary sewer pipes infrastructure system being in good condition is essential for providing safe conveyance of the wastewater from homes, businesses, and industries to the wastewater treatment plants. For sanitary sewer pipes to deliver the wastewater to the treatment plants, they must be in good condition. Most of the water utilities have aged sanitary sewer pipes. Water utilities inspect sewer pipes to decide which segments of the sanitary sewer pipes need rehabilitation or replacement. The process of inspecting the sewer pipes is described as condition assessment. This condition assessment process is costly and necessitates developing a model that predicts the condition rating of sanitary sewer pipes. The objective of this study is to develop Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) models to predict sanitary sewer pipes condition rating using inspection and condition assessment data. MLR and ANN models are developed from the City of Dallas’s data. The MLR model is built using 80% of randomly selected data and validated using the remaining 20% of data. The ANN model is trained, validated, and tested. The significant physical factors influencing sanitary pipes condition rating include diameter, age, pipe material, and length. Soil type is the environmental factor that influences sanitary sewer pipes condition rating. The accuracy of the performance of the MLR and ANN is found to be 75% and 85%, respectively. This study contributes to the body of knowledge by developing models to predict sanitary sewer pipes condition rating that enables policymakers and sanitary sewer utilities managers to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.

Towards an Integrated Approach to Infrastructure Damage Assessment in the Aftermath of Natural Hazards

The world has witnessed an alarmingly increasing number of serious natural hazards. In the aftermath of a hazard, relevant authorities/agencies face, among others, the challenging tasks of rapidly evaluating and assessing the damages to infrastructures and restoring their essential functionality and operation. The availability of reliable, high-quality structural and operational/maintenance data of a structure and its health, before and after a natural hazard, can be instrumental in the rapid assessment of a damaged structure. We collectively refer, in this paper, to the existing as-built and facility operational information about a structure or an infrastructure asset represented respectively in Building Information Modeling (BIM) and Infrastructure Asset Management (IAM) systems as Product Lifecycle Data (PLD). Arguably, PLD combined with other post-hazard condition assessment data can provide a more reliable and integrated solution for a rapid damage assessment of buildings and other critical infrastructures. Unfortunately, the application of PLD in this critical area has been unexplored in the literature, and the mapping between PLD and damage assessment methods is loosely investigated. In an effort to address this research gap, this paper provides a critical analysis of the most common structural damage assessment methods and explores the potential of combining them with PLD to provide more reliable, comprehensive, and integrated solution for damage assessment. Findings from this study could be useful for practitioners in selecting the most appropriate and effective methods to conduct damage and safety assessments of critical infrastructures. The study will also assist the further theoretical developments in the integration of PLD with different damage assessment methods.

Predicting remaining life of lattice steel transmission tower based on condition assessment and corrosion hazard data

This paper presents a novel approach for predicting the remaining life of lattice steel transmission towers in Malaysia. The transmission tower components assessment grouped into ground corrosive condition, foundation condition, lattice steel body of the tower, and conductor. Significant and scientific field investigation and visual assessment evaluate the state of the tower. The corrosion hazard, which is a product of climate and air pollution data, is used. Three separated lines and 84 towers were selected with different ages of the tower in service. The Weighing Scoring Method with Analytical Hierarchy Process to impose the weighed factor was used. The survival function analysis is used to predict service life, and the tower remaining life was determined. The paper shows how to use the model to predict the remaining life of a transmission tower and how this can be used to advise maintenance planning.

A novel method for implementation of corrosion-induced cracks in the finite element models of reinforced concrete structures

Currently, there is a clear need for reliable procedures for condition assessment and service-life evaluation of existing infrastructures. Advanced 3D Nonlinear Finite Element (3D NLFE) analysis has proven to be capable of describing the behavior of reinforced concrete provided that detailed and appropriate condition assessment data are available. The present study aims to review and compare different procedures for coupling 3D NLFE analysis with condition assessment data to model corrosion induced cracking, and, consequently to find a method with better accuracy, less computational cost, and improved robustness. This paper introduces a new method for adding cracked elements directly to the finite element model, called Re-FEM. The force-displacement response, ultimate crack pattern, and failure mode from this model are compared with four other methods for a pull out test case on specimens under an accelerated corrosion process. Using this method, the force displacement response for the corroded specimens was overestimated by about 70%. However, the trend was promising and the failure mode and crack pattern were correct. Moreover, the analysis continued after the peak point in the force-displacement curve, which makes it possible to monitor the behavior of the specimen in the softening regime.

