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Introduction: Fatal fall from height accidents, especially on construction sites, persist, underscoring the importance of monitoring and managing worker behaviors to enhance safety. Deep learning showed the possibility of substituting the manual work of safety managers. However, applying detection results to determine compliance with safety regulations has limitations., Method: This study estimated the actual working height depending on the height of the object detection bounding box by specifying the consistent hinge part as a target marker based on ladder manufacturing regulations. Furthermore, an attempt was made to improve the separation between workers, coworkers, and persons unconnected to ladder activities by applying an optimized loss function alongside an attention mechanism., Results: The experimental results showed that an average precision increased from 87.60% to 90.44%. The performance of the monitoring unsafe behavior of ladder worker following the Korea Occupational Safety and Health Agency (KOSHA) guide was evaluated by 91.40 F1-Score, which accumulated sorted according to the working height., Conclusions: Experimental results show the feasibility of the real-time automate safety monitoring in ladder work., Practical Applications: By linking the estimated working height and deep learning multi-detection results to established safety regulations, the proposed method shows the potential to automatically monitoring unsafe behaviors in construction site., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Seunghee Park reports financial support was provided by Sungkyunkwan University.], (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Our understanding of the impact of atmospheric acid deposition on marine carbonate system remains limited, largely due to a lack of data regarding acidity present in atmospheric particles and precipitation. Previous research has relied on the electroneutrality-based ion balance method for indirect estimation of atmospheric acidity. In this study, atmospheric samples collected at a coastal site of South Korea were mixed with seawater to measure the change in seawater total alkalinity (ΔTA APL ) associated with atmospheric proton loading. For the precipitation samples, the measured ΔTA APL and electroneutrality-based estimates showed a significant correlation. However, we did not observe similar results for the atmospheric particle samples. Furthermore, the decrease in oceanic TA due to ΔTA APL was substantially smaller than that in dissolved inorganic carbon from concurrent nitrogen fertilization. Consequently, the adverse impact of acid deposition on ocean acidification or air-sea exchange of CO 2 appears to be insignificant on a short-term scale., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

South Korea's east coast is facing several issues related to coastal erosion because of sea-level rise, typhoon-induced storm surges, and various coastal development projects. In recent decades, high storm waves have frequently appeared on the east coast, causing casualty, beach erosion, and coastal infrastructure damage, drawing significant public attention. Thus, we analyzed the multi-decadal shoreline changes to understand the coastal dynamics and the forces responsible for the spatio-temporal changes along the 173 km coastline. The shorelines covering 38 years between 1984 and 2022 were derived from Landsat images and the change statistics, i.e., linear regression rate (LRR), endpoint rate (EPR), weighted linear regression (WLR), and net shoreline movement (NSM), were calculated at a 100 m alongshore intervals using Digital Shoreline Analysis System (DSAS), revealed several distinct behaviors of shoreline position. The long-period (1984-2022) assessment showed an average shoreline change rate (LRR) of 0.17 m/year with an estimated mean erosion and deposition rate of -0.57 and 2.07 m/year, respectively. The long-term surface gain and loss of the backshore region exhibited that the net surface gain of the east coast is 421.13 ha, and the net loss is 181.82 ha. The assessment of decadal shoreline changes showed a cyclic pattern of erosion (from 1984-1990 and 1999-2010) and accretion (from 1990-1999 and 2010-2022). Furthermore, a secondary level of investigation was conducted to address a wider variety of coastal behaviors by segmenting shoreline change rates based on coast types and average slopes along coastlines. It was observed that the frequent coastal deformation is associated with a flatter beach compared to a steep one. This study found that the artificial structures constructed along the east coast have not completely solved or stopped the erosion issues but shifted it from one location to another. The analysis of local and regional shoreline changes had shown that typhoon-induced storm surges, high storm waves, and anthropogenic activities like encroachment and the development of artificial coastal structures were the primary drivers of coastline changes along the east coast. Finally, we proposed a decision-making classification scheme that can be used to determine the mechanism of decision for protective and preventive measures against further coastal deterioration., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Building information modeling (BIM), a common technology contributing to information processing, is extensively applied in construction fields. BIM integration with augmented reality (AR) is flourishing in the construction industry, as it provides an effective solution for the lifecycle of a project. However, when applying BIM to AR data transfer, large and complicated models require large storage spaces, increase the model transfer time and data processing workload during rendering, and reduce visualization efficiency when using AR devices. The geometric optimization of the model using mesh reconstruction is a potential solution that can reduce the required storage while maintaining the shape of the components. In this study, a 3D engine-based mesh reconstruction algorithm that can pre-process BIM shape data and implement an AR-based full-size model is proposed, which is likely to increase the efficiency of decision making and project processing for construction management. As shown in the experimental validation, the proposed algorithm significantly reduces the number of vertices, triangles, and storage for geometric models while maintaining the overall shape. Moreover, the model elements and components of the optimized model have the same visual quality as the original model; thus, a high performance can be expected for BIM visualization in AR devices.

