Building Structural Health Research Articles

Harnessing fuzzy logic for building structural health during large diameter tunnelling in urban area

Building structural health is constantly jeopardized by tunnel construction, which thus is in a sustained demand for holistic risk assessment and management approaches. In this study, we propose an enhanced consulting fuzzy model with limited information losses to evaluate the static and dynamic risks of building health deterioration during tunnelling. The model integrates three main strategies including, (i) reducing influence of experts’ bias in the ranking processes; (ii) expanding the space of uncertainties in fuzzy environment, and (iii) controlling risk aversion settings through the prospect theory. The construction of the largest diameter (15.3 m) tunnel in China is adopted as a case study to demonstrate the application of the proposed model in assessing the risk statuses of surrounding buildings. Through the proposed model, the risk index can indicate beneficial or detrimental effects of tunnelling and the degree of risk factors delineates priority actions for risk management. The results enable the development of operational risk abatement procedures to enhance the state of practice of large diameter tunnel construction in complex geological conditions.

Research on health monitoring and damage recognition algorithm of building structures based on image processing

With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of “illness”, if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%.

Monocular vision-based dynamic calibration method for determining the sensitivities of low-frequency tri-axial vibration sensors.

Low-frequency vibrations exist widely in the natural environment and in human activities. Low-frequency tri-axial vibration sensors are enormously applied in the fields of seismic monitoring, building structure health monitoring, aerospace navigating, etc. Their sensitivity calibration accuracy directly determines whether their applications can work reliably. Although the laser interferometry recommended by the International Standardization Organization (ISO) is commonly used to achieve the vibration calibration, it suffers from the shortages of low-frequency range, high cost, low efficiency, and limited applicable environment. In this study, a novel monocular vision-based dynamic calibration method is proposed, which determines the whole sensitivities of tri-axial sensors by the monocular vision method to accurately measure the spatial input excitation. This method improves the calibration performance by eliminating the installation error and enhancing calibration efficiency via decreasing reinstallations. The experimental results compared with the laser interferometry demonstrate that the investigated method can obtain similar calibration accuracy in the range of 0.16-2 Hz with more efficiency. The corresponding maximum relative deviations of X-, Y-, and Z-axial sensitivities were approximately 2.5%, 1.8%, and 0.4%. In addition, the maximum relative standard deviation of the investigated method was only about 0.3% in this range.

A Building Structure Health Monitoring System Based on Inter-Story Drift Angle Measured by Low-Cost Sensors

<p>大地震後為了檢查建築物是否有損壞及保護結構免於因餘震再次破壞，通常會以人工巡檢的方式判斷建築物是否安全，但因需勘查棟數太多致常造成誤判與恢復時間大幅增加的情況。近年來，地震監測網絡已推廣到使用於建築物健康監測上，以便能夠在震後第一時間檢測出損傷位置及破壞程度，快速地進行補強及救災的動作。目前常用的結構健康監測(Structure Health Monitoring, SHM)系統多使用傳統加速度計，但加速度計系統價格昂貴且強震資料易受汙染，因此使得建築物SHM的推廣受到限制，本研究提出低價格的傾斜儀做為SHM之量測設備，可改善上述問題。本研究先透過SAP2000軟體分析某三層樓構架，了解構架之變形特性，且推論出傾斜儀能夠裝設在柱中間做為量測點，可直接測得層間變位角。再研究如何快速判斷建築物各樓層之健康狀況，並且計算出傾斜儀之誤差值。也將此結果應用在2023日本E-Defense足尺十層樓鋼結構振動台實驗，將傾斜儀安裝在受損機會較大之樓層2至5樓。結果顯示出，在沒有破壞發生時，結構沒有殘餘轉角產生，且層剪力對轉角圖為直線；而當結構有破壞發生，會有明顯殘餘轉角以及層剪力對轉角歷時圖會有迴圈發生，跟數值分析之結果類似，故可知道本研究建議之健康監測方法確實可行且具有經濟可行性。</p> <p> </p><p>After a major earthquake, to check for damage in buildings and protect structures from aftershock damage, manual inspections are usually used to determine whether buildings are safe. However, due to the large number of buildings need inspection, misjudgment may occur and recovery time are often delayed. In recent years, building level monitoring networks have been promoted for building health monitoring, which can detect the location and degree of damage immediately after an earthquake, and quickly carry out retrofit work. The common structural health monitoring (SHM) systems use traditional accelerometers for monitoring, but they are expensive and the data of strong earthquake may be polluted. This study proposes a low-cost tiltmeter for SHM, which can improve the extent of above problems. This study first analyzed a three-story building structure using SAP2000 to understand its deformation characteristics, and infer that the tiltmeter can be installed at the mid-height of the column, which can directly measure the Inter-story Drift Angle (IDA) and quickly determine the building health status. We then applied this theory to a 10-story steel structure test program of E-Defense in Japan in 2023, the MEMS tiltmeter was installed on the four floors with a high chance of damage. The results show that if no damage occurs, there is no residual IDA, and the Story Shear-IDA diagram is a straight line. When damage occurs, there is a residual IDA and a hysteresis loop on the Story Shear-IDA diagram.</p> <p> </p>

