LCR Meter Research Articles

SAFETY ASSESSMENT OF CONTINUOUS CONCRETE GIRDER BRIDGES SUBJECTED TO RANDOM TRAFFIC LOADS CONSIDERING FLEXURAL-SHEAR COUPLED FAILURE

Bridges generally perform complicated mechanical behaviors under external loads, such as flexural-shear coupling, compression-bending coupling, and flexural-shear-torsion coupling. In the context of deterministic design approaches such as design codes, these complicated coupled issues are generally simplified to the safety verification of bridge components under a single mechanical state (i.e. flexural, shear, torsion). At present, the rapid development of sensor and information technologies makes it possible to collect the external loads acted on bridges and understand bridge performance under these stochastic external loads. In this manner, the reliability-based full probabilistic approach could be applied to investigate the performance of bridges over their lifetime. However, the current bridge reliability assessment incorporating realistic traffic load measurements mainly focuses on the analysis of bridge components under a single mechanical state. In this paper, a reliability-based probabilistic analytical framework of the flexural-shear performance of girder bridges under random traffic loading is established. The flexural-shear coupled failure path of bridge girders under random traffic loading is characterized for the first time, where the bivariate extreme value theory is incorporated to develop the extreme value distribution of combined flexural and shear load effects. The modified compression field theory recommended by AASHTO is employed to establish the coupled flexural-shear coupling resistances. Finally, the reliability of the flexural-shear performance of bridge girders is evaluated by solving the multivariate ultimate limit state equation. The proposed analytical framework is applied to a realistic bridge. The results show that the reliability index of the flexural-shear coupling evaluation is lower than that of the flexural or shear evaluation, which highlights the importance of the flexural-shear performance checking in the reliability assessment of bridges under random traffic loading. The proposed analytical framework could be further applied to the probabilistic assessment of bridge components subjected to combined loading mechanisms under random loadings.

Hybrid nanofillers loaded epoxy resin; Synthesis, characterizations, and dielectric spectroscopy

In the current work, synthesis, structural, magnetic, and dielectric characterization of nanofillers doped epoxy composites were studied deeply. Bisphenol A-(epichlorohydrin): epoxy and hardener N(3-dimethylaminopropyl)-1,3-propylene diamine was used to prepare epoxy composite and nano epoxy composites. (CoFe2O4 + Fe3O4), and (NiFe2O4 + CoFe2O4) hybrid nanofillers were used as nanofillers. The structural information of neat epoxy and nano-epoxy composites was obtained by XRD. To understand the actual chemical changes, the nature of bonding and interaction mechanism between the nano-particles and the neat epoxy; the structural chemistry and surface functionality of nano-epoxy composites were examined through Fourier transform infrared spectroscopy (FTIR). Moreover, vibrational sample magnetometer (VSM) measurements were done to gain additive information about the magnetic properties of nanoparticle-loaded epoxy composites. Dielectric spectroscopy measurements of nano epoxy composites were carried out using a precision LCR meter in the frequency range of 1 kHz to 2 MHz and with the help of a vector network analyzer along with SPEAG dielectric assessment kit for thin layers over the frequency range of 200 MHz to 20 GHz at room temperature. The influence of nano-fillers on the dielectric properties of neat epoxy composites is discussed thoroughly.

A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation.

The morphological characteristics of a crack serve as crucial indicators for rating the condition of the concrete bridge components. Previous studies have predominantly employed deep learning techniques for pixel-level crack detection, while occasionally incorporating monocular devices to quantify the crack dimensions. However, the practical implementation of such methods with the assistance of robots or unmanned aerial vehicles (UAVs) is severely hindered due to their restrictions in frontal image acquisition at known distances. To explore a non-contact inspection approach with enhanced flexibility, efficiency and accuracy, a binocular stereo vision-based method incorporating full convolutional network (FCN) is proposed for detecting and measuring cracks. Firstly, our FCN leverages the benefits of the encoder-decoder architecture to enable precise crack segmentation while simultaneously emphasizing edge details at a rate of approximately four pictures per second in a database that is dominated by complex background cracks. The training results demonstrate a precision of 83.85%, a recall of 85.74% and an F1 score of 84.14%. Secondly, the utilization of binocular stereo vision improves the shooting flexibility and streamlines the image acquisition process. Furthermore, the introduction of a central projection scheme achieves reliable three-dimensional (3D) reconstruction of the crack morphology, effectively avoiding mismatches between the two views and providing more comprehensive dimensional depiction for cracks. An experimental test is also conducted on cracked concrete specimens, where the relative measurement error in crack width ranges from -3.9% to 36.0%, indicating the practical feasibility of our proposed method.