The phase changes in alkali-activated slag samples when exposed to supercritical carbonation were evaluated. Ground granulated blast furnace slag was activated with five different activators. The NaOH, Na 2 SiO 3 , CaO, Na 2 SO 4 , and MgO were used as activators. C-S-H is identified as the main reaction product in all samples along with other minor reaction products. The X-ray diffractograms showed the complete decalcification of C-S-H and the formation of CaCO 3 polymorphs such as calcite, aragonite, and vaterite. The thermal decomposition of carbonated samples indicates a broader range of CO 2 decomposition. Formation of highly cross-linked aluminosilicate gel and a reduction in unreacted slag content upon carbonation is observed through 29 Si and 27 Al NMR spectroscopy. The observations indicate complete decalcification of C-S-H with formation of highly cross-linked aluminosilicates upon sCO 2 carbonation. A 20-30% CO 2 consumption per reacted slag under supercritical conditions is observed.

The free cantilever method (FCM) is a bridge construction method in which the left and right segments are joined in sequence from a pier without using a bottom strut. To support the imbalance of the left and right moments during construction, temporary steel rods, upon which tensile force is applied that cannot be managed after construction, are embedded in the pier. If there is an excessive loss of tensile force applied to the steel rods, the segments can collapse owing to the unbalanced moment, which may cause personal and property damage. Therefore, it is essential to monitor the tensile force in the temporary steel rods to prevent such accidents. In this study, a tensile force estimation method for the temporary steel rods of an FCM bridge using embedded Elasto-Magnetic (EM) sensors was proposed. After the tensile force was applied to the steel rods, the change in tensile force was monitored according to the changing area of a magnetic hysteresis curve, as measured by the embedded EM sensors. To verify the field applicability of the proposed method, the EM sensors were installed in an FCM bridge pier under construction. The three sensors were installed in conjunction with a sheath tube, and the magnetic hysteresis curve was measured over nine months. Temperature data from the measurement period were used to compensate for the error due to daily temperature fluctuations. The estimated tensile force was consistent with an error range of ±4% when compared with the reference value measured by the load cell. Based on the results of this experiment, the applicability of the proposed method was demonstrated.

We evaluated the potential impacts of atmospheric deposition on marine productivity and inorganic carbon chemistry in the northwestern Pacific Ocean (8-39°N, 125-157°E). The nutrient concentration in atmospheric total suspended particles decreased exponentially with increasing distance from the closest land-mass (Asia), clearly revealing anthropogenic and terrestrial contributions. The predicted mean depositional fluxes of inorganic nitrogen were approximately 34 and 15 μmol m -2 d -1 to the west and east of 140°E, respectively, which were at least two orders of magnitude greater than the inorganic phosphorus flux. On average, atmospheric particulate deposition would support 3-4% of the net primary production along the surveyed tracks, which is equivalent to ~2% of the dissolved carbon increment caused by the penetration of anthropogenic CO 2 . Our observations generally fell within the ranges observed over the past 18 years, despite an increasing trend of atmospheric pollution in the source regions during the same period, which implies high temporal and spatial variabilities of atmospheric nutrient concentration in the study area. Continued atmospheric anthropogenic nitrogen deposition may alter the relative abundances of nitrogen and phosphorus., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

With the advent of the Fourth Industrial Revolution, the economic, social, and technological demands for pipe maintenance are increasing due to the aging of the infrastructure caused by the increase in industrial development and the expansion of cities. Owing to this, an automatic pipe damage detection system was built using a laser-scanned pipe's ultrasonic wave propagation imaging (UWPI) data and conventional neural network (CNN)-based object detection algorithms. The algorithm used in this study was EfficientDet-d0, a CNN-based object detection algorithm which uses the transfer learning method. As a result, the mean average precision (mAP) was measured to be 0.39. The result found was higher than COCO EfficientDet-d0 mAP, which is expected to enable the efficient maintenance of piping used in construction and many industries.