Digital Twin of Built Structures assisted by Computer Vision Techniques: Overview and Preliminary Results

Digital Twin is an effective platform for analyzing, visualizing, and interpreting the condition of as-built structures based on sensor measurement data. Based on the data inflow from the as-built structures, the associated models (typically finite element models) are updated, and/or the full behaviors of the structures are replicated in the virtual space. Despite its potential, field implementation of digital twin concepts often faces challenge, because the specific forms of digital twin, such as sensor types/locations, structural model development, and data fusion algorithms, depend strongly on the case-specific objectives. Focusing on the digital twin concepts assisted by computer vision techniques, this research aims at facilitating the implementation of those concepts by presenting the definition, setting, and preliminary results of digital twin in different application contexts, including post-earthquake building assessment and structural health monitoring based on multiple types of measurements. This research is expected to contribute to the broader impact of digital twin concepts in the structural engineering community.

Research on stress curve clustering algorithm of Fiber Bragg grating sensor

The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors.

Wearable Optical Fiber Sensors for Biomechanical Measurement in Medical Rehabilitation: A Review

With the increasing demand for rehabilitation of the elderly population and patients with limb injuries, it is imperative to develop new wearable health monitoring system and assistive rehabilitation equipment. Optical fiber sensing has the advantages of light-weight, anti-electromagnetic interference, higher strain and elastic limit, safety and dependableness. It has been significantly utilized in engineering prospecting, building structure monitoring, and medical and health fields. At present, the application of optical fiber sensing in medical rehabilitation has been developed rapidly, however, the discussion and summary of its key theories and technologies are insufficient. This review details different types of optical fiber sensing mechanisms and devices, as well as the measurement of physical parameters using optical fiber sensors and application examples in the field of medical rehabilitation. This review systematically analyzes the advance of the sensitivity, flexibility, accuracy, and wearability of the optical fiber sensing in the monitoring of human body sign information. This review also discusses the challenges of making a new generation of optical fiber sensors that fit human joints, muscles, and rehabilitation equipment. Finally, this review provides a research direction for the realization of flexible, comfortable, safe, stable monitoring of human biomechanical parameters and real-time precise control of rehabilitation equipment in the future.

A Quantitative Investigation of the Effect of Scan Planning and Multi-Technology Fusion for Point Cloud Data Collection on Registration and Data Quality: A Case Study of Bond University’s Sustainable Building

The construction industry requires comprehensive and accurate as-built information for a variety of applications, including building renovations, historic building preservation and structural health monitoring. Reality capture technology facilitates the recording of as-built information in the form of point clouds. However, the emerging development trends of scan planning and multi-technology fusion in point cloud acquisition methods have not been adequately addressed in research regarding their effects on point cloud registration quality and data quality in the built environment. This study aims to extensively investigate the impact of scan planning and multi-technology fusion on point cloud registration and data quality. Registration quality is evaluated using registration error (RE) and scan overlap rate (SOR), representing registration accuracy and registration coincidence rate, respectively. Conversely, data quality is assessed using point error (PE) and coverage rate (CR), which denote data accuracy and data completeness. Additionally, this study proposes a voxel centroid approach and the PCP rate to calculate and optimize the CR, tackling the industry’s challenge of quantifying point cloud completeness.