Inductance-based and inductance-capacitance-based structural self-sensing of shape-changing deformation in aluminum

Structural self-sensing refers to a structural material sensing itself without sensor incorporation. Resistance-based self-sensing has long been studied. This work addresses inductance-based self-sensing and inductance-capacitance-based self-sensing. In particular, it provides the first report of inductance-capacitance-based self-sensing. A change in the inductance causes a change in the capacitance. This is to be distinguished from the capacitance change resulting from permittivity change. Permanent mechanical deformation with shape change (such as that associated with bending or undulation, as opposed to tensile strain) increases the inductance, in accordance with Faraday’s Law, thus decreasing the capacitance, as shown in this work for aluminum subjected to undulating bending. Conductivity is required for the inductance. The inductance capacitance is measured with the presence of a dielectric film (a double-sided adhesive tape) between the specimen and the aluminum foil electrode, as needed because an LCR meter is not designed for measuring the capacitance of a conductor. The specimen is subjected to undulating bending for different numbers of bends and different radii of curvature. The inductance and resistivity, as measured by direct clipping without the dielectric film, increase monotonically with the number of bends, while the inductance capacitance decreases monotonically. For radius of curvature 12.24 mm and 7.00 mm, the highest fractional increase in inductance (52% and 110%, respectively) is greater than the highest fractional increase in resistivity (2.2% and 7.3%, respectively) and the highest fractional decrease in inductance capacitance (2.04% and 2.4%, respectively). The sensing is superior for a smaller radius of curvature, whether the sensing is by measuring the inductance, resistance or capacitance. The inductance is superior to the resistivity or inductance capacitance for sensing bending.

Structural behavior of skewed prestress concrete superstructure

The construction of span-skewed bridges has become widespread worldwide and in Turkey. This article focuses on the structural behavior of simply supported prestressed concrete bridges with varying skewness angles. Specifically, supported I girder pretensioned prestressed concrete bridges are investigated using different models (five models) with skewness angles 0° - 21°- 37.6°- 57°–66.6°. These models are analyzed using the Midas Civil software to analyze and design various bridge components and types through the finite element method. This method employs the most commonly accepted codes worldwide. This research showed that shear forces and bending moments were significantly affected by increasing the degree of skewness angle when it exceeds 37.5°. External I-girders behavior was similar yet inversely proportional behavior. Internal I-girder behavior was also identical but inversely, too.

An advanced assessment framework for seismic resilience of railway continuous girder bridge with multiple spans considering 72h golden rescue requirements

