<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7QWNvdXN0aWMgZW1pc3Npb24mcXVvdDsiIG9yaWdpbmFsX3ZhbHVlPSImcXVvdDtBY291c3RpYyBlbWlzc2lvbiZxdW90OyIgLz48L2ZpbHRlcnM+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Low-temperature self-healing of SBS-modified asphalt mixtures via acoustic emission and semicircular bending tests</title>
      <link>https://trid.trb.org/View/2643506</link>
      <description><![CDATA[This study investigated the low-temperature self-healing behaviour of asphalt mixtures modified with 5% Styrene–Butadiene-Styrene (SBS) and subjected to 10 freeze–thaw cycles, with and without a 12-hour resting period between cycles. Acoustic Emission and semicircular bending tests were conducted under three fracture modes (Mode I, Mode II, and mixed Mode I/II) at temperatures of −12°C. The Acoustic Emission tests revealed that SBS modification enhanced the self-healing index by 16% in samples with resting time and by 11% in those without. The semicircular bending tests showed that the healing index varied depending on resting time, mixture type, and fracture mode. SBS-modified mixtures demonstrated improvements of approximately 15% in fracture energy, 10% in secant modulus, 11% in stress intensity factor, and 12% in flexibility index compared to control samples. A comparison of the Acoustic Emission and semicircular bending test results indicated that the resting period in mixed Mode I/II fracture aligns with the findings from the Acoustic Emission tests.]]></description>
      <pubDate>Sun, 22 Feb 2026 14:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643506</guid>
    </item>
    <item>
      <title>Adaptive stepsize forward–backward pursuit and acoustic emission-based health state assessment of high-speed train bearings</title>
      <link>https://trid.trb.org/View/2611146</link>
      <description><![CDATA[Compressed sensing (CS) is a promising tool for data compression reconstruction. However, fault diagnosis methods for high-speed train bearings based on CS and acoustic emission (AE) technologies have not been reported yet. Notably, the accuracy and speed of CS two-stage reconstruction methods are affected and restricted by prior initial conditions. Therefore, this article proposes adaptive dynamic thresholds applicable to adaptive stepsize forward–backward pursuit (ASFBP), and bearing health state assessment method. First, the adaptive dynamic thresholds for atom selection and deletion are constructed based on the residual feedback mechanism and the atom quality distribution law, which enables ASFBP to realize high-precision rapid reconstruction of signal without any atom priori initial conditions. Second, the initial dictionary length is improved based on the AE hit characteristics. Furthermore, a damage state comprehensive evaluation index (DSCEI) is established using principal component analysis based on AE time-domain hit parameters and compression-domain energy parameter. Compared with the kurtosis index and permutation entropy index, the DSCEI demonstrates better monotonicity and stability in the quantitative evaluation of high-speed train bearing condition. Finally, the validity and stability of the method are verified by testing under complex test conditions resembling actual high-speed train lines, providing valuable insights for the CS-based data-driven bearing fault diagnosis.]]></description>
      <pubDate>Wed, 21 Jan 2026 15:36:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611146</guid>
    </item>
    <item>
      <title>Study on mechanical performance and damage characteristics of Semi-Flexible Pavement materials based on acoustic emission tests</title>
      <link>https://trid.trb.org/View/2624727</link>
      <description><![CDATA[Semi-Flexible Pavement (SFP) material is a new type of pavement material that combines the flexibility of asphalt and the rigidity of cement concrete. However, the cracking problem of SFP has significantly hindered the widespread adoption of SFP. To mitigate rutting and cracking, thereby extending pavement service life, three types of SFP mixtures were designed and prepared. Road performance test, three-point bending test and splitting test were conducted. The road performance and damage characteristics of the SFP materials were analyzed based on acoustic emission (AE) signal features. The results indicate that the steel slag powder based expansive agents type Semi-Flexible Pavement (SSP-SFP) material demonstrates superior overall road performance. A positive correlation was observed between the mechanical properties (strength and stiffness) of the SFP materials and the strength of the Cement-based Grouting Material (CGM). The SSP-SFP exhibited minimal damage at the interface and possessed the highest bond strength. Additionally, under three-point bending and splitting loads, the resistance to tensile failure of the three SFP types ranked as follows: ordinary SFP < U-type expansive agent SFP < SSP-SFP. Furthermore, the tensile failure resistance of the SFP material under splitting loading is higher than that under three-point bending loading. The damage process during the three-point bending and splitting tests can be divided into three and four stages for analysis, respectively. Based on a comprehensive comparison, the SSP-SFP mixture demonstrated the most favorable AE characteristics. Given its exceptional crack resistance, this material is highly suitable for the construction of perpetual pavements.]]></description>