Smartphone based structural health monitoring using deep neural networks

In order to create a low-cost and easy-to-implement structural health monitoring method, this paper uses smartphones to collect data on the movement of a building to determine whether it has suffered any damage. Smartphones have easy handling, sensors embedded, and data processing properties. The smartphone-based structural health monitoring system have many advantage over the classical methods. Recurrent fuzzy neural network of long- and short-term memory is applied to determine the damage of the structure. Comparisons with classical neural networks and fuzzy systems are given to show the proposed deep neural networks have better performances. Finally, several experiments are applied to evaluate the proposed methods for the determination of structural damage. • A smartphone-based building structural health monitoring system is developed. • This system uses fuzzy long and short term memory (LSTM) to deal the data obtained from the smartphone. • This system is implemented in a prototype of structural health monitoring.

MEMS Dynamic Characteristics Analysis of Electrostatic Microbeams for Building Structure Monitoring

Building structure health monitoring is essential for modern buildings, sensors related to building structure health monitoring are often made with microelectrostatic cantilever beam (MECB), and the performance of this kind of devices is often affected by instability, which affects the measurement results and accuracy. Therefore, it is necessary to study the nonlinear dynamic characteristics of the MECB in the process of bending and pull‐in. In this paper, based on the energy principle and fluid pressure film damping effect, the dynamic equation mathematical model of MECB is established and then the dynamic characteristics of the pull‐in and lift‐off voltage of the MECB and the harmonic motion characteristics under the bias voltage are obtained, which provides guidance for the design of the electrostatic driving sensor.

A temperature-insensitive FBG acceleration sensor with sinusoid-shaped curved beams

PurposeThis study aims to solve the problem of temperature cross sensitivity of fiber Bragg grating in structural health monitoring, proposing a novel acceleration sensor based on strain chirp effect which is insensitive to temperature.Design/methodology/approachA kind of M-shaped double cantilever beam structure is developed. The fiber grating is pasted in the gradient strain region of the beam, and the chirp effect is produced under the action of non-uniform stress, and then the change of acceleration is converted into the change of reflection bandwidth to demodulate and eliminate the temperature interference. Through theoretical analysis, simulation and experimental verification with rectangular beam sensor.FindingsThe results show that the sinusoidal curvature beam sensor is insensitive to the change of temperature and is more likely to produce chirp effect. The sensitivity is about 317 pm/g, and the natural frequency is 56 Hz.Originality/valueThis paper fulfils an insensitive to temperature changes sensor which has effectively solved the temperature cross-sensitivity problem in building structure health monitoring.

Comparison of sensitivity matrix method, power function-based response surface method, and artificial neural network in the analysis of building fire egress performance

Unlike the areas of building energy and structural health, performance monitoring tools are currently absent in the area of building fire protection. Computational Fluid Dynamics (CFD) models like Fire Dynamics Simulator (FDS) are widely applied in building fire performance design, which can be equally used to predict changes of building fire performance. However, due to its time-consuming nature, it is not realistic to apply FDS frequently. The sensitivity matrix method (SMM) has been discussed as a quick method to predict changes of building fire egress performance. However, this approach can have significant uncertainties when being applied to datasets with many input parameters due to its inherent incapability of predicting accurately system responses if input data are considerably far away from the baseline points around which a SMM is developed. Response surface methods (RSM) are commonly used to characterize the relationships between input variables and output quantities for complicated problems. Different from conventional RSMs, a novel two phase power function fitting process is proposed to develop substitute algebraic models of the available safe egress time (ASET) from FDS numerical experiments based on a theorem which states that an output variable is proportional to the product of input parameters to their respective powers if the output variable is proportional to each input parameter to some power and the input parameters are independent of each other. An artificial neural network (ANN) is a universal method to approximate any arbitrary complicated, nonlinear system response with limited number of discontinuities without deep understanding of how the system works. This paper employs MATLAB's feedforward neural networks with error backpropagating algorithm to approximate the FDS response. Applicability in terms of uncertainties including system bias and relative standard deviation (RSD) or percentage of predictions falling in a preset acceptable error range are compared among RSMs developed from various datasets, ANNs with various hidden layer sizes and dataset sizes, and SMMs which use the same fire scenarios in a small three-story apartment building. The result shows that it is possible for ANNs to have lowest model uncertainties and highest percentage of predictions within the preset 20% error scope as far as the specific fire egress safety problem discussed in this paper is concerned, but the cost of developing a SMM, namely the number of data cases, is the lowest. Due to the different aspects of RSMs, ANNs, and SMMs, to better understand the building fire performance gap continuously, a hybrid strategy of starting with SMMs followed by RSMs and/or ANNs is recommended in a fire performance monitoring tool. • A novel Response Surface Method is proposed based on two-phase power function fitting. • MATLAB's feedforward NN with error backpropagating algorithm is employed to approximate the FDS simulation results. • The applicability of Sensitivity Matrix Method, Response Surface Method, and Artificial Neural Network is compared. • A hybrid strategy of starting with SMM followed by RSM and/or ANN is recommended in a fire performance monitoring tool.

