Distributed Fiber Optic Sensors Research Articles

The water vapor distribution profile in dry soil layer during evaporating with high spatial resolution monitored by distributed fibre-optic sensing technology

The dry soil layer (DSL) typically forms on the surface of sandy soils during the evaporation process, with water in the DSL moving solely as vapor. Understanding the DSL thickness and its water vapor distribution is crucial for accurately estimating soil water flux, improving evaporation mechanisms, and enhancing ecological conservation efforts. This study employed distributed fibre-optic sensing (DFOS) technology to achieve high spatial resolution measurements (up to 1 mm) of the water vapor distribution within the DSL. We measured soil water vapor profiles in a sand column subjected to evaporation over 24 days using a 60 cm long relative humidity sensing probe and an optical frequency domain reflection (OFDR) interrogator. We effectively captured the downward migration of the evaporating surface from the sand surface and quantified the inhibitory effect of DSL thickness on evaporation using a logarithmic function. Additionally, we identified a linear distribution of water vapor with depth in the DSL. Our findings offer novel insights into the role of DSL in the hydrological behaviour of unsaturated soils and demonstrate the potential of our approach for investigating dynamic changes in in-situ DSLs, particularly in arid and semi-arid regions.

Bond performance and damage assessment of self-sensing steel–fiber composite bar with geopolymer concrete

This study employed a novel steel–fiber composite bar (SFCB) enhanced with Distributed Optical Fiber Sensors (DOFS) technology to reveal the local bonding and damage mechanisms between composite reinforcement and seawater sea sand geopolymer concrete. By embedding DOFS within the steel core of the SFCB reinforcement, the local bond stress between the reinforcement and geopolymer concrete was monitored. The continuous measurement provided by DOFS revealed a non-uniform strain distribution along the length of the SFCB reinforcement and an uneven distribution of bond stress within the bonded section. Based on test results, a modified bond–slip model was utilized to predict the bond–slip relationship between the SFCB reinforcement and the geopolymer concrete, taking into account the influence of surface treatment on the descending branch of the bond–slip curve. Additionally, a damage model was employed to evaluate the extent of damage at the bond interface between the SFCB reinforcement and the geopolymer concrete under different load levels. Finally, the modified bond–slip model was also used to propose the anchorage length for SFCB reinforcements, which was compared with ACI, CSA, and JSCE codes.

Deformation monitoring and data denoising without clean data for sandbars protective flexible mattress in the Yangtze River

The stability of the shoreline, such as sandbars, is vital for navigation and flood control in the Yangtze River, and the flexible mattresses are widely used to protect shorelines and submerged structures from erosion. To determine the long-term stability of the sandbars, it can be achieved by analyzing the monitoring data of the protective flexible mattress deformation. These mattresses are deployed in challenging environments, where traditional monitoring methods fall short in capturing their subtle, short-term deformations. To address this gap, this study introduces a novel deformation monitoring method based on Distributed Fiber Optic Sensing (DFOS) tailored for flexible mattresses and proposes a BiLSTM-CNN model for denoising the measured data, which circumvents the need for clean reference data. Key findings include: (1) For the monitoring of flexible mattresses, armored optical sensing cables should be installed from the perimeter inward, arranged in a U-shape post-submersion on the surface of the flexible mattress, and installed simultaneously with construction. The strain is measured by the single-end BOTDR. (2) The proposed BiLSTM-CNN model which is established without clean data, outperformed traditional denoising techniques like wavelet transform and Kalman filtering, effectively preserving essential strain responses while suppressing noise, enabling accurate depiction of the deformation of the flexible mattress, and demonstrating its applicability in complex monitoring scenarios. (3) The flexible mattress monitoring method was successfully deployed in the Yudaizhou shoreline protection engineering project. The BiLSTM-CNN denoising model, combined with a post-adjust method to process the different periods’ data, resulted in analyzable strain data. Comprehensive analysis of long-term strain monitoring data indicates that the flexible mattress in the monitoring area remains stable. This study’s significant contribution lies in advancing the methodology for monitoring and denoising strain data from underwater flexible mattresses, thereby enhancing the understanding and management of erosion protection systems in challenging environments.

