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Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements. [ABSTRACT FROM AUTHOR]

Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements.

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Our ability to test probabilistic models linking clast movements to bedload diffusion is most limited by basic field data, because measuring transport statistics during natural floods is difficult. We embedded accelerometers and gyroscopes into artificial cobbles, and measured transport during 28 daily snowmelt floods in Halfmoon Creek, Colorado, USA. The tracers captured ≈6 orders of temporal magnitude of rest durations in one data set for the first time. Motions and rests suggest a scaling transition around ≈12.5 min from subdiffusion to superdiffusion with increasing shear stress and timescale. We interpret that diurnal hydrograph cyclicity may cause another diffusion scaling break at ≈12 h. Shear stress controls and scaling uncertainties may explain differences in diffusion exponents found in several field data sets, suggesting that gravel superdiffusion scaling may be relatively universal over minutes to seasons. Methodologically, "smartrocks" can quantify field transport probabilities previously only possible in laboratory experiments. Plain Language Summary: During floods in mountain streams, gravel moves downstream but also spreads out. Predicting this spreading—called diffusion—is useful for river restoration and for assessing flood risks. We used "smartrocks" containing accelerometers and batteries to measure exactly when individual sediment grains moved during a month‐long flood in the Rocky Mountains of Colorado. The data were used to calibrate various equations to improve predictions of gravel diffusion during future floods. Key Points: Sensor‐embedded cobble tracers quantify bedload rest and hop durations during a ≈month‐long 10‐year flood in a natural mountain streamShear stress is quantified as a first‐order control on bedload diffusionA single field data set resolves scaling transitions from subdiffusion to superdiffusion with increasing shear stress and timescale [ABSTRACT FROM AUTHOR]

For gyratory compaction, the concept of the locking point was initially developed to identify the compactability of asphalt mixes and to alleviate potential aggregate crushing in the mold. Most previous studies on the locking point were based on specimens’ height change. Recent studies have indicated that the gyratory locking point of cold mix asphalt mixtures could be determined by the rotation angle range indicator using SmartRock. However, height or rotation angle change ultimately reflects a change in volume. Additionally, there is no clear physical and mechanical connection between the volume change and the gyratory locking point. In this paper, a stone mastic asphalt mixture (SMA 13) was selected for gyratory compaction applying various compaction temperatures. The compaction data were recorded by a SmartRock embedded in different positions. Collected data included stress, rotation angle, and acceleration. The major findings are as follows: (1) the specimen’s locking point could be determined based on a representative stress value when the SmartRock was embedded in the specimen’s center, and the results are close to the traditional evaluation results (LP3 or LP2-2-3); (2) the representative rotation angle value reached a plateau earlier than the representative stress value; (3) the representative acceleration value is not suitable for characterizing the interlocking process during gyratory compaction.

Bedload movement is fundamentally probabilistic. Our quantitative understanding of gravel transport is particularly limited when flow conditions just exceed thresholds of motion, in part because of difficulties in measuring transport statistics during natural floods. We used accelerometer‐embedded tracer clasts to precisely measure the timing of grain motions and rests during snowmelt floods in Halfmoon Creek, a gravel‐bed mountain stream in Colorado, USA. These new data let us explore how probabilities of tracer movement varied as functions of discharge and time. Bedload hysteresis occurred over both daily and seasonal timescales and included clockwise, counterclockwise, and figure‐eight patterns. We empirically explain the hysteresis by modifying a bedload transport model to have an evolving threshold of motion parameter. We calculate how the thresholds of motion progressively evolved through time over 22 days during the 2015 snowmelt flood. Our results quantitatively show that thresholds of motion are functions of both (a) cumulative shear stress and (b) temporal changes in shear stress during floods. Plain Language Summary: Predicting the effects of floods on mountain river channels remains difficult but is important because floods affect people, communities, and ecosystems. Our research shows that the amount and timing of gravel transported downstream depends not only on how much water is flowing in the channel but also on the "history" of flow and sediment movement that has occurred previously during the flood or previous recent floods. We developed "smartrocks" that each hold sensors and batteries to measure the exact timing of movement of these artificial tracer gravels. We collected field data during a month‐long flood in a stream in the Rocky Mountains near Leadville, Colorado, USA. By measuring exactly when rocks move during floods, we can better understand how to predict when channels will be stable or will change during future floods of different sizes and how much change is likely to occur. Key Points: "Smartrock" tracer cobbles were used to measure the timing of gravel transport during a natural snowmelt floodThresholds of motion increased with cumulative discharge but decreased after days of high peak dischargeEvolving thresholds of motion can explain observed bedload hysteresis [ABSTRACT FROM AUTHOR]

