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Automated crack identification in structures using acoustic waveforms and deep learning

Structural elements undergo multiple levels of damage at various locations due to environments and critical loading conditions. The level of damage and its location can be predicted using acoustic emission (AE) waveforms that are captured from the generation of inherent microcracks. Existing AE methods are reliant on the feature selection of the captured waveforms and may be subjective in nature. To automate this process, this paper proposes a deep-learning model to predict the damage severity and its expected location using AE waveforms. The model is based on a densely connected convolutional neural network (CNN) that offers superior feature extraction and minimal training data requirements. Time-domain AE waveforms are used as inputs of the proposed model to automate the process of predicting the severity of damage and identifying the expected location of the damage in structural elements. The proposed approach is validated using AE data collected from a concrete beam and a wooden beam and plate. The results show the capability of the proposed method for predicting the level of damage with an accuracy range of 92-95% and identifying the approximate location of damage with 90-100% accuracy. Thus, the proposed method serves as a robust technique for damage severity prediction and localization in civil structures.

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Inspection prioritization of gravity sanitary sewer systems using supervised machine learning algorithms

Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.

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Numerical investigation on the deformation of railway embankment under normal faulting

Active faults in the earthquake region are consistently regarded as a potential geological hazard to the construction and operation of railway engineering. However, the effects of normal faulting on railway embankments have not been investigated thoroughly. For bridging this knowledge gap, three-dimensional finite element analysis considering the influence of faulting offset, the soil layer’s thickness, the fault dip angle and the embankment cross-fault angle are conducted to clarify the normal faulting effects on the railway embankment. Emphasis is given to the stress and strain characteristic in the fault rupture outcropping regions on the embankment, the deformation of the embankment centerline for design purposes, and the determination of the affected zones for railway embankment preservation. The analysis shows that the normal fault rupture outcropping regions on railway embankment are tensile yield in most cases. The existence of the soil layer and its thickening would widen the affected zones and the regions where the fault ruptures outcrops. The fault dip angle and the cross-fault angle of the embankment have a complex effect on the behaviors of the crossing embankment. The depth of the subsidence zone of the embankment would increase with the decrease of the fault dip angle and the large fault dip angle would change the primary fault rupture to be a compressive one directly above the fault line. If the embankment crosses the fault line obliquely, the curvature radius of the centerline would hardly meet the design code.

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Evaluation of the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to ultrasonic measurements

The use of reinforced concrete is foundational to modern infrastructure. Acknowledging this, it is imperative that health monitoring techniques be in place to study corrosion within these structures. By using a non-destructive method for detecting the early formation of cracks within reinforced concrete, the method presented in this paper seeks to improve upon traditional techniques of monitoring corrosion, within reinforced concrete structures. In this paper, the authors present a method to evaluate the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to a set of ultrasonic measurements, which constitutes a novel approach to the problem of observing the impact of corroding rebars and resulting concrete damage. A non-contact ultrasonic transducer is operated at a carrier frequency of 500 [kHz], with a layer of saltwater separating the sensor from the concrete surface. Following this non-contact measurement collection of the surface and rebar echo responses, a poro-elastic model is used to model the sound propagation, through an adapted version of the Biot-Stoll model. At first, a set of default parameters, obtained from the physical characteristics of the reinforced concrete, are used to match experimental and simulated acoustic signature of the sample. Performing statistical averaging along the corroding rebar within three samples over a period of nearly nine months, a small but monotonous increase in the distance between the concrete surface and the top of the rebar, indicating gradual corrosion of the rebar. Next, a non-linear optimization algorithm is used to optimize the match between measured and simulated echoes. Through the implementation of this model parameter optimization, the root mean square error between measured and simulated responses was reduced by 63.7% for the full signal, and 62.6% for the rebar echo.

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Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons

AbstractThe operational characteristics of freight shipment will significantly change after the implementation of Autonomous and Connected Trucks (ACT). This change will have a significant impact on freight mobility, transportation safety, and the sustainability of infrastructure. Truck platooning is an emerging truck configuration that is expected to become operational in the future due to the rapid advancements in connected vehicle technology and autonomous driving assistance. The platooning configuration enables trucks to be connected with themselves and the surrounding infrastructure. This arrangement has shown to be a promising solution to improve the vehicles’ fuel efficiency, reduce carbon dioxide emission, reduce traffic congestion, and improve transportation service. However, platooning may accelerate the damage accumulation of pavement and bridge structures due to the formation of multiple load axles within each platoon since those structures were not designed for such loads. According to AASHTO, bridges are designed based on a notional live load model comprised of one or two trucks per lane in conjunction with or separate from an applied uniform load (AASHTO, LRFD 2022). This damage, if accumulated, its repair would require billions of dollars from the government and would impede the movement of both people and goods. The potential damage to infrastructure may arise due to various factors such as the number of trucks in a platoon, gap spacing between trucks, and the type of trucks. This research work includes a thorough parametric study with 295,200 computer simulations using SAP 2000. The goal was to evaluate the effect of different truck platooning configurations on the load rating of existing bridges. The obtained results served as the dataset for training various machine learning models, including Random Tree, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). Results showed that Random Forest model performed the best, with the lowest prediction errors. The proposed machine learning model has shown its effectiveness in identifying optimal platooning configurations for bridge structures within the scope of the study. Graphical Abstract