Concrete strength and factors affecting its development during early concrete curing are important research topics. Avoiding uncertain incidents during construction and in service life of structures requires an appropriate monitoring system. Therefore, numerous techniques are used to monitor the health of a structure. This paper presents a nondestructive testing technique for monitoring the strength development of concrete at early curing ages. Dispersed carbon nanotubes (CNTs) were used with cementitious materials and piezoelectric (PZT) material, a PZT ceramic, owing to their properties of intra electromechanical effects and sensitivity to measure the electromechanical impedance (EMI) signatures and relevant properties related to concrete strength, such as the elastic modulus, displacement, acceleration, strength, and loading effects. Concrete compressive strength, hydration temperature, mixture ratio, and variation in data obtained from the impedance signatures using fuzzy logic were utilized in the comparative result prediction method for concrete strength. These results were calculated using a fuzzy logic-based model considering the maturity method, universal testing machine (UTM) data, and analyzed EMI data. In the study, for data acquisition, a hybrid PZT-CNT sensor and a temperature sensor (Smart Rock) were embedded in the concrete to obtain the hydration temperature history to utilize the concrete maturity method and provide data on the EMI signatures. The dynamic changes in the medium caused during the phase in the concrete strengthening process were analyzed to predict the strength development process of concrete at early curing ages. Because different parameters are considered while calculating the concrete strength, which is related to its mechanical properties, the proposed novel method considers that changes in the boundary condition occurring in the concrete paste modify the resonant frequency response of the structure. Thus, relating and analyzing this feature can help predict the concrete strength. A comprehensive comparison of the results calculated using the proposed module, maturity method, and cylindrical specimens tested using the UTM proved that it is a cost-effective and fast technique to estimate concrete strength to ensure a safe construction of reinforced cement concrete infrastructures.

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

The application of deep learning (DL) algorithms to non-destructive evaluation (NDE) is now becoming one of the most attractive topics in this field. As a contribution to such research, this study aims to investigate the application of DL algorithms for detecting and estimating the looseness in bolted joints using a laser ultrasonic technique. This research was conducted based on a hypothesis regarding the relationship between the true contact area of the bolt head-plate and the guided wave energy lost while the ultrasonic waves pass through it. First, a Q-switched Nd:YAG pulsed laser and an acoustic emission sensor were used as exciting and sensing ultrasonic signals, respectively. Then, a 3D full-field ultrasonic data set was created using an ultrasonic wave propagation imaging (UWPI) process, after which several signal processing techniques were applied to generate the processed data. By using a deep convolutional neural network (DCNN) with a VGG-like architecture based regression model, the estimated error was calculated to compare the performance of a DCNN on different processed data set. The proposed approach was also compared with a K-nearest neighbor, support vector regression, and deep artificial neural network for regression to demonstrate its robustness. Consequently, it was found that the proposed approach shows potential for the incorporation of laser-generated ultrasound and DL algorithms. In addition, the signal processing technique has been shown to have an important impact on the DL performance for automatic looseness estimation.

The Light Detection And Ranging (LiDAR) system has become a prominent tool in structural health monitoring. Among such systems, Terrestrial Laser Scanning (TLS) is a potential technology for the acquisition of three-dimensional (3D) information to assess structural health conditions. This paper enhances the application of TLS to damage detection and shape change analysis for structural element specimens. Specifically, estimating the deflection of a structural element with the aid of a Lidar system is introduced in this study. The proposed approach was validated by an indoor experiment by inducing artificial deflection on a simply supported beam. A robust genetic algorithm method is utilized to enhance the accuracy level of measuring deflection using lidar data. The proposed research primarily covers robust optimization of a genetic algorithm control parameter using the Taguchi experiment design. Once the acquired data is defined in terms of plane, which has minimum error, using a genetic algorithm and the deflection of the specimen can be extracted from the shape change analysis.