• Ballast box tests were conducted to investigate the ballast movement pattern. • Wireless embedded devices – "SmartRock" – were utilized. • An Autoregressive (AR) model for identifying ballast movement pattern is offered. • The method is capable of identifying ballast fouling and shoulder instability. • A threshold value is suggested for the test conditions. In railroad, unfavorable ballast performances (e.g., ballast fouling, loss of lateral confinement) can lead to deterioration of upper structures such as the rail and tie. Therefore, accurate and timely monitoring of ballast condition is critical for rail safety operation and effective maintenance. In this paper, a series of ballast box tests were conducted to investigate the ballast particle movement pattern inside railway ballast under different ballast, loading, moisture, and shoulder confinement conditions. Eight wireless embedded devices – "SmartRocks" – were used in the laboratory tests in three different locations to study different ballast movement patterns under different conditions. A statistical Autoregressive (AR) model with X-bar control chart method was used to identify changes in particle movement patterns under different conditions. The results show that 1) the ballast particle movements are much more sensitive to moisture content for the fouled ballast than for the clean ballast; and 2) the AR model is capable of identifying ballast fouling and shoulder instability. In addition, a threshold value of 20% for the percentage of outliers of ballast particle movement patterns is suggested for the test conditions considered in this study. This study represents a preliminary step towards developing a reliable ballast condition identification index and further field studies are needed. [ABSTRACT FROM AUTHOR]

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).

Track performance is largely dependent on ballast performance. Unfavorable ballast conditions cause track geometry roughness and can contribute to increased rates of damage and deterioration to the rail, tie, and fastening components. Recent discrete element modeling (DEM) studies demonstrated a strong relationship between individual ballast particle movement and overall ballast performance. This paper presents a laboratory and numerical study on ballast particle movement under cyclic loading. In the laboratory test, a wireless device called SmartRock was embedded in a ballast box to monitor individual ballast particle movement beneath a crosstie under cyclic loading. In the numerical study, an image-aided DEM approach was utilized to generate DEM particles with a realistic shape of ballast particles and simulate the ballast box test. The laboratory test results recorded by the SmartRock and DEM simulations results were compared. Good agreement was observed between the simulated and recorded particle motion in terms of peak vertical, horizontal, and angular accelerations during ballast deformation. The results indicate that horizontal movement and rotation are important modes of motion for ballast particles under cyclic loading. The SmartRock is also shown to be capable of recording real-time particle movement including translation and rotation and, thus, can be used as a fundamental research and monitoring tool in railroads. [ABSTRACT FROM AUTHOR]

For gyratory compaction, the concept of the locking point was initially developed to identify the compactability of asphalt mixes and to alleviate potential aggregate crushing in the mold. Most previous studies on the locking point were based on specimens' height change. Recent studies have indicated that the gyratory locking point of cold mix asphalt mixtures could be determined by the rotation angle range indicator using SmartRock. However, height or rotation angle change ultimately reflects a change in volume. Additionally, there is no clear physical and mechanical connection between the volume change and the gyratory locking point. In this paper, a stone mastic asphalt mixture (SMA 13) was selected for gyratory compaction applying various compaction temperatures. The compaction data were recorded by a SmartRock embedded in different positions. Collected data included stress, rotation angle, and acceleration. The major findings are as follows: (1) the specimen's locking point could be determined based on a representative stress value when the SmartRock was embedded in the specimen's center, and the results are close to the traditional evaluation results (LP3 or LP2-2-3); (2) the representative rotation angle value reached a plateau earlier than the representative stress value; (3) the representative acceleration value is not suitable for characterizing the interlocking process during gyratory compaction. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]