Given the common sense that people in danger could survive with high probability in the golden rescue time of 72 h after earthquakes, railway bridges that are important transportation hubs and the lifelines for earthquake relief have been required to satisfy the necessities of safe passage and fast traffic for emergency response personnel in this critical period. To ensure the post-earthquake temporary functionality of the railway bridges to a sufficient safety level in golden rescue scenario, an advanced resilience assessment framework, including seismic resilience determinations for individual bridge components based on residual responses with respect to the plastic capacities originated from performance limit state definitions, definitions of three recovery levels considering repair efficiency for different recovery phases, and the series-parallel and parallel-serial system resilience models developed in temporal and spatial domains, was established. A commonly constructed irregular railway multi-span continuous girder bridge was chosen to verify the practicability of the aforementioned framework. The finite-element model of the illustrative bridge was developed taking abutment-girder interaction effects and nonlinear seismic behaviors of girder into account. Average spectral acceleration was taken as the intensity measure (IM) to consider the response variations originated from higher modes and natural period extension during earthquakes, and the site-matched and IM-based scaled ground records were adopted in the multiple stripe analysis to obtain instantaneous peak and residual seismic responses. Based on the framework, post-event resilience characteristics of bridge components were discussed, four recovery strategies were proposed for bridge system, and the seismic resilience of the illustrated bridge was assessed at system level. The research shows that the response contribution of fundamental dominant mode is only 64.37 %, indicating the dynamic response contributions from higher modes are considerable and the case bridge can show a strong irregularity during seismic events. Obvious nonlinear seismic behaviors occur to the superstructure where material strain in girder critical sections can increase to 0.00422 during earthquakes, which illustrates the inaccuracy of girder modeling only taking elastic behavior into consideration. Also, the prestress loss after earthquakes can reach 31.14 % and attention should be paid to the prestress recovery in subsequent repair works. Most components can satisfy the easy recovery resilience level, and the resilience is closely related to ground intensities and repair-waiting time; earthquake-induced functionality reduction can be 46.57 %, and resilience can increase by 25.35 % if the waiting time is shortened by 5 h. System resilience is influenced by the similar factors with component and varies in cases of different repair sequences due to the accumulation effect of repair-waiting time for various subsystems.

Microwave Assisted Sintering of Sr-doped Zinc Titanate (Sr0.2Zn0.8TiO3) Nano-ceramics

Sr0.2Zn0.8TiO3 (SZT) ternary nanopowders of perovskite structure samples were prepared by conventional solid state reaction technique using ultra-pure metal oxide powders. SrCO3, ZnO and TiO2 powders were used as a source of Strontium, Zinc, and Titanium. The calcination temperature of the samples is 820 °C and sintered at different temperatures ranging from 850 °C to 1000 °C in conventional and microwave furnaces for 2 hours and 30 min., respectively. The synthesized nano-powders were characterized using XRD, SEM, XPS and their dielectric properties were studied by temperature dependent LCR meter. The dielectric constant increases with an increase in sintering temperature until 950 °C and it decreases with a further increase of frequency.

Anti-cancer Schiff bases as photostabilizer for poly(vinyl chloride)

To prevent or reduce the drawback effect and prolong the life of PVC and preserve its composition and efficiency, compounds are incorporated with PVC. In this work, three highly aromatic anti-cancer Schiff base compounds in symbol (C2, C3 and C5) were applied to photostabilize of PVC against photodegradation, by added it to the polymer in the form of Schiff bases, to increase the stability of polymer when irradiated with ultraviolet (UV) light, (λmax = 365 nm) at 30 ºC, with the intensity of the light wavelength at 1920 lux for a long duration. The PVC was blended with Schiff's base in concentration (0.5 %wt.), with thickness films (40 μm) were made from the blended materials. Several methods were used, including infrared spectroscopy, surface examination of irradiated films to know the effect of additives on PVC photostabilization and conductivity of films were measured by LCR meter. The results showed that the additives significantly reduced the photodegradation of PVC. C2 compound as additive, stabilize PVC more than other additives, it can absorb harmful radiation, deactivate hydrogen chloride, and scavenge high-energy species such as peroxides. Several mechanisms of photostabilization for C2 compound have been suggested. Therefore it is acting as stabilizers for PVC.
 KEY WORDS: PVC, Photostabilizer, UV radiation, AFM, FESEM, LCR-meter
 Bull. Chem. Soc. Ethiop. 2024, 38(1), 135-146. DOI: https://dx.doi.org/10.4314/bcse.v38i1.11

Optimization of physical and dielectric properties of Co-doped ZnO nanoparticles for low-frequency devices.