Corrosion detection using a pulsed laser scanning system can be performed via ultrasonic wave propagation imaging. This method outputs illustrations of the wave field within the host structure; thus, it can depict wave-corrosion area interactions. Additionally, post-processing can be performed to enhance the visualization of corroded areas. The wavefield energy computed using RMS (Root Mean Square) is a validated post-processing tool capable of displaying the location and area of corrosion-damaged regions. Nonetheless, to characterize corrosion, it is necessary to determine its depth. The measurement of depth in conjunction with that of the corroded area via the RMS distribution enables the determination of all dimensions of corrosion damage. Thereafter, the flaw severity can be evaluated. This study employed a wavefield within a plate on which corrosion was developed artificially to generate frequency-wavenumber dispersion curves. The curves were compared with their counterparts from a corrosion-free plate. Alternatively, they could be compared with dispersion curves drawn using the depth and material properties of a pristine plate via a computer program. Frequency-wavenumber pairs were extracted from the dispersion curves produced using the portion of the wavefield within the corroded area. These were inserted into the Rayleigh-Lamb equation, from which depths were calculated and averaged.

Wire ropes used in various applications such as elevators and cranes to safely carry heavy weights are vulnerable to breakage or cross-sectional loss caused by the external environment. Such damage can pose a serious risk to the safety of the entire structure because damage under tensile force rapidly expands due to concentration of stress. In this study, the magnetic flux leakage (MFL) method was applied to diagnose cuts, corrosion, and compression damage in wire ropes. Magnetic flux signals were measured by scanning damaged wire rope specimens using a multi-channel sensor head and a compact data acquisition system. A series of signal-processing procedures, including the Hilbert transform-based enveloping process, was applied to reduce noise and improve the resolution of signals. The possibility of diagnosing several types of damage was verified using enveloped magnetic flux signals. The characteristics of the MFL signals according to each damage type were then analyzed by comparing the extracted damage indices for each damage type. For automated damage type classification, a support vector machine (SVM)-based classifier was trained using the extracted damage indices. Finally, damage types were automatically classified as cutting and other damages using the trained SVM classifier.

The atmospheric deposition of anthropogenic nitrogen is an increasingly important new source of nitrogen to the ocean. Coastal areas east of the Korean Peninsula are suitable for the investigation of the effects of atmospheric anthropogenic nitrogen on the ocean nutrient system because of the low riverine discharge rates and the prevailing influence of the East Asian outflow. Thus, we measured the concentrations of nitrate (NO 3 - ) and ammonium (NH 4 + ) in airborne particles and in precipitation from March 2014 to February 2016 at a coastal site (37.08°N, 129.41°E) on the east coast of Korea. The dry deposition of NO 3 - (27-30 mmol N m -2  yr -1 ) was far greater than that of NH 4 + (6-8 mmol N m -2  yr -1 ). The greater rate of dry NO 3 - deposition was associated with air masses traveling over northeastern China and central Korea. In contrast, the rates of wet deposition of NO 3 - (17-24 mmol N m -2  yr -1 ) and NH 4 + (14-27 mmol N m -2  yr -1 ) were comparable and were probably associated with in-cloud scavenging of these ions. The results indicate that the total deposition of NO 3 - and NH 4 + combined could contribute to ~2.4% and ~1.9% of the primary production in the coastal areas east of the Korean Peninsula and in the East Asian marginal seas, respectively, which would be a lower bound because the dry deposition of reactive nitrogen gas was not included. Our study shows that the atmospheric input of anthropogenic NO 3 - and NH 4 + may substantially increase phytoplankton biomass in the coastal waters of the East Sea near the Korean Peninsula., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Recently, the early-age strength prediction for RC (reinforced concrete) structures has been an important topic in the construction industry, relating to project-time reduction and structural safety. To address this, numerous destructive and NDTs (non-destructive tests) are applied to monitor the early-age strength development of concrete. This study elaborates on the NDT techniques of ultrasonic wave propagation and concrete maturity for the estimation of compressive strength development. The results of these comparative estimation approaches comprise the concrete maturity method, penetration resistance test, and an ultrasonic wave analysis. There is variation of the phase transition in the concrete paste with the changing of boundary limitations of the material in accordance with curing time, so with the formation of phase-transition changes, changes in the velocities of ultrasonic waves occur. As the process of hydration takes place, the maturity method produces a maturity index using the time-feature reflection on the strength-development process of the concrete. Embedded smart temperature sensors (SmartRock) and PZT (piezoelectric) sensors were used for the data acquisition of hydration temperature history and wave propagation. This study suggests a novel relationship between wave propagation, penetration tests, and hydration temperature, and creates a method that relies on the responses of resonant frequency changes with the change of boundary conditions caused by the strength-gain of the concrete specimen. Calculating the changes of these features provides a pattern for estimating concrete strength. The results for the specimens were validated by comparing the strength results with the penetration resistance test by a universal testing machine (UTM). An algorithm used to relate the concrete maturity and ultrasonic wave propagation to the concrete compressive strength. This study leads to a method of acquiring data for forecasting in-situ early-age strength of concrete, used for secure construction of concrete structures, that is fast, cost effective, and comprehensive for SHM (structural health monitoring)., Competing Interests: The authors declared no conflict of interest.

