Health Monitoring Of Buildings Research Articles

Advanced Predictive Structural Health Monitoring in High-Rise Buildings Using Recurrent Neural Networks

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.

Optimal Sensor Placement for Enhanced Efficiency in Structural Health Monitoring of Medium-Rise Buildings.

Output-only modal analysis using ambient vibration testing is ubiquitous for the monitoring of structural systems, especially for civil engineering structures such as buildings and bridges. Nonetheless, the instrumented nodes for large-scale structural systems need to cover a significant portion of the spatial volume of the test structure to obtain accurate global modal information. This requires considerable time and resources, which can be challenging in large-scale projects, such as the seismic vulnerability assessment over a large number of facilities. In many instances, a simple center-line (stairwell case) topology is generally used due to time, logistical, and economic constraints. The latter, though a fast technique, cannot provide complete modal information, especially for torsional modes. In this research, corner-line instrumented nodes layouts using only a reference and a roving sensor are proposed, which overcome this issue and can provide maximum modal information similar to that from 3D topologies for medium-rise buildings. Parametric studies are performed to identify the most appropriate locations for sensor placement at each floor of a medium-rise building. The results indicate that corner locations at each floor are optimal. The proposed procedure is validated through field experiments on two medium-rise buildings.

An Integrated Data Acquisition Approach for the Structural Health Monitoring and Real-Time Earthquake Response Assessment of a Retrofitted Adobe Church in Peru.

The structural health monitoring (SHM) of buildings provides relevant data for the evaluation of the structural behavior over time, the efficiency of maintenance, strengthening, and post-earthquake conditions. This paper presents the design and implementation of a continuous SHM system based on dynamic properties, base accelerations, crack widths, out-of-plane rotations, and environmental data for the retrofitted church of Kuñotambo, a 17th century adobe structure, located in the Peruvian Andes. The system produces continuous hourly records. The organization, data collection, and processing of the SHM system follows different approaches and stages, concluding with the assessment of the structural and environmental conditions over time compared to predefined thresholds. The SHM system was implemented in May 2022 and is part of the Seismic Retrofitting Project of the Getty Conservation Institute. The initial results from the first twelve months of monitoring revealed seasonal fluctuations in crack widths, out-of-plane rotations, and natural frequencies, influenced by hygrothermal cycles, and an apparent positive trend, but more data are needed to justify the nature of these actions. This study emphasizes the necessity for extended data collection to establish robust correlations and refine monitoring strategies, aiming to enhance the longevity and safety of historic adobe structures under seismic risk.

In-Fiber Hybrid Structure Sensor Based on the Vernier Effect for Vector Curvature and Temperature Measurement

A vector curvature and temperature sensor based on an in-fiber hybrid microstructure is proposed and experimentally demonstrated. The proposed scheme enables the dimensions of the Fabry–Perot and Mach–Zehnder hybrid interferometer to be adjusted for the formation of the Vernier effect by simply changing the length of a single optical fiber. The sensor is fabricated using a fiber Bragg grating (FBG), multimode fiber (MMF), and a single-hole dual-core fiber (SHDCF). The sensor exhibits different curvature sensitivities in four vertical directions, enabling two-dimensional curvature sensing. The temperature and curvature sensitivities of the sensor were enhanced to 100 pm/°C and −25.55 nm/m−1, respectively, and the temperature crosstalk was minimal at −3.9 × 10−3 m−1/°C. This hybrid microstructure sensor technology can be applied to high-sensitivity two-dimensional vector curvature and temperature detection for structural health monitoring of buildings, bridge engineering, and other related fields.

Smart Clay Bricks enabling Diffuse Strain Monitoring in Masonry Buildings: Progresses at Full-scale

Strain monitoring in masonry buildings can provide useful information regarding the onset of structural damage during operating conditions. This is because when cracks develop on masonry components, i.e., clay bricks and mortar layers, induce discontinuities and redistributions in their original stress/strain configurations. Although the application of local strain measurements for Structural Health Monitoring (SHM) of masonry buildings can be effective in detecting anomalies and modifications in their structural response caused by crack development, achieving diffuse strain monitoring remains a challenge in practical SHM applications, nowadays. This is mainly because strain sensors currently available on the market present several practical limitations, including scalability issues, which often make their large-scale application to masonry buildings difficult. In recent years, for overcoming the drawbacks of traditional sensor systems, innovative smart clay bricks have been developed, able to measure deformation when incorporated into load-bearing masonry structures. In that sense, this paper presents the latest advancements and applications of this brand-new sensing technology for SHM of real-scale masonry buildings.