In this study, zinc-oxide (ZnO) nanoparticles (NPs) doped with cobalt (Co) were synthesized using a simple coprecipitation technique. The concentration of Co was varied to investigate its effect on the structural, morphological, optical, and dielectric properties of the NPs. X-ray diffraction (XRD) analysis confirmed the hexagonal wurtzite structure of both undoped and Co-doped ZnO-NPs. Scanning electron microscopy (SEM) was used to examine the morphology of the synthesized NPs, while energy-dispersive X-ray spectroscopy (EDX) was used to verify their purity. The band gap of the NPs was evaluated using UV-visible spectroscopy, which revealed a decrease in the energy gap as the concentration of Co2+ increased in the ZnO matrix. The dielectric constants and AC conductivity of the NPs were measured using an LCR meter. The dielectric constant of the Co-doped ZnO-NPs continuously increased from 4.0 × 10-9 to 2.25 × 10-8, while the dielectric loss decreased from 4.0 × 10-8 to 1.7 × 10-7 as the Co content increased from 0.01 to 0.07%. The a.c. conductivity also increased with increasing applied frequency. The findings suggest that the synthesized Co-doped ZnO-NPs possess enhanced dielectric properties and reduced energy gap, making them promising candidates for low-frequency devices such as UV photodetectors, optoelectronics, and spintronics applications. The use of a cost-effective and scalable synthesis method, coupled with detailed material characterization, makes this work significant in the field of nanomaterials and device engineering.

Co-precipitation method followed by ultrafast sonochemical synthesis of aluminium doped M type BaFe11.4-xAlxCo0.6O19 hexaferrites for various applications

The co-precipitation method followed by Sonochemical process was used to synthesize M-type BaFe11.4-xAlxCo0.6O19 hexaferrites (x = 0.0, 0.4, 0.8, 1.2, and 1.6) with Al3+ substitutions. XRD, FTIR, UV–vis spectroscopy, VSM, SEM and LCR meter are used for the characterizations of the synthesized materials. The optical properties, magnetic properties, structural, morphological, and dielectric properties had been systematically investigated. XRD data and cell refinement study confirmed the hexagonal crystal structure of the synthesized materials that belong to space-group of P63/MMC-(No 194). The values of lattice parameters ‘a’ and ‘c’ have been reduced from their original value 5.9145 Åto 5.8446 Åand 23.239 Åto 23.1684 Åcorrespondingly with doping content. The values of ratio c/a are found within 3.9292 to 3.9641 range, which can be regarded as optimal values for M-type hexaferrites structure as mentioned in previous studies. The crystallite size-(D) and volume-(V) decreases with doping concentration. The band gap of the synthesized materials had shown decreasing trend with doping content. The values of dissipation factor, dielectric loss ε'', and dielectric constant ε', are higher at lower frequency but get smaller as frequency increases. The dissipation factor, dielectric loss ε'', and dielectric constant ε', reduced as the concentration of aluminium is increased. From the analysis of M−H loops it has been concluded that saturation magnetization and remnant magnetization is decreasing with aluminium content, while coercivity is increasing. The anisotropy parameter (B) and anisotropy field (Ha) had shown increasing trend with aluminium concentration. FTIR analysis showed the development of barium hexaferrites in two main absorption-bands, the enormous frequency-band and the low frequency-band. The position of these bands were moved towards a lower frequency range with increasing Al substitution level. The reduction in band gap as well as the enhancement in magnetic and dielectric properties of barium M type hexa-ferrites as a result of aluminium doping making them suitable for various applications, including magnetic storage media, magnetic sensors, microwave devices, electromagnetic wave absorbers and permanent magnets etc.

Effects of Deck-Abutment Pounding on the Seismic Fragility Curves of Box-Girder Highway Bridges

