The Strain Transfer Mechanism of Fiber Bragg Grating Sensor for Extra Large Strain Monitoring

Fiber Bragg grating sensing system for temperature measurements based on optically injected DFB-LD with an OEO loop

A novel fiber Bragg grating (FBG) sensing system, based on an optically injected distributed feedback laser diode (DFB-LD) with an optoelectronic oscillating (OEO) loop, is proposed and experimentally demonstrated for temperature measurements with high and tunable sensitivity. The FBG sensor device works as an edge filter to adjust the optical power of the injected beam in response to temperature variations. The optically injected DFB-LD works at Period-one (P1) oscillating state, and the central wavelength of the oscillating mode of the DFB-LD can be tuned by the variable power of the injected beam. Furthermore, an OEO loop is implemented to improve the signal quality of the generated P1 microwave signal. Hence, the sensing parameter of temperature is converted to the frequency variation of the generated P1 microwave signal in the proposed sensing system. In the proof-of-concept experiment, a series of P1 microwave signals are generated while different temperatures are applied to the FBG sensor. The sensitivity of the proposed FBG sensing system for temperature measurements can be tuned from 0.44322 GHz/°C to 1.25952 GHz/°C. The stability and repeatability experiments are also performed, demonstrating the high measurement accuracy (0.0629°C) and low error of the system. The proposed FBG-based sensing and interrogation system exhibits high sensitivity, large tunability, good linearity, and flexible sensing generality.

Investigating the Mechanical Properties and Temperature Compensation of a Spot-Welded Strain Sensor within an Intelligent Steel Strand Cable

According to current regulations, welding is strictly prohibited for prestressed and tension cables. In response, this article proposes the use of a portable spot-welding machine to spot weld steel strands. This method generates a small current during spot welding, with a voltage of only 3 V to 5 V, and does not damage the internal structure of the steel strand. To effectively monitor cable tension in cable-supported structures, a novel approach utilizing a chip-based, encapsulated spot-welded strain sensor was investigated. The strain sensing capability, temperature sensitivity, stress relaxation, and static load responses were investigated on the proposed smart steel strand cables with spot-welded strain sensors. The theoretical analyses and finite element simulations revealed that the strain transfer efficiency of the spot-welded strain sensor exceeded 96%. The experimental results demonstrated that the load-strain relationship of the smart steel strand cable had a fitting degree greater than 0.999, and the tension errors obtained under different loads were within 1.26%. The tension full capacity errors measured at different temperatures were generally within 1.0%. The relaxation rate of the smart steel strand cable after 120 h was 3.78% and reduced the sensor accuracy error by 3.97%. Thus, the proposed strain sensor equipped with a smart steel strand cable is suitable for use in long-term tension monitoring.

In the Direction of an Artificial Intelligence-Enabled Monitoring Platform for Concrete Structures

In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to generate early warnings useful for decision-making purposes. With a perspective of developing a remote monitoring platform for the built environment in a seismic context, the authors tested self-sensing concrete beams in loading tests, focusing on the measured electrical impedance. The formed cracks were objectively assessed through a vision-based system. Also, a comparative analysis of AI-based and statistical prediction methods, including Prophet, ARIMA, and SARIMAX, was conducted for predicting electrical impedance. Results show that the real part of electrical impedance is highly correlated with the applied load (Pearson's correlation coefficient > 0.9); hence, the piezoresistive ability of the manufactured specimens has been confirmed. Concerning prediction methods, the superiority of the Prophet model over statistical techniques was demonstrated (Mean Absolute Percentage Error, MAPE < 1.00%). Thus, the exploitation of electrical impedance sensors, vision-based systems, and AI technologies can be significant to enhance SHM and maintenance needs prediction in the built environment.