Temperature insensitive distributed wide-dynamic-range strain sensing based on polarization-maintaining photonic crystal fiber

In this paper, a temperature insensitive wide-dynamic-range distributed strain sensor with centimeter-level spatial resolution based on optical frequency domain reflection technology is proposed. It adopts a polarization-maintaining photonic crystal fiber as the sensing fiber, and experimental results demonstrate that its temperature sensitivity is much smaller than that of the standard polarization-maintaining fiber. Benefited from the specially designed cross-correlation algorithm, the sensor achieves the maximum measurable strain reaches 7000 μϵ with spatial resolution of 1.66 cm. This provides a new approach to address current issues with special fiber sensors, such as single-point sensing and low spatial resolution, which shows great significance for structural health monitoring of buildings, bridges and tunnels with urgent requirement of large dynamic range.

Revolutionizing Depth Sensing: A Review Study of Apple LiDAR Sensor for as-built Scanning Applications

Incorporating the LiDAR sensor in the most recent Apple devices represents a substantial development in 3D mapping technology. Meanwhile, Apple's Lidar is still a new sensor. Therefore, this article reviews the potential uses of the Apple Lidar sensor in various fields, including engineering and construction, focusing on indoor and outdoor as-built 3D mapping and cultural heritage conservation. The affordable cost and shorter observation times compared to traditional surveying and other remote sensing techniques make the Apple Lidar an attractive choice among scholars and professionals. This article highlights the need for continued research on the Apple LiDAR sensor technology while discussing its specifications and limitations. A comprehensive review found that the Apple LiDAR sensor has shown promise in capturing 3D point clouds of small to medium-sized objects with exceptional detail. This technology offers a cost-effective and accessible option to scan areas faster and analyze data more quickly and automatically for 3D mapping and modelling in indoor and outdoor environments, particularly in areas with restricted access when using other traditional techniques. It also opens the door for more sophisticated applications in future studies, including cultural heritage conservation, archaeological investigations and feature detection, building health monitoring and many more.

A bootstrap-based stochastic subspace method for modal parameter identification and uncertainty quantification of high-rise buildings

This paper proposes a bootstrap-based stochastic subspace method for modal parameter identification and uncertainty quantification of high-rise buildings. Firstly, the stochastic subspace method in combination with the bootstrap technique enables the estimation of multiple sets of modal parameters from raw data series. Then, a bootstrap-based stabilization diagram is used to extract the physical modes. Finally, the modal identification and associated uncertainty quantification results are determined via statistical analysis. Through a numerical study of high-rise buildings, the performance of the proposed method is validated, demonstrating that it can provide reliable modal parameter identification and uncertainty quantification as well as has good noise immunity. Furthermore, the developed approach is employed to identify modal parameters of a 600-m-tall skyscraper during a typhoon, proving its applicability to field measurements and structural health monitoring of high-rise buildings. This paper aims to present a novel tool for modal parameter identification and associated uncertainty quantification of high-rise buildings.

Dynamic response of the annular hole defect under ultrasonic in brick

In recent years, ultrasonic diagnostics and health monitoring of buildings have become widely used in construction. However, the accuracy of ultrasonic devices in heterogeneous media such as concrete, brick and other elements is fraught with difficulties and leads to errors. The article considers the problem of the dynamic response of a defect in the form of a cylindrical hole in a brick during ultrasonic diagnostics. The problem was simulated in the ANSYS environment using the finite element method. A dynamic stimulus with a frequency of 60 kHz is applied in the immediate vicinity of the defect and the response is numerically determined at a point symmetrically located from the defect. The results of numerical analysis in the form of fields of displacements, stresses and deformations have been obtained. It is shown that to effectively determine the geometry of a defect in the form of a hole, it is necessary to analyse data on the 3rd or 4th half-waves of the response.

Optical Fiber Sensing-Aided 3D-Printed Replacement Parts for Enhancing the Sensing Ability of Architectural Heritage.

This study discussed the application of optical fibers in addressing the problem of insufficient light harvesting and sensing health monitoring in ancient buildings. Based on three-dimensional (3D) printing technology to fix the light-harvesting lens and conducting optical fiber, develop the replacement parts that can be buried into the optical fiber of ancient buildings. By introducing the experimental application to improve the experimental quality of research and teaching. Firstly, it highlights the advantage that the optical fiber plus lens structure design can make the natural light introduced for a long time; secondly, it points out that the buried optical fiber structure design does not affect the warmth and sound insulation of the building; finally, the health monitoring of the building is realized through the proposed method of buried optical fiber sensing. The design scheme adopts a fiber optic light transmission and sensing system, which can realize the whole system's corrosion resistance, after laying buried and low-cost operation.