ABSTRACT Earthquake-induced pounding in bridges is a complex contact phenomenon in which the dynamic responses of structures, including collisions between deck and abutments, are strongly related to structural properties and earthquake excitation. The goal of this study is to develop and compare the seismic fragility curves of overall system and individual components of regular and irregular box-girder highway bridges in two cases: with pounding and without pounding. For this purpose, four levels of altitudinal irregularity, ranging from regular to highly irregular, are considered. To extend the results for all bridges in the same class, different sources of uncertainties related to earthquakes, structural geometries, and material properties are taken into account. The analytical fragility curves have been developed based on nonlinear time history analyses in OpenSees finite element software for the cases with and without pounding effects. The process has been repeated for each two-, three-, and four-span classes at four irregularity levels. The fragility function parameters for the two cases with and without pounding have been compared for all classes considered in this study. Using fragility functions, this paper clarifies the interactive roles of irregularity and pounding between deck and abutments for seismic vulnerability of multi-span box-birder highway bridges. The results indicate that collisions often show an adverse effect on all structural components. It has also been observed that the detrimental effect of pounding on seismic fragility is more apparent in irregular bridges when compared with regular ones. In addition, the study introduces a conversion coefficient to clarify the effects of pounding on the fragility of bridge components and the overall system. This coefficient can be applied in both conventional analytical methods like static or simplified analysis and technical earthquake models like HAZUS, adjusting fragility values for pounding and irregularity effects.

Fabrication of molecularly imprinted polymer‐based interdigital sensor for L‐ascorbic acid

The level of L‐ascorbic acid (AA) in various natural and artificial foods, medicines and other substances must be determined for biological and agricultural purposes. In this study, an AA‐imprinted poly (methacrylic acid)‐based receptor was synthesized by thermal free‐radical bulk polymerization for the detection of ascorbic acid using the methacrylic acid as monomer, ethylene glycol dimethacrylate (EGDMA) as cross‐linker, and azobisisobutyronitrile (AIBN) as initiator, in the presence of a porogenic solvent dimethyl sulfoxide (DMSO). The synthesized molecularly imprinted polymer (MIP) was used as a receptor. Immobilization of the receptor layer on IDEs provided a suitable sensor for AA detection by measuring changes in electrical parameters including inductance, capacitance, and resistance with the help of LCR meter. In the series and parallel inductance, the lowest detection limits were 0.13 and 0.001 ppm, respectively. While, for series and parallel capacitance, the lowest detection limits were 0.01548 and 1.3698 ppm, respectively. In the case of resistance, the lowest limit of detection was 0.0076 and 0.08121 ppm in series and parallel, respectively. The imprinted polymer‐based sensor showed sensitivity, selectivity, and reversibility for ascorbic acid.

Hanger replacement and corrosion assessment in a suspension bridge

Due to repetitive harsh environments and ever-increasing traffic on long-span suspension bridges, damages such as corrosion may occur in hangers, eventually necessitating their replacement. This replacement task is challenging as it affects various adjacent components of the suspension bridge, namely, the main cable, the main girder, the cable clamp, and other hangers. This paper investigates damage induced by corrosion in a hanger of a suspension bridge and validates a multi-point lifting approach through numerical investigation and implementation. The numerical investigation takes into account three controlling factors, namely, (i) the tension of the temporary and adjacent hangers; (ii) the displacements of the main cable and the main girder; (iii) the anti-sliding coefficient of the cable clamp. Based on the finite element (FE) investigation, the replacement was successfully implemented using the five-point lifting approach. In addition, a corrosion distribution map is utilized to visualize the corrosion characteristics of the replaced hanger, leading to recommendations for enhancing the durability of hangers. In conclusion, this study sheds light on the design and replacement of long hangers in other suspension bridges and provides valuable insights for improving the durability and maintenance of such structures.

Noncontact cable tension estimation using edge recognition technology based on convolutional network

The cable is the main load-bearing component of cable-stayed, suspension, and tied-arch bridges. The tension of the cable is crucial to the bridge's overall safety. Therefore, achieving an accurate and rapidcable tension estimation is of great practical importance. Currently, the method of estimating cable tension based on image processing technology avoids the disadvantages of conventional estimation methods, such as high cost, difficult sensor installation, and potential structural damage. However, for estimating cable tension based on image processing technology, a complex optical background may reduce the accuracy and stability of cable tension estimation. A method for estimating cable tension based on a deep convolutional network is proposed to solve the problem. The convolutional network is initially used to identify cable edges. Second, analyzing the edge pixels' motion identifies the cable's fundamental frequency. Finally, the cable tension is estimated using the correlation between fundamental frequency and cable tension. A field experiment was conducted on cable-stayed and tied-arch bridges in Chengdu to validate the proposed method's applicability and accuracy. The results of the experiments indicate that the edge detection effect of the proposed method is demonstrably superior to that of the Canny algorithm under various optical backgrounds and that the cable tension relative difference between the proposed method and the conventional method based on accelerometer is within 5%.