Force-Displacement Analysis in Diaphragm-Embedded Fiber Bragg Grating Sensors

This paper presented the force and displacement analyses of a diaphragm-embedded fiber Bragg grating (FBG) sensor. In the first step, a numerical analysis (via finite element method) was performed considering linear elastic materials, where there is a linear variation on the strain in the optical fiber for both displacement and force (or pressure). In the second step, the experimental analysis was performed using two approaches: (i) controlling the displacement applied in the diaphragm-embedded FBG (while the force is also measured). (ii) Controlling the force applied in the sensor (also with the measurement of the displacement). Results showed reflected optical power variations and wavelength shift following the application of displacement and force. The sensitivities of both wavelength shift and optical power were different (and non-proportional) when displacement and force were compared. However, a higher correlation, determination coefficient (R²) of 0.998, was obtained in the analysis of the wavelength shift as a function of the displacement, which indicated that the strain transmission in the optical fiber is directly related to the strain in the diaphragm, whereas the force has an indirect relation with the strain and depends on the material features. Then, the possibility of simultaneous estimation of force and displacement was investigated, where the linear relation of both parameters (displacement and force) with the wavelength shift and the optical power were obtained in a limited range of displacement and force. In this range, root mean squared errors of 0.37 N and 0.05 mm were obtained for force and displacement, respectively. In addition, the force variation with a step displacement input also shows the possibility of using the proposed FBG device for the characterization of the materials' viscoelastic features such as phase delay, creep, and stress relaxation, which can be employed for in situ characterization of different viscoelastic materials.

Application of FBG Sensor to Safety Monitoring of Mine Shaft Lining Structure

The use of fiber Bragg grating (FBG) sensors is proposed to solve the technical problem of poor sensor stability in the long-term safety monitoring of shaft lining structures. The auxiliary shaft of the Zhuxianzhuang coal mine was considered as the engineering background, and a test system implementing FBG sensors was established to monitor the long-term safety of the shaft lining structure. Indoor simulation testing revealed that the coefficient of determination (r2) between the test curves of the FBG sensor and the resistance strain gauge is greater than 0.99 in both the transverse and vertical strains. Therefore, the FBG sensor and resistance strain gauge test values are similar, and the error is small. The early warning value was obtained by calculation, according to the specific engineering geological conditions and shaft lining structure. The monitoring data obtained for the shaft lining at three test levels over more than three years reveal that the measured vertical strain value is less than the warning value, indicating that the shaft lining structure is currently in a safe state. The analysis of the monitoring data reveals that the vertical strain increment caused by the vertical additional force is approximately 0.0752 με/d. As the mine drainage progresses, the increasing vertical additional force acting on the shaft lining will compromise the safety of the shaft lining structure. Therefore, the monitoring must be enhanced to facilitate decision-making for safe shaft operation.

Polarization Maintaining Fiber Temperature and Stress Gradient Sensitization Sensor Based on Semiconductor-Metal-Polymer Three-Layer Film Coating

Increasing sensitivity, measuring points, and stability have always been the pursuit of sensors. ZnSe₉:CO₁ and Ag composite nano films were coated on polarization maintaining fiber (PMF). Then, the coated PMF was nested in capillary and hose which was encapsulated with polydimethylsiloxane (PDMS) and epoxy resin. The integrated capillary sensor and thermoplastic hose sensor were prepared. The gradient sensitization of various measurement parameters such as temperature, stress, and micro bending is realized. The temperature sensitivity is 1.49 nm/°C, the micro bending sensitivity is 1.72 nm/10² g, and the stress sensitivity is 6.27 nm/mε. The sensors maintain good linearity and instantaneous response while having high sensitivity. By adjusting the length of PMF, the number of troughs is increased in the same band range, and different troughs have different sensitivities, which solves the inherent problem of cross sensitivity and realizes multiparameter measurement. Capillary sensors are used for remote safe real-time monitoring of mechanical overheating, and hose sensors are used for real-time monitoring of bridge load and human joint bending. This work is of great significance to the extension of the application range of optical fiber sensor.

Geometrical shapes effects of optical cavity on entanglement dynamics of optomechanical systems: A numerical analysis

In this work, a model of optomechanical system was investigated by analyzing the entanglement dynamics of two related mechanical oscillators in a modified system. Geometrical shapes effects of optical cavities on entanglement of a representative optomechanical system were investigated by means of performing numerical analysis. It was signified that the steady-state or the dynamic behavior of optomechanical engagement could be created owing to the strength of mechanical pairs, which are strong towards the oscillating temperature. In addition, the mentioned entanglement dynamics were seen to be entirely related to the natural state’s stability. Furthermore, rendering the mechanical damping effects, the critical mechanical coupling strength-related analytical expression, where the transition from a steady state to a dynamic clamp occurs, was reported. In the studied system, two identical mechanical oscillators were formed in different conditions of the optical cavities shapes.