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Modeling retroreflectivity degradation of pavement markings across the US with advanced machine learning algorithms

Retroreflectivity is the primary metric that controls the visibility of pavement markings during nighttime and in adverse weather conditions. Maintaining the minimum level of retroreflectivity as specified by Federal Highway Administration (FHWA) is crucial to ensure safety for motorists. The key objective of this study was to develop robust retroreflectivity prediction models that can be used by transportation agencies to reliably predict the retroreflectivity of their pavement markings utilizing the initially measured retroreflectivity and other key project conditions. A total of 49,632 transverse skip retroreflectivity measurements of seven types of marking materials were retrieved from the eight most recent test decks covered under the National Transportation Product Evaluation Program (NTPEP). Decision Tree (DT) and Artificial Neural Network (ANN) algorithms were considered for developing performance prediction models to estimate retroreflectivity at different prediction horizons for up to three years. The models were trained with randomly selected 80% data points and tested with the remaining 20% data points. Sequential ANN models exhibited better performance with the testing data than the sequential DT models. The training and testing R2 ranges of the sequential ANN models were from 0.76 to 0.96 and 0.55 to 0.94, respectively, which were significantly higher than the R2 range (0.14 to 0.75) from the regression models proposed in past studies. Initial or predicted retroreflectivity, snowfall, and traffic were found to be the most important inputs to model predictions.

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The mechanism of spontaneous corrugation on the snowy and icy roads produced by the moving vehicles in cold regions

Traffic safety in cold regions is seriously affected by the snow and ice brought by the extreme climate. The snowy and icy road cannot provide enough friction for the safe operation of vehicles due to its smooth and uneven surface. In this research, we are going to focus on the uneven corrugation occurred on snowy and icy roads and to investigate the formation mechanism of this spontaneous corrugation which can seriously threaten the traffic safety. According to field observations, we found that the corrugation phenomenon generated by moving vehicles is a complicated thermal–mechanical coupled process. In order to simplify this complicated process, we are going to focus on the mechanical process of the formation of spontaneous corrugation only at this stage. Field observation by time-lapse cameras has been conducted to disclose its forming process directly. Then, we adopted sand as the material to reproduce the spontaneous corrugation in the laboratory which can eliminate the influence of the thermal process. By considering the compressibility and mobility of the surface material comprehensively, a numerical model has been successfully constructed for imitating the forming process of corrugation. Then based on this proposed numerical model, a preliminary discussion on the influence of natural frequency on the number of the corrugation has been conducted. The relationship between the natural frequency which is decided by the vehicle itself and the corrugation is promising to be utilized in optimizing the vehicle design to improve the performance on the snowy and icy roads.

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Corrosion of carbon steel rebar in binary blended concrete with accelerated chloride transport

Samples with two different binary blended concrete mixes were prepared, one containing cement replacement of 50% slag (referred here as SL mix) and the other containing cement replacement of 20% fly ash (termed here as FA mix). The water to cementitious ratio used to produce concrete specimens was 0.41. On the top surface of each specimen, various reservoir lengths that ranged from 2.5 cm to 17.5 cm were fitted, and these reservoirs were filled with a 10% NaCl solution. Electromigration was used to accelerate the transport of chlorides, with an applied potential of 9 V at first, and subsequently reduced to 3 V after about a week. The electromigration was applied for a short period (few weeks to a couple of months). For a period of about 1100 days, the corrosion related parameters such as concrete solution resistance, rebar potential, and corrosion current were monitored via the rebar potential measurements, linear polarization resistance (LPR) and electrochemical impedance spectroscopy (EIS) measurements, the latter used only to obtain the solution resistance. The corrosion current values determined through experimental observations were then converted to mass loss using Faraday’s law. The readings of corrosion current values (last 7 sets of readings) as well as the calculated mass loss values were found to be larger for the rebars embedded in specimens prepared with SL mix, followed by rebars embedded in specimens prepared with FA mix. Corrosion current and calculated mass loss values in general tended to increase with increasing solution reservoir lengths. No cracks or corrosion products that reached the surface of the concrete were observed on the specimens for the duration of the reported monitored propagation period. This study offers a framework for future studies on accelerated steel corrosion in concrete.

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