Performance comparison of waste plastic modified versus conventional bituminous roads in Pune city: A case study

Scarce resources in a developing country like India calls for the optimal use of the road infrastructure using scientific pavement management tools. In the process of finding ways for modifying the traditional methods of making bituminous roads, the use of waste plastic as a substitute to a certain percentage of bitumen is a promising alternative. Gradual deterioration of road infrastructure is a major concern. A vast amount of condition assessment data must be collected to identify the major defects and their causes, to plan for an optimum maintenance alternative. This study evaluates the deterioration rates of waste plastic modified bituminous roads (also called plastic roads) as compared to normal bituminous roads in the metro city of Pune, India. Measurement of deterioration is done with field tests and distress surveys. Ranking of distress parameters was done using Analytic Hierarchy Process (AHP) where the relative importance (on Saaty’s scale) assigned to each parameter in the hierarchy was given by the experts (transportation professionals). AHP was implemented using the MATLAB package. The study reveals that the deterioration rate for plastic roads is slower as compared to conventional bituminous roads.

Framework for network-level pavement condition assessment using remote sensing data mining

Pavement condition monitoring is fundamental for the efficient allocation of resources in transportation asset management. However, data collection involves laborious and costly procedures. Our study intends to investigate the usage of remote sensing data for network-level pavement condition assessment that offers a more cost-effective alternative and a rapid infrastructure assessment tool that can be used in the aftermath of natural disasters. Based on an extensive literature review, a data mining framework was established to train models that predict the pavement condition of different road segments. The framework exploits the inherent information of multispectral images by generating spectral related attributes. To identify pavement sampling areas, an automated procedure using image segmentation replaces manual surface digitizing. Unlike previous research, different classification models were used to approximate the mapping function from spectral information to pavement conditions. A preliminary case study was conducted with data provided by the City of Dallas and multispectral images acquired from the Texas Natural Resources Information System. The mean-shift segmentation algorithm was used to locate noise introducing areas on the pavement surface. Four different classification models were trained using k-nearest neighbors, naive Bayes, support vector machines, and a multilayer perceptron. The developed models were employed to predict the road surface condition class of a test set not included in the training procedure. The multilayer perceptron presented the highest accuracy level of 71%, showing that the framework might have the potential for future implementation.

3D surface texture analysis of high-resolution normal fields for facial skin condition assessment.

BackgroundThis paper investigates the use of a light stage to capture high‐resolution, 3D facial surface textures and proposes novel methods to use the data for skin condition assessment.Materials and MethodsWe introduce new methods for analysing 3D surface texture using high‐resolution normal fields and apply these to the detection and assessment of skin conditions in human faces, specifically wrinkles, pores and acne. The use of high‐resolution normal maps as input to our texture measures enables us to investigate the 3D nature of texture, while retaining aspects of some well‐known 2D texture measures. The main contributions are as follows: the introduction of three novel methods for extracting texture descriptors from high‐resolution surface orientation fields; a comparative study of 2D and 3D skin texture analysis techniques; and an extensive data set of high‐resolution 3D facial scans presenting various skin conditions, with human ratings as “ground truth.”ResultsOur results demonstrate an improvement on state‐of‐the‐art methods for the analysis of pores and comparable results to the state of the art for wrinkles and acne using a considerably more compact model.ConclusionsThe use of high‐resolution normal maps, captured by a light stage, and the methods described, represent an important new set of tools in the analysis of skin texture.