Practical evaluation of high-strength concrete corbels reinforced with GFRP bent bars

Corbels, designed primarily as discontinuity-region structural elements, are often used as components of bridges and buildings to support beams and girders. There has been extensive research on steel-reinforced concrete (RC) corbels; however, there is a noticeable gap in research to investigate the behavior of glass fiber-reinforced polymer (GFRP)-RC corbels. Considering the linear stress–strain relationship up to failure of GFRP, the use of high-strength concrete (HSC) in lieu of the normal-strength concrete (NSC) in corbels would reduce their sizes considerably; yet it will aggravate their brittle nature. In this study, nine full-scale concrete corbels were constructed and tested until failure. One reference specimen was reinforced with steel bars, while the other eight were GFRP-RC corbels. The main test parameters were the type of concrete (HSC or NSC), the shear span-to-depth ratio, and the total reinforcement ratio of the main and horizontal bars. The tests showed that the principal mode of failure was concrete strut crushing for specimens with a shorter shear-span-to-depth ratio. The steel-RC corbels failed by tension tie yielding, which showed large ductility. The Canadian standards for FRP-RC building structures predicted a conservative mean capacity with Ppre/Pexp ratio of 0.59 compared to the American code and Eurocode, which overestimated the capacities with a ratio of 1.18 and 1.25, respectively.

Structural, dielectric, thermoelectric, and magmatic properties of Er3+ ion substituted Mg-Zn spinal nanoferrites: High charge-storage capacitors and high-frequency microwave device applications

The Er-substituted magnesium and zink ferrite nanomaterials with the chemistry formula Mg0.8Zn0.2ErxFe2-xO4 (x = 0.00, 0.005, 0.01, 0.015, 0.02, 0.025) were generated in the study utilizing the citrate-gel auto combustion method. XRD is being used to investigate the cubic spinal structural structure. A transmission electron microscope (TEM) and a field emission scanning electron microscope (FESEM) were used to examine surface morphology. From the structural studies, the crystal sizes range from 14.46 to 96.40, with lattice parameters ranging from 8.434 to 8.449 and X-ray density (gm/cc) ranging from 4.585 to 4.671 and porosity (%) ranging from 7.67 to 7.91. The resulting macrostrain produces internal tension and can impair material grain growth. UV–visible absorption spectroscopy gives grain size pictures as well as quantifications at 300 K and 100 K, respectively. The wavelength range cut-off from 537.6 nm to 270.9 nm. The dielectric characteristics of the samples were tested at room temperature using an LCR meter at various frequencies. The dielectric parameters include the real component of the dielectric constant, the dielectric constant's imaginary portion, and the loss tangent. For each sample, the loss tangent of nanoferrites fell as the frequency increased. This type of material is used in capacitors and microwave devices. Thermoelectric power studies measured all samples. From the studies observed, the seeback coefficient decreases with increasing temperature and Er doping. The room temperature and low temperature magnetic properties were studied at 300 k and 100 k at low temperature ZFC and FC. The blocking temperature Tb was around 300 K.

Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine

To address the problem in model computations and the limited accuracy of current bridge deterioration prediction methods, this paper proposes a novel bridge deterioration prediction meth-od using the whale optimization algorithm and extreme learning machine (WOA-ELM). First, we collected a dataset consisting of 539 sets of bridge inspection data and determined the necessary influencing factors through correlation analysis. Subsequently, the WOA-ELM algorithm was applied to establish a nonlinear mapping relationship between each influencing factor and the bridge condition indicators. Furthermore, the extreme learning machine (ELM), back-propagation neural network (BPNN), decision trees (DT), and support vector machine (SVM) were employed for comparison to validate the superiority of the proposed method. In addition, this paper provides further substantiation of the model’s exceptional predictive capabilities across diverse bridge components. The results demonstrate the accurate predictive capability of the proposed method for bridge conditions. Compared with ELM, BPNN, DT, and SVM, the proposed method exhibits significant improvements in predictive accuracy, i.e., the correlation coefficient is increased by 4.1%, 11.4%, 24.5%, and 33.6%, and the root mean square error is reduced by 7.3%, 18.0%, 14.8%, and 18.1%, respectively. Moreover, the proposed method presents considerably enhanced generalization capabilities, resulting in the reduction in mean relative error by 11.6%, 15.3%, 6%, and 16.2%. The proposed method presents a robust framework for proactive bridge maintenance.