Review on Sensing Technology Adoption in the Construction Industry

Sensing technologies demonstrate promising potential in providing the construction industry with a safe, productive, and high-quality process. The majority of sensing technologies in the construction research area have been focused on construction automation research in prefabrication, on-site operation, and logistics. However, most of these technologies are either not implemented in real construction projects or are at the very early stages in practice. The corresponding applications are far behind, even in extensively researched aspects such as Radio Frequency Identification, ultra-wideband technology, and Fiber Optic Sensing technology. This review systematically investigates the current status of sensing technologies in construction from 187 articles and explores the reasons responsible for their slow adoption from 69 articles. First, this paper identifies common sensing technologies and investigates their implementation extent. Second, contributions and limitations of sensing technologies are elaborated to understand the current status. Third, key factors influencing the adoption of sensing technologies are extracted from construction stakeholders' experience. Demand towards sensing technologies, benefits and suitability of them, and barriers to their adoption are reviewed. Lastly, the governance framework is determined as the research tendency facilitating sensing technologies adoption. This paper provides a theoretical basis for the governance framework development. It will promote the sensing technologies adoption and improve construction performance including safety, productivity, and quality.

Two transition complexes based on 1H-benzimidazole-5,6-dicarboxylic acid: Synthesis, structure and photocatalytic degradation of dyes

Metal-organic frameworks [Co(Hbidc)(H2O)2] (1) and [Mn(Hbidc)(H2O)] (2), with multidentate 1H-benzimidazole-5,6-dicarboxylic acid (H3bidc) ligand, have been synthesized under hydro/solvothermal conditions and structurally characterized by elemental analysis, IR spectrum, and single-crystal X-ray diffraction. Single-crystal X-ray diffraction analysis revealed that the center Co atom of complex 1 is six-coordinated with three-dimensional supramolecular structure and center Mn of complex 2 is five-coordinated with exhibiting a 2D layered network. The photodegradation of Crystal violet dye and Methylene blue dye were studied firstly by complexes 1 and 2 as photocatalysts. Research result indicates that the degradation rate for complex 1 can reach 89.85% , 90.6% and that for complex 2 can reach 88.28% , 79.48% . At the same time, corresponding to photocatalytic kinetics was performed.

Generative Design in Building Information Modelling (BIM): Approaches and Requirements

The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers’ future studies.

An Automated Pipeline for Dynamic Detection of Sub-Surface Metal Loss Defects across Cold Thermography Images

Utilising cooling stimulation as a thermal excitation means has demonstrated profound capabilities of detecting sub-surface metal loss using thermography. Previously, a prototype mechanism was introduced which accommodates a thermal camera and cooling source and operates in a reciprocating motion scanning the test piece while cold stimulation is in operation. Immediately after that, the camera registers the thermal evolution. However, thermal reflections, non-uniform stimulation and lateral heat diffusions will remain as undesirable phenomena preventing the effective observation of sub-surface defects. This becomes more challenging when there is no prior knowledge of the non-defective area in order to effectively distinguish between defective and non-defective areas. In this work, the previously automated acquisition and processing pipeline is re-designed and optimised for two purposes: 1-Through the previous work, the mentioned pipeline was used to analyse a specific area of the test piece surface in order to reconstruct the reference area and identify defects. In order to expand the application of this device over the entire test area, regardless of its extension, the pipeline is improved in which the final surface image is reconstructed by taking into account multiple segments of the test surface. The previously introduced pre-processing method of Dynamic Reference Reconstruction (DRR) is enhanced by using a more rigorous thresholding procedure. Principal Component Analysis (PCA) is then used in order for feature (DRR images) reduction. 2-The results of PCA on multiple segment images of the test surface revealed different ranges of intensities across each segment image. This potentially could cause mistaken interpretation of the defective and non-defective areas. An automated segmentation method based on Gaussian Mixture Model (GMM) is used to assist the expert user in more effective detection of the defective areas when the non-defective areas are uniformly characterised as background. The final results of GMM have shown not only the capability of accurately detecting subsurface metal loss as low as 37.5% but also the successful detection of defects that were either unidentifiable or invisible in either the original thermal images or their PCA transformed results.