      <pubDate>Wed, 07 Jan 2026 09:09:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624727</guid>
    </item>
    <item>
      <title>Innovative prognostic methodology for pipe defect detection leveraging acoustic emissions analysis and computational modeling integration</title>
      <link>https://trid.trb.org/View/2631044</link>
      <description><![CDATA[Pipelines play a crucial role in transporting essential resources such as water, oil, and gas across industrial, urban, and environmental infrastructures. Clogging remains a persistent challenge, potentially resulting in catastrophic failures, operational disruptions, increased maintenance costs, and serious safety risks. This study presents a novel prognostic and health monitoring approach that utilizes bubble-induced acoustic emissions to detect and characterize pipeline blockages. An analytical model is developed to capture the acoustic signatures of detaching bubbles, revealing features highly sensitive to clogging. Finite element simulations using Abaqus further investigate how different clogging conditions affect acoustic wave propagation. These insights drive the development of a machine learning-based predictive maintenance strategy, validated on real-world datasets. The results demonstrate exceptional accuracy, with most classifiers achieving 100% detection rates for clogging presence, shape, and severity. Additionally, model generalization tests show that machine learning algorithms adapt more effectively to varying clogging thickness than clogging shape. This research paves the way for a highly accurate, non-destructive monitoring solution, enhancing predictive maintenance and ensuring the reliability of industrial pipelines.]]></description>
      <pubDate>Wed, 31 Dec 2025 10:58:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2631044</guid>
    </item>
    <item>
      <title>Study on the acoustic characteristics of corrosion damage of reinforced concrete materials used in highway bridges</title>
      <link>https://trid.trb.org/View/2633287</link>
      <description><![CDATA[Reinforced concrete is a critical component of concrete bridges. This study employs an embedded cement-based acoustic emission (AE) sensor to investigate the relationship between corrosion rate, durability and acoustic emission characteristics of the samples, based on the theory of corrosion stress waves. At the initial stage of corrosion, variations in corrosion rate have the most significant impact on the AE characteristics of the samples. When the corrosion rate increased from 5 to 15 V, the abrupt change time of cumulative AE energy advanced by 2 days. This trend was primarily determined by the failure mode at each corrosion stage and was minimally influenced by concrete strength. As the degree of corrosion increased, the peak main frequency shifted from 250 to 50 kHz, while the waveform peak amplitude rose from 0.53 mV to 1.87 mV. A concrete bridge damage risk warning model was developed by utilizing cumulative energy transition points as warning thresholds, integrated with the correlation between specimen durability and AE characteristics. This model enables the assessment of deterioration of reinforced concrete deterioration under varying corrosion rates, offering a novel technical approach for disaster early warning and rational maintenance strategies for highway bridges.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633287</guid>
    </item>
    <item>
      <title>Damage Evolution Mechanism of Hydraulic Asphalt Concrete at Different Temperatures Using the Acoustic Emission Technique</title>
      <link>https://trid.trb.org/View/2606372</link>
      <description><![CDATA[Previous research on the damage evolution of hydraulic asphalt concrete (HAC) was mainly conducted using imaging techniques that are insufficient for describing the dynamic development of internal cracks. Therefore, by combining uniaxial compression testing with acoustic emission (AE) technology, the present study investigates the damage evolution mechanism of HAC in different temperature conditions. The locations of damage at different temperatures within the specimens were identified, the dynamic evolution of cracks was tracked, and the crack types were classified. The results show that HAC exhibits brittle failure between −20°C and 0°C and ductile failure between 10°C and 30°C. A critical AE amplitude of 75 dB was found to vary between ductile and brittle failure. At lower temperatures (−20°C to 0°C), microcracks appear early in the loading process and expand in an unstable phase, with numerous microcracks present throughout the entire loading process. In contrast, at temperatures between 10°C and 30°C, microcracks begin to form after a period of loading and propagate uniformly within the specimen. Shear cracks were predominant under low-temperature conditions, whereas tensile cracks dominated at higher temperatures. These findings provide insights into improving the safety and durability of asphalt concrete in panel dams.]]></description>
      <pubDate>Fri, 05 Dec 2025 14:12:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606372</guid>
    </item>
    <item>
      <title>Acoustic Emission Monitoring and Localization Method for Broken Wires in 13 In-Service Stay Cables Connected by a 165-m-Long Waveguide Rod</title>