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size., Competing Interests: The authors declare no conflict of interest.

The tensile force of pre-stressed concrete (PSC) girders is the most important factor for managing the stability of PSC bridges. The tensile force is induced using pre-stressing (PS) tendons of a PSC girder. Because the PS tendons are located inside of the PSC girder, the tensile force cannot be measured after construction using conventional NDT (non-destructive testing) methods. To monitor the induced tensile force of a PSC girder, an embedded EM (elasto-magnetic) sensor was proposed in this study. The PS tendons are made of carbon steel, a ferromagnetic material. The magnetic properties of the ferromagnetic specimen are changed according to the induced magnetic field, temperature, and induced stress. Thus, the tensile force of PS tendons can be estimated by measuring their magnetic properties. The EM sensor can measure the magnetic properties of ferromagnetic materials in the form of a B (magnetic density)-H (magnetic force) loop. To measure the B-H loop of a PS tendon in a PSC girder, the EM sensor should be embedded into the PSC girder. The proposed embedded EM sensor can be embedded into a PSC girder as a sheath joint by designing screw threads to connect with the sheath. To confirm the proposed embedded EM sensors, the experimental study was performed using a down-scaled PSC girder model. Two specimens were constructed with embedded EM sensors, and three sensors were installed in each specimen. The embedded EM sensor could measure the B-H loop of PS tendons even if it was located inside concrete, and the area of the B-H loop was proportionally decreased according to the increase in tensile force. According to the results, the proposed method can be used to estimate the tensile force of unrevealed PS tendons.

The potential for monitoring the construction of post-tensioned concrete beams and detecting damage to the beams under loading conditions was investigated through an experimental program. First, embedded sensors were investigated that could measure pre-stress from the fabrication process to a failure condition. Four types of sensors were installed on a steel frame, and the applicability and the accuracy of these sensors were tested while pre-stress was applied to a tendon in the steel frame. As a result, a tri-sensor loading plate and a Fiber Bragg Grating (FBG) sensor were selected as possible candidates. With those sensors, two pre-stressed concrete flexural beams were fabricated and tested. The pre-stress of the tendons was monitored during the construction and loading processes. Through the test, it was proven that the variation in thepre-stress had been successfully monitored throughout the construction process. The losses of pre-stress that occurred during a jacking and storage process, even those which occurred inside the concrete, were measured successfully. The results of the loading test showed that tendon stress and strain within the pure span significantly increased, while the stress in areas near the anchors was almost constant. These results prove that FBG sensors installed in a middle section can be used to monitor the strain within, and the damage to pre-stressed concrete beams., Competing Interests: The authors declare no conflict of interest.

Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process., Competing Interests: The authors declare no conflict of interest.

In this study, a non-contact laser ultrasonic propagation imaging technique was applied to detect the damage of plate-like structures. Lamb waves were generated by an Nd:YAG pulse laser system, while a galvanometer-based laser scanner was used to scan the preliminarily designated area. The signals of the structural responses were measured using a piezoelectric sensor attached on the front or back side of the plates. The obtained responses were analyzed by calculating the root mean square (RMS) values to achieve the visualization of structural defects such as crack, corrosion, and so on. If the propagating waves encounter the damage, the waves are scattered at the damage and the energy of the scattered waves can be expressed by the RMS values. In this study, notch and corrosion were artificially formed on aluminum plates and were considered as structural defects. The notches were created with different depths and angles on the aluminum plates, and the corrosion damage was formed with different depths and areas. To visualize the damage more clearly, edge detection methodologies were applied to the RMS images and the feasibility of the methods was investigated. The results showed that most of the edge detection methods were good at detecting the shape and/or the size of the damage while they had poor performance of detecting the depth of the damage.