Shrinkage behaviour of high-strength concrete plates reinforced with carbon textile reinforcement

Carbon textile reinforcement is a relatively new type of CFRP reinforcement which, when utilized with high-strength concrete, leads to the production of ultra-thin free-form concrete shells. However, such reinforcement usually suffers from weak resistance to compressive stresses, while high-strength concrete is susceptible to high shrinkage rates. This combination may lead to a boor shrinkage behaviour, and thus an excessive shrinkage-related craking. Therefore, this paper aims to study the shrinkage behaviour of high-strength concrete plates reinforced with carbon textile reinforcement. Additionally, the utilization of shrinkage-compensating concrete as a possible shrinkage mitigation method was also investigated. For this aim, twelve concrete plates of dimensions 500 mm × 150 mm x 30 mm were cast. The shrinkage and expansion behaviour of the specimens were monitored for six months using the distributed fibre optic sensor (DFOS) technology, and then all the specimens were tested till failure under a direct tension test. The results have revealed that the existence of textile reinforcement does not provide any significant resistance to the negative strains generated from the shrinkage of concrete, which, for restrained concrete plates, leads to the formulation of wide cracks. On the other hand, the utilization of shrinkage-compensating concrete not only eliminates the formulation of shrinkage-related cracks but also reduces the crack’s width resulting from the loading process by up to 35 % during all stages of loading as illustrated by the fibre optic sensors measurements. Additionally, shrinkage-compensating concrete exhibits a slightly higher compressive strength than normal concrete.

Strain mode measurement of asphalt pavements using distributed fibre optic sensor data

This paper proposes a method for measuring strain modes in asphalt pavements using distributed fibre optic strain sensor data. A processing algorithm based on cross power spectral density (CPSD) was developed to utilize high spatial resolution strain data to determine the CPSD peak frequency and corresponding relative amplitude. The proposed method was applied in both laboratory and field settings, comparing the results of distributed fibre optic cables with those of accelerometers or theoretical solutions to validate accuracy. Local heating of asphalt structures was used to test the sensitivity of the measured strain modes to changes in material properties. The results demonstrate that different fibre optic cables can provide peak frequencies comparable to traditional accelerometers (±0.6 Hz). Additionally, they offer relative amplitude with a resolution of 1 cm, achieving a modal assurance criterion exceeding 0.95 when compared to theoretical strain mode shapes. The method also sensitively reflects material property changes at different positions within the asphalt structure. This indicates that the proposed approach can effectively measure strain modes in asphalt structures, offering a more detailed understanding of the distribution of structural properties compared to traditional methods.

Experimental and numerical investigations on the tensile response of pin-loaded carbon fibre reinforced polymer straps

Carbon fibre reinforced polymer (CFRP) pin-loaded looped straps are increasingly being used in a range of structural load-bearing applications, notably for bridge hanger cables in network arch rail and highway bridges. The static performance of such CFRP straps is investigated through experimental and numerical analyses. Finite element (FE) models based on both one-eighth and half pin-strap assembly geometries were modelled. The resulting strains, stresses, and applied loads were compared against experimental data obtained using Digital Image Correlation, Distributed Fibre Optic Sensing (DFOS), and Fibre Bragg Grating (FBG) Sensing. The FE models effectively captured local strain distributions around the vertex area, close to the pin ends of the straps, as well as in the mid-shaft region, and aligned reasonably with experimental observations. The half FE model accurately predicted the overall strain distribution when compared to DFOS data; however, higher strain magnitudes (by 0.45–10.2 %) and larger strain reductions were observed in some locations. Regarding failure loads, the FE models agreed well with Schürmann's analytical solution and the maximum stress criterion, exhibiting less than 2.5 % deviations from the experimental data. Furthermore, the predicted onset of strap failure (by delamination) in the half model agreed with experimental values, with a maximum variance of 9.2 %.

Geophysical Monitoring Technologies for the Entire Life Cycle of CO2 Geological Sequestration

Geophysical monitoring of CO2 geological sequestration represents a critical technology for ensuring the long-term safe storage of CO2 while verifying its characteristics and dynamic changes. Currently, the primary geophysical monitoring methods employed in CO2 geological sequestration include seismic, fiber optic, and logging technologies. Among these methods, seismic monitoring techniques encompass high-resolution P-Cable three-dimensional seismic systems, delayed vertical seismic profiling technology, and four-dimensional distributed acoustic sensing (DAS). These methods are utilized to monitor interlayer strain induced by CO2 injection, thereby indirectly determining the injection volume, distribution range, and potential diffusion pathways of the CO2 plume. In contrast, fiber optic monitoring primarily involves distributed fiber optic sensing (DFOS), which can be further classified into distributed acoustic sensing (DAS) and distributed temperature sensing (DTS). This technology serves to complement seismic monitoring in observing interlayer strain resulting from CO2 injection. The logging techniques utilized for monitoring CO2 geological sequestration include neutron logging methods, such as thermal neutron imaging and pulsed neutron gamma-ray spectroscopy, which are primarily employed to assess the sequestration volume and state of CO2 plumes within a reservoir. Seismic monitoring technology provides a broader monitoring scale (ranging from dozens of meters to kilometers), while logging techniques operate at centimeter to meter scales; however, their results can be significantly affected by the heterogeneity of a reservoir.