      <link>https://trid.trb.org/View/2611167</link>
      <description><![CDATA[The stay cable is one of the crucial structures of a cable-stayed bridge, and the occurrence of broken wires in cables may endanger the safety of the entire bridge. Currently, the acoustic emission (AE) monitoring method for detecting broken wires of cables requires a significant number of sensors, which leads to high monitoring costs. To reduce the monitoring cost and the number of sensors for cable monitoring, this study used a 165-m-long waveguide rod to connect 13 cables on the Yellow River Super Bridge of Anluo Expressway in Henan Province, China, and carried out experiments on the acoustic emission attenuation of the waveguide rod and the arrangement of sensors. The acoustic emission attenuation law of rigid and flexible waveguide rods with a length of 165 m was studied, the difference in the AE sensor arrangement on the side and cross section of the waveguide rod was analyzed, and the arrangement of AE sensors was optimized. Two AE sensors were arranged at both ends of the waveguide rod to locate the cable with broken wires. The results indicate that the acoustic emission amplitude of the 165-m-long rigid waveguide rod is the characteristic parameter with the smallest attenuation compared with duration, ringing count, and energy. The attenuation of the acoustic emission amplitude of the 165-m-long flexible waveguide rod is greater than that of the rigid waveguide rod under the same length, while the attenuation rates of time duration, ringing count, and energy are less than those of the rigid waveguide rod. Under the comprehensive comparison, the two types of waveguide rods show lower attenuation in different characteristic parameters and are suitable for various application scenarios; therefore, one can reasonably choose the type of waveguide rods according to the actual structure of the cable, monitoring needs, and the installation environment. The AE sensor arranged on the cross section of the waveguide rod is more conducive to monitoring the broken wires of the cables. A 165-m-long waveguide rod is used to connect 13 cables, and a sensor arranged at both ends of the waveguide rod can determine cables with broken wires.]]></description>
      <pubDate>Wed, 19 Nov 2025 17:09:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611167</guid>
    </item>
    <item>
      <title>A Method for Arrival Time Picking of Acoustic Emission Signals Based on Convolutional Neural Networks</title>
      <link>https://trid.trb.org/View/2606607</link>
      <description><![CDATA[To achieve an accurate pickup of acoustic emission (AE) signal arrival time, a convolutional neural network (CNN)-based AE arrival time pickup method is proposed. The positioning results of the concrete lead fracture test were used to verify the arrival time picking accuracy of the CNN algorithm, and it was compared with the threshold-based arrival time extraction algorithm. The results showed that after applying the CNN algorithm for correction, the number of AE events increased. The distribution of positioning points after arrival time correction more accurately reflects the actual pattern, with clearer spacing between vertical lines and points clustered near grid points. From the error distribution map, the average positioning error using threshold-based arrival time data is relatively large, with more yellow points on the map and usually lighter colors. The CNN model significantly reduced the average error of lead fracture points, and the corrected average positioning error at the time was reduced by about 6.8?mm, achieving excellent results. These findings can provide reference for the selection of AE signal arrival time.]]></description>
      <pubDate>Fri, 10 Oct 2025 16:30:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606607</guid>
    </item>
    <item>
      <title>Experimental study on damage evolution characteristics of concrete beams embedded with acceleration sensor under cyclic loading</title>
      <link>https://trid.trb.org/View/2592477</link>
      <description><![CDATA[Embedded sensors play a crucial·role in long-term health monitoring of cement concrete pavements. Considering that flexural strength is the primary design criterion for cement concrete pavements, a three-point bending fracture test is conducted on concrete beams embedded with acceleration sensors under a cyclic loading regime, this study compares and analyzes the influence of a pre-notch at the bottom of the beam on the mechanical properties of concrete under cyclic loading. The damage evolution characteristics of the concrete beams embedded with acceleration sensors during cyclic loading are systematically analyzed using methods such as acoustic emission systems, digital image correlation, and high-precision extensometers. The vibration response characteristics of the concrete beams are studied by integrating embedded acceleration sensors, and the amplitude frequency characteristics and time-frequency spectrum of acceleration signals are explored through the fast Fourier transform (FFT) and Short-Time Fourier Transform (STFT). Both the overall and local failure modes of concrete beams are analyzed, forming a multi-source data analysis method that can comprehensively characterize the damage characteristics, strain evolution, and vibration responses of concrete beam components. The research results can provide valuable reference and insights for non-destructive testing and health monitoring of concrete structures.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:55:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592477</guid>