Simultaneous measurement of temperature and strain based on SCF-based MZI cascaded with FBG

A novel seven-core fiber (SCF)-based sensor capable of simultaneous measurement of temperature and strain was proposed and experimentally demonstrated. The sensor consists of a fiber Bragg grating (FBG) and a SCF-based Mach-Zehnder interferometer (MZI). The MZI is formed by splicing a short section of SCF between two single-mode fibers (SMFs), and in the meantime, two fiber tapers that act as light splitter and coupler are formed at both ends of the SCF by the fusion splicing method. In addition, the FBG with a central wavelength of 1547.98 nm is cascaded at the end of one fiber taper, and thus simultaneous measurement of temperature and strain can be realized. The experiment result shows that the SCF-based MZI is only sensitive to temperature and has the higher temperature sensitivity of 93.11 pm/°C, while the FBG is sensitive to temperature and strain with sensitivities of 11.46 pm/°C and 0.627pm/µε, respectively. The corresponding temperature and strain resolutions of the sensor are 0.21°C and 27.95µε, respectively. The proposed sensor has the advantages of simple fabrication and high repeatability and has potential applications in many fields such as engineering machinery and building structure health monitoring.

Effect of heat and moisture transfer on the growth of mould on the inner surface of walls: A case study in Dalian of China

Due to the influence of outdoor climate and indoor human activities, the moisture accumulation of building enclosure structure will lead to the growth of mould on the interior surface of the wall, damage the building structure and endanger human health. Taking an office building in Dachangshan Island of Dalian, a cold area in North China, as an example, the outdoor relative humidity is high, and the temperature difference between indoor and outdoor is large during the heating period, which is easy to cause the increase of moisture content of the enclosure structure and the growth of mould. This study focuses on the influence of the heat and moisture transfer characteristics of the office building wall on the growth process of mould on the interior surface of the building. The dominant fungi of indoor air and wall surface were Penicillium and Cladosporium. WUFI software was used to simulate the heat and moisture transfer and the growth process of mould on the inner surface of the wall. The results show that the relative humidity of the air micro layer on the inner wall surface of the office building reaches more than 80% at the beginning of heating period in November every year, the mould hypha grows significantly in December and brings pollution risk. Finally, the relative humidity drops below 70% after the heating period is over in April, and the mould stops growing. The main reason for this phenomenon is that the condensation interface in the wall structure during the heating period leads to the accumulation of water in the wall, which is then transmitted to the wall surface through the wall pores, thus increasing the moisture content. This method provides a r eliable way for the numerical simulation of indoor mould growth risk.

Full-Scale Building Structural Health Monitoring by Shake Table Tests and Extreme Learning Machine

Structural health monitoring (SHM) represents a type of techniques that enables the monitoring of structural motion of a building structure during external loading such as earthquakes. SHM always provides the in-depth understanding of the severity and location of damage to a structure without requiring visual structural inspection. In this research, it proposes a data-driven approach for building structural health monitoring based on data collected from Shake Table tests. Experimental data from shake table experiments has been utilized and analyzed. Fast Fourier Transformation is employed to extract the SHM related data-features. Engineers are also invited to label the ground truth risk levels according to the observation from the shake table test. A data-driven classifier namely extreme learning machine is introduced to classify the structural risk based on the extracted features. Comparison is performed with other state-of-art machine-learning classification algorithms. Numerical experiments validates the effectiveness, efficiency, and universality of the proposed method.