Distributed optical fiber sensors for real-time tracking of fouling buildup for tubular continuous polymerization reactors

Early online fouling detection is expected to be a real step forward in the operation of continuous tubular reactors. As an online technology, the Rayleigh backscatter based Distributed Optical Fiber Sensor (DOFS) technology was evaluated with respect to temperature measurement resolution, reproducibility and the best online calibration method. Different coatings and terminations were characterized for emulsion polymerization reactors. Commercially available sensors with acrylate primary coatings, dual acrylate coatings, and polyimide coatings were all compared with each other. It is shown that the presence of a secondary coating significantly alters sensor behaviour. Sensors with only a primary coating showed a temperature resolution of 0.1 °C. Online calibration for temperature readout was carried out and validation tested on segments of fiber integrated in a 3D-printed reactor channel (diameter 1.5 mm). Acrylate coated sensors showed a deviation of 20 % for the calibration coefficients when inside the reactor channel compared to the online calibration, leading to errors in temperature measurement. Polyimide coated sensors showed a deviation of just 0.6 % in this validation test, demonstrating good capabilities for online calibration with accurate temperature measurements. The possibility of spatial and temporal monitoring of fouling buildup through a heat exchanging wall equipped with DOFS was evaluated.

Temperature-insensitive and cost-effective distributed NP-Doped optical fiber sensors

This paper presents the development of a cost-effective distributed optical fiber sensor for temperature-insensitive assessment of mechanical disturbances along an optical fiber cable. The proposed sensor system uses a nanoparticle (NP)-doped optical fiber with enhanced Rayleigh backscattering to provide higher sensitivity and spatial resolution using the transmission and reflection analysis (TRA) approach, where the transmitted and backscattered optical powers are analyzed as a function of the mechanical disturbance. In addition, Fiber Bragg Gratings (FBGs) are used as wavelength filters to provide the wavelength division multiplexing of the proposed device, which enable the use of 3 different NP-doped optical fiber sections for simultaneous detection of multiple curvature conditions in a cost-effective distributed sensing approach. The sensor characterization tests are performed by means of applying curvature angles from 360° to 1080° at different positions along NP-doped fibers (namely 25 mm, 100 mm and 175 mm) at 4 different temperatures of 25 °C, 30 °C, 40 °C and 50 °C. The results indicate the feasibility of the proposed approach, where the temperature variations lead only to a wavelength shift of the Bragg wavelength, whereas the mechanical disturbances (the curvatures) lead only to variations in the transmitted and reflected optical powers. Thus, by analyzing the transmitted and reflected optical powers in conjunction with the Bragg wavelength shift, it is possible to estimate both the mechanical disturbance amplitude (i.e., curvature angle) and the position along each NP-doped optical fiber section. Results indicate a relative error of around 3 % and 4 % for the mechanical disturbance location and absolute value, respectively. Moreover, the temperature cross-sensitivity in this case is below 2 % considering both amplitude and location of the mechanical disturbance. The proposed approach can be applied in structural health monitoring of different types of structures by integrating the fibers in the structures themselves with the possibility of measuring the strain distribution along the fibers (instead of in different points along the fiber) using a lower cost hardware when compared with similar distributed optical fiber sensing approaches.