This paper proposes a non-contact nondestructive evaluation (NDE) technique that uses laser-induced ultrasonic waves to visualize corrosion damage in aluminum alloy plate structures. The non-contact, pulsed-laser ultrasonic measurement system generates ultrasonic waves using a galvanometer-based Q-switched Nd:YAG laser and measures the ultrasonic waves using a piezoelectric (PZT) sensor. During scanning, a wavefield can be acquired by changing the excitation location of the laser point and measuring waves using the PZT sensor. The corrosion damage can be detected in the wavefield snapshots using the scattering characteristics of the waves that encounter corrosion. The structural damage is visualized by calculating the logarithmic values of the root mean square (RMS), with a weighting parameter to compensate for the attenuation caused by geometrical spreading and dispersion of the waves. An intact specimen is used to conduct a comparison with corrosion at different depths and sizes in other specimens. Both sides of the plate are scanned with the same scanning area to observe the effect of the location where corrosion has formed. The results show that the damage can be successfully visualized for almost all cases using the RMS-based functions, whether it formed on the front or back side. Also, the system is confirmed to have distinguished corroded areas at different depths., Competing Interests: The authors declare no conflicts of interest.

Objective: To determine the type and frequency of postpartum symptoms during recovery from antepartum bed rest treatment across 6 weeks postpartum., Design: Longitudinal repeated measures study., Setting: Three perinatal tertiary care hospitals in two cities in the Midwest., Participants: One hundred six postpartum women who had a singleton high-risk pregnancy and were treated with antepartum bed rest., Main Outcome Measures: A Postpartum Symptom Checklist assessed physiologic and psychological symptoms., Results: Women reported 12.5 symptoms at 2 days postpartum, but symptoms rapidly declined at 1 week. Ninety-three percent reported a mean of 6.6 symptoms at 6 weeks postpartum. Symptoms that continued to be reported at 6 weeks by at least 40% of women were fatigue, mood changes, tenseness, difficulty concentrating, back muscle soreness, dry skin, and headache. Women who had a cesarean delivery reported significantly more symptoms than those who had a vaginal delivery (p = .006). Length of maternal bed rest was significantly correlated with the number of symptoms at postpartum weeks 1, 2, 4, 5, and 6., Conclusion: Postpartum symptoms decreased across time but reveal an underlying morbidity that is not resolved by 6 weeks postpartum.

This methodological study was designed to replicate three previous studies of depressive symptoms, compare assessment of antepartum depressive symptoms among high-risk pregnant women using three standardized instruments, and evaluate the psychometric properties of the instruments. The sample consisted of 89 high-risk pregnant women treated with bed rest, of whom 37 remained hospitalized at 4 weeks. Depressive symptoms were measured by the Multiple Affect Adjective Checklist Revised (MAACL-R) Dysphoria construct, the Profile of Mood States (POMS) Depression scale, and the Center for Epidemiologic Studies Depression Scale (CES-D) across antepartum hospitalization. Internal consistency, test-retest reliability, and convergent validity were high. Depressive symptoms were high on admission as measured by all three instruments and significantly decreased across time when measured by the MAACL-R and POMS., ((c) 2004 Wiley Periodicals, Inc.)

Despite lack of evidence for effectiveness, obstetricians in the United States prescribe antepartum bed rest for more than 700,000 women per year. However, in nonpregnant samples, bed rest treatment produces weight loss. This study assessed maternal weight change (gain) during antepartum hospitalization for bed rest treatment; compared appropriateness of infant birth weights for gestational age, race, and gender; and determined whether maternal weight change predicted infant birth weight. The convenience sample for this longitudinal study consisted of 141 women with high-risk pregnancies who were treated with hospital bed rest. Weekly rate of pregnancy weight change by body mass index was compared with Institute of Medicine recommendations for rate of pregnancy weight gain. Infant birth weight was compared with current US infant birth weights for matching gestational age, gender, and race. Weekly antepartum weight change was significantly lower than Institute of Medicine recommendations (P < 0.001). Infant birth weights were also significantly lower than the national mean when matched for each infant's gestational age, race, and gender (P < 0.001). Maternal weight change predicted infant birth weight (P = 0.05). Bed rest treatment is ineffective for improving pregnancy weight gain. Lower infant birth weights across all gestational ages suggest that maternal weight loss during bed rest may be associated with an increased risk of fetal growth restriction. A randomized trial comparing women with high-risk pregnancies who are ambulatory with those on bed rest is needed to determine whether bed rest treatment, underlying maternal-fetal disease, or both influence inadequate maternal weight gain and poor intrauterine growth.