    </item>
    <item>
      <title>A transfer learning-based framework for acoustic emission source localization of real cracks in reinforced concrete</title>
      <link>https://trid.trb.org/View/2558623</link>
      <description><![CDATA[Deep learning models have emerged as a promising solution for acoustic emission (AE) source localization, offering adaptability to composite materials like reinforced concrete. However, existing deep learning models often rely on AE data from simulated sources, such as pencil lead breakage or ball impact, which may not accurately represent real cracks, thereby limiting the performance of deep learning models. This paper proposes a novel deep learning model based on transfer learning for AE source localization of real cracks in reinforced concrete components. The proposed model consists of a source model and a target model, both built with a 1-dimensional convolutional neural network (CNN) and fully connected layers. The source model is trained on a simulated AE dataset, and its 1-dimensional CNN is transferred to the target model. The target model is then fine-tuned using limited AE data from real cracks collected during a four-point bending test. The trained model locates AE sources from real cracks by outputting a grid-based probability map. The performance of the model was compared with and without transfer learning. Additionally, the robustness of the proposed model against noise was investigated through field testing on a real bridge. The generalization performance on unseen reinforced concrete components was also examined. Additionally, t-distributed stochastic neighbor embedding was used to analyze the interpretability. The results indicate the effectiveness of the proposed model in AE source localization of real cracks in reinforced concrete components. Additionally, the model remains robust under noisy conditions, indicates its effectiveness in practical AE source localization applications.]]></description>
      <pubDate>Tue, 08 Jul 2025 09:56:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2558623</guid>
    </item>
    <item>
      <title>A leak sensing method for offshore oil pipeline using acoustic emission signals and machine learning</title>
      <link>https://trid.trb.org/View/2565205</link>
      <description><![CDATA[Ensuring the integrity and safety of pipeline systems is crucial in the oil and gas industry. Efficient and accurate detection of pipeline leaks can not only prevent pipeline failures but also significantly reduce the risk of accidents caused by leaks or explosions. This study explores pipeline leak detection technology based on acoustic emission signals. Firstly, a laboratory platform was built to simulate pipeline leak experiments on an offshore platform, collecting laboratory leak signal data and on-site noise signals from the “Liwan 3–1″ offshore platform. The leak signals were fused with the on-site noise data to approximate real conditions. Then, three machine learning-based pipeline leak detection methods in this field—Wavelet Transform - Support Vector Machine (WT-SVM), Simulated Annealing - Particle Swarm Optimization - Back Propagation Neural Network (SA-PSO-BPNN), and Sparrow Search Algorithm - Convolutional Neural Network (SSA-CNN)—were compared to validate the effectiveness of existing models. Finally, this paper proposes a new leak detection method that integrates Variational Mode Decomposition (VMD) with Gradient Boosting Decision Tree (GBDT). By optimizing signal processing with VMD technology, combining time-frequency domain feature extraction, and using the GBDT model for leak event identification and classification, a high recognition accuracy of 99.44 % was achieved.]]></description>
      <pubDate>Mon, 23 Jun 2025 08:46:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2565205</guid>
    </item>
    <item>
      <title>Acoustic Noise Reduction Using Half-Period- Switching Pseudorandom Sinusoidal Injection With External Tangent Demodulation for Sensorless PMSM Drives</title>
      <link>https://trid.trb.org/View/2553509</link>
      <description><![CDATA[High-frequency (HF) acoustic noise is unavoidable when HF injection is adopted for sensorless permanent magnet synchronous motor (PMSM) drives, which limits the application on some occasions. This article proposes a novel acoustic noise reduction method using half-period-switching pseudorandom sinusoidal injection (HPS-PRSI) with external tangent demodulation. The superiority of HPS-PRSI on acoustic noise reduction over the conventional PRSI is comparatively evaluated in terms of the power spectral density of the HF current. Furthermore, a system-delay-tolerant external tangent demodulation method is proposed to remove the demodulation signal and filters. Therefore, enhanced acoustic noise reduction with higher injection frequency can be achieved. Finally, experiments are carried out on a platform of 2.2-kW PMSM drive to verify the feasibility and effectiveness of the proposed method.]]></description>