Digital Vision Based Concrete Compressive Strength Evaluating Model using Deep Convolutional Neural Network

Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples. The experimental results indicated a root mean square error (RMSE) value of 3.56 (MPa), demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations. This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.

Investigations optimum scheduling and TCP mechanism of hybrid topology sensor network in building SHM

Abstract Sensor technology has opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure such as large buildings. Compare to traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure at a lower price and greater range. Numerous domestic buildings, especially long-span buildings, have a low-profile frequency response that is challenging to accurately measure with the application of sensors suitable for integration with lower-cost, lower-power-profile wireless sensor networks. To cover the whole area of interest, sensor network topology is one of the most important issues. The objective of this article is to investigate of hybrid topology scheduling algorithm to determine the optimum TCP (Transmission Control Mechanism) mechanism for assessing large building structural health data using various kinds TCP and scheduling algorithm. Result show that, the Full TCP mechanism is selected as optimum TCP mechanism for monitoring building structural health data.

Building structural health monitoring using dense and sparse topology wireless sensor network

Wireless sensor technology has been opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure. Compare to the traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure health at a lower price. Numerous domestic buildings, especially longer-span buildings have a low frequency response and challenging to measure using deployed numbers of sensors. The way the sensor nodes are connected plays an important role in providing the signals with required strengths. Out of many topologies, the dense and sparse topologies wireless sensor network were extensively used in sensor network applications for collecting health information. However, it is still unclear which topology is better for obtaining health information in terms of greatest components, node\'s size and degree. Theoretical and computational issues arising in the selection of the optimum topology sensor network for estimating coverage area with sensor placement in building structural monitoring are addressed. This work is an attempt to fill this gap in high-rise building structural health monitoring application. The result shows that, the sparse topology sensor network provides better performance compared with the dense topology network and would be a good choice for monitoring high-rise building structural health damage.

Performance Assessment of tree topology sensor network based on scheduling algorithm for overseeing high-rise building structural health information

Wireless sensor network (WSN) is the new invention applied for detecting the damage of the high-rise building structural health. The research has been focused in the early age and curing phase period. The objective of this article is to provide a solution to find out the optimum throughput, queue loss and queue size mechanism technique for tree topology sensor network in high-rise civil building structural health. Sensor technology offers new opportunities to advance monitoring for civil large building infrastructure health and maintenance by providing relevant information regarding the condition of high-rise building structure strength at a light price compared with traditional monitoring tactics. To cover the whole patient area, sensor network topology is one of the most important issues. From all investigation result shows that, queue loss of the FQ (fair queuing) buffer mechanism with Newreno, Reno, Tahoe and Vegas based tree topology network offer optimum buffering technique when Vegas DT (drop tail), SFQ (stochastic fair queuing) delivered the same output. On the other hand, Vegas based TCP (transmission control protocol) mechanism with any scheduling algorithm provides the optimum throughput tree topology sensor network.

M-ARRAY QUADRATURE AMPLITUDE MODULATION WIRELESS SENSOR NETWORK MODULATOR RELIABILITY AND ACCURACY ANALYZE IN CIVIL SHM

Wireless Sensor Network (WSN) is the new invention applying for assessment the damage of the historica l or high rise civil building structural health. Tech nical challenges affecting deployment of wireless s ensor network including the range of the transmission pro blem, low data transmission rate of the existing SH M strategies. The most vital factor of SHM wireless s ensor systems is the modulator accuracy and reliabi lity that qualify the wireless communication system to a ssess large building structure health Information. The objective of this article is to provide solution to measure both reliability and accuracy of the wirel ess sensor network modulator. we computed M-array QAM modulator BER and compare the simulation result with theoretical to find out optimum modulation techniqu e for transmission System with considering maximum data rate, AWGN channel and also measured modulator accuracy based on ZigBee by computing M-array modulator Error Vector Magnitude (EVM) to quantify the transmitter quality.