Health monitoring of historic buildings using machine learning in real-time internet of things

<span>In this paper, structural health monitoring (SHM) is used to detect the damage level for the historic building. The damaged level is defined based on the support vector machine (SVM) algorithm to extract the damage feature. Physical checks allow us to detect any damage or structural degeneration. Supervised training machine learning (ML) is used as a tool to examine accelerometer data to ascertain the condition of structures following an occurrence. The three training models, the SVM, the random forest linear classification, and the k-nearest neighbor (KNN) model are tested and compared to classify data. The data obtained from structural health monitoring, teams of responders, and investigators can be used to manage the most vulnerable structures. The accuracy of the SVM algorithm was found up to 94% accurate and precise, at a high level. The internet of things (IoT) architecture is also introduced with SVM learning algorithms for early warning. The proposed system makes use of an SHM system to identify seismic events or accelerations. The IoT system SHM uses real data from the structure, allowing for online damage identification and ongoing monitoring. A dashboard is used to represent the monitoring data and the damage level.</span>

Design development and testing of traffic induced wind based artificial tree type hybrid energy harvester for wireless sensor nodes

With the rapid development of the urban system, the structural health monitoring (SHM) of high-rise buildings, flyovers, and bridges have attracted researchers’ attention. Currently, wireless sensor nodes (WSNs) are used for the SHM, however, WSNs use batteries as power sources which have limited shelf life. To attain the self-sustain long-term operation of WSNs, ambient energy from their surrounding can be harvested for their power requirements. In this research, a traffic induced wind based artificial tree type hybrid energy harvester is designed, developed, and tested. The developed prototype is able to harvest traffic induced wind. The prototype consists of multiple piezoelectric leaves and two electromagnetic energy harvesters (EM-EH) - short beam EM-EH and long beam EM-EH. Each EM-EH is composed of a cantilever beam, a permanent magnet, an airfoil, and a planar coil. Test results reveals that at a maximum windspeed of 10 m/s, the prototype can harvest 1.60 μW from a single piezoelectric leaf, 53 μW from a short beam EM-EH and 50 μW from a long beam EM-EH at optimum load. This artificial tree type hybrid energy harvester will help in the deployment of WSNs and in the beautification of the urban system.

All-Fiber In-Line Twist Sensor Based on a Capillary Optical Fiber

Twist sensors have emerged as crucial tools in the field of structural health monitoring, playing a significant role in monitoring and ensuring the integrity of critical infrastructure such as dams, tunnels, bridges, pipelines, and buildings. We proposed and demonstrated an all-fiber in-line twist sensor which was based on a capillary fiber spliced between two single-mode fibers with a transverse offset. Through a series of experiments, the sensor’s performance was evaluated and quantified. The results showcased remarkable twist sensitivities in both clockwise and anticlockwise directions. With a transverse offset of 8.0 µm, the sensor exhibited twist sensitivities of −0.077 dB/° and 0.043 dB/° in the clockwise and anticlockwise directions, respectively, in the measured twist range from 0 to 90°. Furthermore, it was also demonstrated that the sensor was temperature insensitive at the chosen wavelength of 1520 nm, which can assist in increasing measurement accuracy. Our sensor’s low cost, simplicity of manufacture, and improved performance will push forward its adoption in future engineering applications such as structural health monitoring in dams, tunnels, and buildings.

An approach for considering the object surface properties in a TLS stochastic model.

The interaction between laser beams and backscattering object surfaces lies at the fundamental working principle of any Terrestrial Laser Scanning (TLS) system. Optical properties of surfaces such as concrete, metals, wood, etc., which are commonly encountered in structural health monitoring of buildings and structures, constitute an important category of systematic and random TLS errors. This paper presents an approach for considering the random errors caused by object surfaces. Two surface properties are considered: roughness and reflectance. The effects on TLS measurements are modeled stepwise in form of a so-called synthetic variance-covariance matrix (SVCM) based on the elementary error theory. A line of work is continued for the TLS stochastic model by introducing a new approach for determining variances and covariances in the SVCM. Real measurements of cast stone façade elements of a tall building are used to validate this approach and show that the quality of the estimation can be improved with the appropriate SVCM.

Verification of the Mode Decomposition Technique for Closely Distributed Modal Systems in the State Space Domain.

This study aims to propose and validate the state space mode decomposition technique for precise mode separation of non-classical damping systems and closely distributed modal systems. To assess the reliability and applicability of this technique, a 40-story building with a tuned mass damper is investigated, and acceleration responses measured by the building's health monitoring system are used for the verification of the technique. The mode separation results reveal that the separated modal power spectrum becomes distorted at neighboring natural frequency ranges when the performance index only considers the concentration of power spectral energy at the target natural frequency. However, by introducing an augmented performance index that includes a constraint condition to account for distortion, more accurate mode decomposition can be achieved.