Thermodynamic stability, structural and electronic properties for the C20-nAln heterofullerenes (n = 1-5): a DFT study

DFT calculations are utilized to compare and contrast the substituted aluminum-heterofullerenes, C_20-nAl_n (with n = 1-5) from thermodynamically view point, at density functional theory (DFT). Vibrational frequency analysis confirms that apart from C₁₅Al₅, all studied species are true minima. Considering the optimized geometries shows that all heterofullerenes are isolated-pentagon cage and none collapse to open deformed as segregated structure. The highest binding energy (5.56 eV/atom) and absolute heat of atomization (3323.68 kcal mol^-1) reveals open-shell C₁₉Al₁ as the most stable thermodynamic heterofullerene. The most NICS (0) (isotropic and anisotropic parameters, -49.58 and - 46.47 ppm, respectively) introduces closed-shell C₁₈Al_2-2 as the most aromatic structure. Also, closed-shell C₁₆Al_4-1 heterofullerene emerges with the most polarizability (307.71 a.u.) and hence activity to interact with the surrounding polar species. The lowest and the highest charge transfer on the surfaces of C₂₀ and C₁₆Al_4-2 without weak Al-Al bond, as the worst and the best candidate, respectively, provokes further investigation on impossible and possible application for hydrogen storage, respectively. We wish that the present survey will stimulate new experiments.

Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being.

Strain Transfer Characteristics of Multi-Layer Optical Fiber Sensors with Temperature-Dependent Properties at Low Temperature

Optical fiber sensors have been potentially expected to apply in the extreme environment for their advantages of measurement in a large temperature range. The packaging measure which makes the strain sensing fiber survive in these harsh conditions will commonly introduce inevitable strain transfer errors. In this paper, the strain transfer characteristics of a multi-layer optical fiber sensing structure working at cryogenic environment with temperature gradients have been investigated theoretically. A generalized three-layer shear lag model incorporating with temperature-dependent properties of layers was developed. The strain transfer relationship between the optical fiber core and the matrix has been derived in form of a second-order ordinary differential equation (ODE) with variable coefficients, where the Young’s modulus and the coefficients of thermal expansion (CTE) are considered as functions of temperature. The strain transfer characteristics of the optical sensing structure were captured by solving the ODE boundary problems for cryogenic temperature loads. Case studies of the cooling process from room temperature to some certain low temperatures and gradient temperature loads for different low-temperature zones were addressed. The results showed that different temperature load configurations cause different strain transfer error features which can be described by the proposed model. The protective layer always plays a main role, and the optimization geometrical parameters should be carefully designed. To verify the theoretical predictions, an experiment study on the thermal strain measurement of an aluminum bar with optical fiber sensors was conducted. LUNA ODiSI 6100 integrator was used to measure the Rayleigh backscattering spectra shift of the optical fiber at a uniform temperature and a gradient temperature under liquid nitrogen temperature zone, and a reasonable agreement with the theory was presented.

Uniaxial Compressive Behavior of Concrete Columns Confined with Superelastic Shape Memory Alloy Wires

Superelastic shape memory alloy (SMA) exhibits the ability to undergo large deformations before reverting back to its undeformed shape following the removal of the load. This unique property underlies its great potential in the seismic design and retrofitting of structure members. In this paper, superelastic SMA wires were utilized to confine concrete cylinders to enhance their axial compressive behavior. The axial carrying and deformation capacities of SMA-confined concrete cylinders are assessed by uniaxial compression testing on a total of eight SMA-confined concrete columns and one unconfined column. The influence of the amount of SMA and the prestrain level of SMA wires, as well as the reinforcing mode, on the axial carrying and deformation capacity of confined concrete columns were considered. The analysis focuses on the axial carrying capacity and deformation performance of concrete columns reinforced with superelastic SMA under different loading conditions. Based on the experimental data and analysis results, it is found that superelastic SMA wires can increase the axial loading capacity and enhance deformation performance of concrete columns. Under the same loading condition, the ultimate bearing capacity of SMA-confined concrete columns increases as the increasing of the amount of SMA wire. The results of this study verify the effectiveness of superelastic SMA in enhancing the loading capacity and deformation behavior of concrete cylinders.