Topicality of formation of the universal approach to the financial condition assessment of the enterprise using indicators of financial reporting forms

Financial and economic management of enterprises should be based on the use of structured and reliable data on the financial condition of the enterprise. The main source of such information is the indicators of financial condition of the entity, which currently are accumulated in the financial reporting, the indicators of which are formed using the accounting data as part of management science. The diversity of views on the methodology of the financial condition assessment has led, on the one hand, to consolidation of the scientific substantiation of the authors’ approaches, and on the other hand, to the desystematization of the calculated indicators. However, all contemporary authors agree that these indicators should be calculated on the basis of financial reporting. The challenging issue of forming the universal approach to the development of calculation methodology for indicators of the financial condition based on modern financial reporting forms to improve the quality of management decisions both at the level of top managers of the company (internal level) and at the level of external institutions (external level) has been highlighted in the article. The authors of the article intend to update the knowledge about the significance of the information component of financial reporting indicators and adjust its items to the calculation of indicators of the financial condition. Within the stated purpose, the authors of the article have presented modern views on the systematization of indicators of the financial condition in the areas of study. The applied purpose of calculating the indicators of the financial condition is finding and developing measures aimed at enhancing the potential and profitability of the enterprise as a basis for stable operation and capability enhancement of the enterprise. The authors of the study revealed the following points: the relevance of the development of effective financial policy of the enterprise; the financial condition assessment as a stage of financial analysis; the need to determine the purpose and level of research with regard to the specific entity; sources of origin of data for the financial condition assessment; systematization of diverse indicators of financial condition into clearly identified areas of research with a specific set of indicators in each; understandability of their calculation; the need for a meaningful interpretation of the findings; as well as perspective directions of the financial condition assessment using the computer-aided calculation of indicators of financial condition.

Pattern recognition framework using asynchronous discrete binary data for condition and damage assessment in plate-like structures

Recent advances in energy harvesting technologies have led to the evolution of self-powered structural health monitoring techniques that are energy-efficient. Concurrent to the emergence of self-powered sensing has been the development of power-efficient data communication protocols. One such technology is an energy-aware pulse communication architecture that employs ultrasonic pulses through the material substrate for information forwarding. This results in limited discrete binary data that raises the need for new data analysis methods for structural health monitoring purposes. A pattern recognition framework that allows interpretation of the resulting asynchronous discrete binary data for condition and damage assessment in plate-like structures is presented in this article. The proposed pattern recognition framework is based on integration of image-based pattern recognition using anomaly detection, a pattern anomaly measure, a focal density concept, and the k-nearest neighbor algorithm. Using numerical simulations, damage indication parameters were determined from the strain response of dynamically loaded plates. Simulated test cases considering different levels of damage severity, single and multiple damage regions, loading conditions, and measurement noise were studied to evaluate the effectiveness and robustness of the strategy. Furthermore, the effect of sensor density on the proposed strategy was explored. Results demonstrate satisfactory performance and robustness of the proposed pattern recognition framework for localized damage detection in plate-like structures using limited and low-resolution discrete binary data.

Sudden Event Monitoring of Civil Infrastructure Using Demand-Based Wireless Smart Sensors.

Wireless smart sensors (WSS) have been proposed as an effective means to reduce the high cost of wired structural health monitoring systems. However, many damage scenarios for civil infrastructure involve sudden events, such as strong earthquakes, which can result in damage or even failure in a matter of seconds. Wireless monitoring systems typically employ duty cycling to reduce power consumption; hence, they will miss such events if they are in power-saving sleep mode when the events occur. This paper develops a demand-based WSS to meet the requirements of sudden event monitoring with minimal power budget and low response latency, without sacrificing high-fidelity measurements or risking a loss of critical information. In the proposed WSS, a programmable event-based switch is implemented utilizing a low-power trigger accelerometer; the switch is integrated in a high-fidelity sensor platform. Particularly, the approach can rapidly turn on the WSS upon the occurrence of a sudden event and seamlessly transition from low-power acceleration measurement to high-fidelity data acquisition. The capabilities of the proposed WSS are validated through laboratory and field experiments. The results show that the proposed approach is able to capture the occurrence of sudden events and provide high-fidelity data for structural condition assessment in an efficient manner.