Vibration-based building health monitoring using spatio-temporal learning model

Vibration-based building health monitoring is a promising and feasible approach to assess the operational state of building structures in a remote, automated, and continuous fashion; however, efficiently handling high-dimensional vibration signals from multiple sensors and effectively coping with missing/noisy data represent two main technical challenges. In order to overcome these issues, this study proposes a novel, reliable and robust framework, abbreviated CLG-BHM, based on a hybrid deep learning architecture. First, the framework uses a 1D convolutional neural network layer to learn low-dimensional representation vectors of long sensor signals, which preserve underlying structures’ dynamic characteristics. Second, temporal relationships within data are distilled via a Long-Short Term Memory layer. Third, the representation vectors of sensors are aggregated with those of their neighbors in a principled way via a graph attention network layer, resulting in a new latent representation rich in both temporal and spatial information. Finally, the latter is gone through a fully-connected layer to provide damage detection results. The performance and viability of the present method are evidenced via various examples involving a simple lumped mass structure, a semi-rigid steel frame, and an experimental 4-story structure from the literature. Moreover, a robustness study is performed, showing that the method can provide reasonable results with the presence of noisy and missing data.

Study on detecting deterioration in beam structure stiffness using deformed shape

This study presents a diagnostic technique for identifying the loss of stiffness of homogeneous beams using only the deformed shape in an effort to develop more effective building health monitoring instruments. Method based on a correlation between beam deformation at two damaged and undamaged selection states. Diagnostic indicators such as the correlation coefficient (CC) and the absolute value of the mean deviation ratio (MAPD) are extensively used to determine the presence of decreased stiffness. Using an approach based on finite element analysis, the deformation line data is computed. Various failure scenarios are proposed to evaluate the beam damage detection sensitivity of the indicators. In the case of beam damage, the CC index value is less than 1, and in the absence of damage, it is equal to 1. Similarly, for the MAPD index, the value is greater than 0 in the case of damage to the beam and 0 in the case of no assumed failure. The result demonstrates that the deformed line data can be used to calculate the assessment indicators for determining the onset of damage in homogeneous beams. Indicators that have not yet determined the location of the local stiffness decline on the beam when using input data are the deformed shape corresponding to the survey examples in this study.

Structural health monitoring of ASCE benchmark building using machine learning algorithms

Structural health monitoring of ASCE benchmark building using machine learning algorithms

ABS-PLA-Al composite for digital twinning of heritage buildings

In the past two decades, significant studies have been reported on the use of 3D-printed thermal sensors for online health monitoring of heritage buildings. But hitherto little has been reported on the 4D capability of such thermal sensors for the digital twinning of heritage buildings. In this study, 80% acrylonitrile butadiene styrene (ABS) and 20% polylactic acid (PLA) matrix (by wt %), was reinforced with 5% Al powder (≈45-50 µm) based on melt flow index (MFI). The substrate (as a passive sensor) of ABS-PLA-5 %Al was 3D printed with fused filament fabrication (FFF) for the construction of a micro-strip patch antenna (MPA) as an internet of things (IoT) based solution for online health monitoring and digital twinning of heritage buildings. The dielectric properties (dielectric constant (ɛr) and loss tangent (tanδ)) were explored by using a ring resonator test to ascertain the dimensions and design of the passive sensor. For 4D capabilities, the prepared sensor was exposed to two stimuli (temperature and moisture) for exploring its use as a digital twinning component.

A Multibranch U-Shaped Tunable Encoding Chipless RFID Strain Sensor for IoT Sensing System

Structural health monitoring (SHM) is essential for modern large buildings and infrastructure. Radio frequency identification (RFID) widen a new paradigm for SHM in the Internet of Things (IoT) era. This article introduces a low-cost intelligent RFID monitoring system for future IoT applications. Through adding a multiparameter sensing strategy to the passive RFID tags that can be deployed on a large scale to form a sensor network, which expands the coverage of IoT in key positions health monitoring of buildings. In this work, a novel chipless RFID strain sensing tag is designed to characterize the magnitude and direction of metal surface strain, and a low-cost detection method suitable for large mechanical structures is proposed. The traditional strain antennas focus on identifying the strain characteristics without the function of encoding, and there are limitations, such as the rigid measurement methods and the expensive measurement instruments. The tag designed in this article integrates both strain sensing and encoding functions, and has the advantages of small size, high data capacity, and information reading is not easily affected by environmental noise. This article proposes an economical and flexible tag spectrum extraction method, which realizes the intelligent collection and transmission of tag data by connecting lightweight vector network analyzer (VNA) with microcontroller. Combined with the IoT, this method can be well applied to the security assessment and damage detection of large infrastructure structures, which provides a new approach to modern building health monitoring applications from integrated tag design to intelligent detection and risk assessment schemes.