MSAACNN for intense noise suppression in DAS-VSP records

Distributed optical fiber sensor (DAS) is an emerging acquisition technique and has begun to be widely applied in seismic exploration owing to its advantages in acquisition and deployment. Nonetheless, DAS record has a low signal-to-noise ratio (SNR) due to the intense background noise. How to suppress the DAS background noise and increase the SNR of the DAS records has gradually become one of the hot issues in the field of seismic data processing. To solve the challenging tasks in intense DAS noise suppression, a multiscale sparse asymmetric attention convolutional neural network (MSAACNN) is proposed. The network uses dilated convolutions to expand the receptive field and map more feature information. Moreover, asymmetric convolutions are introduced to form an asymmetric unit, aiming to strengthen the feature extraction ability and realize the interaction between feature information of different scales. Finally, a pyramid attention module is used to enhance the primary features and improve the network denoising performance. The experimental results show that MSAACNN can effectively suppress the complex background noise in DAS records, compared with the traditional denoising methods and typical convolutional neural network (CNN) architecture. Additionally, the recovered signal components in the processing results are clear and complete, with significantly improved SNR.

Experimental Verification of GFRP Bridge Deck Panels Using an Integrated Distributed Fiber Optic Sensing System

Experimental Verification of GFRP Bridge Deck Panels Using an Integrated Distributed Fiber Optic Sensing System

Strain measurement of CFRP-Steel bonded joints using distributed optical fibre sensors

ABSTRACT Externally bonded carbon fibre reinforced polymer (CFRP) is widely employed for strengthening steel structures. The CFRP-steel bond behaviour is typically conducted using the single-lap joint (SLJ) and double-lap joint (DLJ) shear tests. This study utilizes advanced distributed optical fibre sensors (DOFS) to measure strains in CFRP-steel SLJs and DLJs, with a specific focus on examining the bending effect. An experimental investigation was conducted to verify the appropriate adhesive and fibre coating materials, achieving synergistic deformation between optical fibre sensors and adherends. The strain results reveal that PI-coated fibres bonded with epoxy resin enhance deformation compatibility. The CFRP strain distributions within the joints are accurately measured by DOFS, enabling the calculation of bending strain to assess joint bending effects. The observed strain aligns well with the finite element (FE) predictions. A more significant bending effect is observed in SLJs compared to DLJs, predominantly concentrated at the loaded end. It can be inferred that the SLJs are subjected to mixed-mode I/II loading in shear tests, and the DLJs appears to be mainly influenced by mode II loading. Additionally, the verified FE model demonstrates that the shear strain across the thickness of the adhesive layer exhibits significant variation at the bond ends.

Fundamental study on performances of fiber-superconducting composite CORC cable: electromagnetic, thermal, mechanical and quench behaviors

Abstract Common terminal voltage measurement can be hardly applied to detect quenches in long high temperature superconducting (HTS) conductor on round core (CORC) cable because not only the HTS normal zone propagation velocity (NZPV) is low but also immobilizing a large number of voltage leads is inconvenient. Distributed optical fiber sensing (DOFS) technology is a promising method for quench detection of long superconductors. To sense the thermal changes of CORC cables during quenches more directly and prevent optical fibers from damages by external stress, a fiber-superconducting composite (FSC) CORC cable was fabricated. For this cable, three optical fibers were placed in three grooves of the copper core respectively, then three HTS tapes were spirally wound on the copper core in sequence. Comparing to a traditional CORC cable, obviously, the FSC CORC cable structure has been changed. To promote FSC CORC cable engineering applications, it is necessary to study the fundamental performance of the cable. In this paper, we investigated the electromagnetic, thermal and mechanical properties of the FSC CORC cable by comparing those with a common one. The results demonstrated that, compared to the common one, the magnetic distribution of the FSC CORC cable hardly changed, but the current distribution of the copper core in the FSC CORC cable slightly changed which led to decreases of transport AC loss, in addition, the thermal characteristics of the FSC CORC cable was slightly changed and the bending tolerance ability of the cables reduced within a bending diameter range of 15 cm. What’s more, the embedded optical fibers combined with DOFS system are successfully used to detect the temperature changes of the cable surface. Finally, to study the quench behaviors of the cable, we built a quench detection platform, which equips with a voltage acquisition system, a thermocouple temperature acquisition system and the DOFS system. By using the platform to detect the quenches of the FSC CORC cable, minimum quench energy of the cable and NZPV of the tape and cable at different currents was tested.

Investigation of the Robust Integration of Distributed Fibre Optic Sensors in Structural Concrete Components.