Post-stroke Depressive Symptoms and Cognitive Performances: A Network Analysis

ObjectiveTo examine the relationships between post-stroke depression and cognition using network analysis. In particular, we identified central depressive symptoms, central cognitive performances, and bridge components that connect these 2 constructs. DesignAn observational study. We applied network analysis to analyze baseline data to visualize and quantify the relationships between depression and cognition. SettingHome and Community. Participants202 participants with mild-to-moderate stroke (N=202; mean age: 59.7 years; 55% men; 55% Whites; 90% ischemic stroke). InterventionNot applicable. Main Outcome MeasuresPatient Health Questionnaire (PHQ-8) for depressive symptoms and the NIH Toolbox Cognitive Battery for cognitive performances. ResultsDepressive symptoms were positively intercorrelated with the network, with symptoms from similar domains clustered together. Mood (expected influence=1.58), concentration (expected influence=0.67), and guilt (expected influence=0.63) were the top 3 central depressive symptoms. Cognitive performances also showed similar network patterns, with executive function (expected influence=0.89), expressive language (expected influence=0.68), and processing speed (expected influence=0.48) identified as the top 3 central cognitive performances. Psychomotor functioning (bridge expected influence=2.49) and attention (bridge expected influence=1.10) were the components connecting depression and cognition. ConclusionsThe central and bridge components identified in this study might serve as targets for interventions against these deficits. Future trials are needed to compare the effectiveness of interventions targeting the central and bridge components vs general interventions treating depression and cognitive impairment as a homogenous clinical syndrome.

LSTM combined with BIM technology in the management of small and medium-sized span highway concrete beam bridges

To better carry out maintenance management for medium and small span highway concrete beam bridges, this bridge is taken as the research object. Building information modeling technology is applied to its maintenance management. The corresponding management system is constructed. Through this system, the distribution of diseases and other information can be viewed. Long short-term memory neural network is used to build the corresponding prediction model to predict the technical condition score of bridge components. The results show that the prediction error is small. The minimum error rate in component technical condition scoring prediction is 0.00 %. In the full bridge technology scoring prediction, the minimum scoring error is 2.8 points, and the maximum scoring error is 6 points. The minimum average error rate of this model in predicting component technical condition scores is 0.15 %. Except for individual components, the average error rate of all other components is less than 5 %. The accuracy of the prediction model is 95 %. The performance is superior to linear regression models. The average error rate in the abutment is 7.45 % lower than the linear regression model. This method can effectively predict the degradation of small and medium-sized span highway concrete beam bridges and achieve three-dimensional visualization of diseases.

Shake table test and numerical model update for a viscous damper isolated cable-stayed bridge

This study focused on the seismic responses and numerical model update of fluid viscous dampers (FVDs) isolated cable-stayed bridges excited by ground motions with different frequency contents. A 1/20 scaled three-span cable-stayed bridge was designed, constructed, and tested on a facility with four shake tables. Four ground motions with different frequency contents were adopted to excite the shake model. Then, a numerical model update framework of the shake table test was introduced, which combines the differential evolution (DE) algorithm and the parallel finite element computing technique. In addition, a time history response-based objective function was established that considered various responses of the bridge components. The shake table test results reveal that frequency contents affect the seismic responses of the cable-stayed bridge, and the ground motions with strong velocity pulses can amplify the displacements of the pylons and girder. In addition, the accelerations of the girder are relatively small and less sensitive to input ground motions. In general, the proposed method is efficient and accurate for updating the numerical model of the shake table test model.