      <pubDate>Fri, 20 Jun 2025 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553509</guid>
    </item>
    <item>
      <title>Cracking characteristics of warm-mix recycled fiber asphalt mixture under Mode I, Mode III, and mixed-Mode I/III based on acoustic emission technology</title>
      <link>https://trid.trb.org/View/2558795</link>
      <description><![CDATA[Acoustic emission (AE) technology was employed to dynamically monitor edge-notched disk bending (ENDB) tests of warm-mix recycled asphalt mixture (WRAM), WRAM reinforced with flocculent basalt fibers (WRAM-F), and WRAM reinforced with chopped basalt fibers (WRAM-C) under low-temperature conditions. To investigate the damage behavior under different fracture conditions, three cracking modes—Mode I, Mode III, and Mode I/III mixed—were simulated. Fracture toughness (Kic) was employed to evaluate the crack propagation resistance of the three asphalt mixtures. The damage evolution and crack propagation behavior of WRAM were characterized by analyzing the spatial distribution of AE signals, the evolution of peak frequencies, and their proportional distribution across the stiffness, strength, toughness, and failure zones. Ringing count analysis was used to evaluate the cracking characteristics of WRAM, and the reinforcing mechanisms of chopped basalt fiber (CBF) and flocculent basalt fiber (FBF) were compared. Scanning electron microscopy (SEM) was employed to investigate fiber dispersion and interfacial interaction mechanisms. The results indicate that the spatial distribution of AE signals can effectively reflect the crack initiation point, propagation path, and affected zones, enabling the localization and characterization of both microcracks and macrocracks. The AE peak frequencies are primarily concentrated within the 0–280 kHz range and exhibit a banded distribution pattern. The ringing count exhibits stage-wise variation, and its abrupt changes can serve as early warning signal of impending failure. Owing to its excellent asphalt adsorption capacity, light weight, and varied fiber lengths, FBF can form a well-developed three-dimensional network structure within the mixture. This network enhances the overall mechanical properties and stress distribution capability of the composite, thereby effectively delaying crack propagation. In contrast, CBF has a single fiber length and a smooth surface, and primarily contributes to toughening by hindering crack propagation after the formation of macrocracks. However, its effectiveness in improving the crack resistance of WRAM is inferior to that of FBF.]]></description>
      <pubDate>Fri, 20 Jun 2025 11:58:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2558795</guid>
    </item>
    <item>
      <title>A study of the effect of grinding machine parameters on acoustic rail roughness and surface quality</title>
      <link>https://trid.trb.org/View/2543119</link>
      <description><![CDATA[Rail grinding is performed by infrastructure managers to control, reduce or prevent the growth of rail defects, such as rolling contact fatigue and corrugation. This is done using preventive methods (to attempt to prevent defects from forming) or corrective methods (to remove defects present in the rail). Trials were undertaken on preventive rail grinding machines used by Network Rail, with the aim of improving the finished quality of the rail whilst still achieving the metal removal and reprofiling required. An important aspect considered in the trials was the acoustic rail roughness and its relationship with grinding surface quality indices. The results demonstrated that, in the case of the operational machines used by Network Rail, the largest impact on the overall surface quality was the age and conditioning of the grinding stones. The trials also demonstrated the differences in Standard requirements for achieving good surface quality indices for grinding and good acoustic roughness levels. They further highlighted the importance of identifying rail corrugation prior to preventive grinding to reduce the likelihood of the grinding signature increasing roughness at corrugation wavelengths.]]></description>
      <pubDate>Tue, 27 May 2025 09:31:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543119</guid>
    </item>
    <item>
      <title>Noise emissions in electric drive vehicles on the example of the Dacia spring electric car</title>
      <link>https://trid.trb.org/View/2519008</link>
      <description><![CDATA[Conventionally powered cars produce noise from the combustion engine and exhaust system. Electric cars do not have an internal combustion engine, and therefore no intake, exhaust or gearbox, so noise is generated mainly as a result of variable electromagnetic forces accompanying the conversion of electrical energy into mechanical energy. All vehicles equipped with both conventional and diesel engines also emit noise as a result of the interaction of the tyres with the road surface and from the air flowing over the body (aerodynamic noise). The article measures noise inside and outside an electric vehicle. The measurements were carried out in city and highway traffic conditions. Research has shown that the main source of noise is the interaction of tyres with the road surface and aerodynamic noise.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2519008</guid>
    </item>
  </channel>
</rss>