In recent times, the value of data has grown. This tendency is also observeable in the construction industry, where research and digitalisation are increasingly oriented towards the collection, processing and analysis of different types of data. In addition to planning data, measurement data is a main focus. fibre optic measurements offer a highly precise and comprehensive approach to data collection. It is, however, important to note that this technology is still in research regarding concrete structures. This paper presents two methods of integrating filigree sensors into concrete structures. The first approach entails wrapping a fibre around a tendon duct and analysing the installation and associated measurements. The second method involves bonding polyimide and acrylate-coated fibres with 2K epoxy and cyanoacrylate in the grooves of rebars, exposing them to chemical environments. The resulting measurement data is evaluated qualitatively and quantitatively to ascertain its resilience to environmental factors. These developed criteria are consolidated in a decision matrix. Fibre-adhesive combinations necessitate protection from chemical and mechanical influences. The limitations of the solutions are pointed out, and alternative options are proposed.

Research and application of ice force monitoring technology for upright structures based on distributed fiber optic sensors

A ice force monitoring system based on fiber optic sensing technology is proposed to address the limitations of traditional ice force monitoring methods in harsh marine environments. The design requirements of the ice force sensor, the design calculations of the elastic element, and the selection and arrangement of fiber optic sensors are elaborated. The sensitivity coefficient of the designed sensor is determined to be 0.044 nm/KN, with an R2 value of 0.999. The performance and reliability of the sensor are validated through calibration tests and indoor loading experiments. Field measurements of ice force are conducted, with ice force amplitudes measured to be around 2KN. Additionally, Temporal Convolutional Network (TCN) is introduced for sequential prediction of extreme static ice force data, aiming to provide more comprehensive support for long-term safety assessment and risk warning of marine platforms.

Measuring strain distributions in an asphalt pavement using fibre optic sensors under static loading by test vehicles

Understanding the in-situ structural behaviour of pavements plays an important role in road engineering. Hence, various measurement methods were developed in the past to get an insight into the response of roads to traffic loading. In the present study, distributed fibre optic strain sensors were embedded into a hot mix asphalt pavement. The sensors were used for strain measurements with high spatial resolution in order to assess its feasibility to gain the continuous strain distribution in the pavement under short-term static loading. In addition, the loading process was measured with high temporal resolution, indicating that – using shorter sensor lengths – vehicle passages could also be studied. The strain distributions gathered with this type of sensor were compared to the results of a simple finite element model and the effect of applying various sensor cables. In addition, new opportunities provided by distributed fibre optic sensors in road engineering are discussed.

Mass Timber Beams Under Loading

This summer, I participated in a few research projects under the supervision of Dr. Woods, focusing on the load-bearing performance of mass timber structures. The projects aim to address critical gaps in understanding timber beam-column connections under loading to enhance structural safety and promote the use of sustainable building materials. I assisted in the construction and instrumentation of experimental test specimen setup (including retrofit beams from a decommissioned bridge, CLT-Steel composite beams, and curved glulam beams), executing the tests, and analyzing the resulting data. Advanced sensors like digital image correlation and distributed fiber optic sensors provided insights into the behaviour of beams. Through hands-on training in the Structures Laboratory and support in data analysis using MATLAB, I gained a deep understanding of experimental testing under loading and contributed to a collaborative effort in structural engineering.

Smart prediction of rock crack opening displacement from noisy data recorded by distributed fiber optic sensing

The commonly used method for estimating crack opening displacement (COD) is based on analytical models derived from strain transferring. However, when large background noise exists in distributed fiber optic sensing (DFOS) data, estimating COD through an analytical model is very difficult even if the DFOS data have been denoised. To address this challenge, this study proposes a machine learning (ML)-based methodology to complete rock’s COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models. The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models. To ensure that the best hyper-parameters will not be missing, the configuration space in Hyperopt is specified by probability distribution. The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness. The proposed models are compared each other in terms of goodness of fit and mean squared error. The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.

Signal recognition based on transfer learning for Φ-OTDR fiber optic distributed disturbance sensor

Abstract Traditional classification models for optical fiber vibration signals can achieve good recognition results when there are a large number of samples. However, it is difficult to obtain sufficient training samples in real scenes. A method of Φ-OTDR sensor signal recognition based on VGGish transfer learning is proposed in this paper. Firstly, a selection indicator is designed to filter out vibration signals from sparse signals. Then, the VGGish network pretrained for sound classification is used for transfer learning as a feature extractor for optical fiber vibration signals. This method was applied to classify 6 kinds of optical fiber vibration signals. When the number of training samples is reduced to 480 samples, the method can still achieve 84.17% classification accuracy. Compared to the method of training from scratch, this proposed method reduces training time by 73.9%.

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.