Search Results (39)

Efficient antenna orientation of transmitters is essential for improving wireless signal quality and coverage, especially in large-scale and complex 6G networks. Identifying the best antenna angles is difficult due to the nonlinear interaction among orientation, signal propagation, and interference. This paper introduces a digital twin-based evaluation approach utilizing ray tracing simulations to assess the influence of antenna orientation on critical performance metrics: path gain, received signal strength (RSS), and signal-to-interference-plus-noise ratio (SINR). A thorough array of orientation scenarios was simulated to produce a dataset reflecting varied coverage conditions. The dataset was utilized to investigate antenna configurations that produced the optimal and suboptimal performance for each parameter. Additionally, three machine learning models—k-nearest neighbors (KNN), multi-layer perceptron (MLP), and XGBoost—were developed to forecast ideal configurations. XGBoost had superior prediction accuracy compared to the other models, as evidenced by regression outcomes and cumulative distribution function (CDF) analyses. The proposed workflow demonstrates that learning-based predictors can uncover orientation refinements that conventional grid sweeps overlook, enabling agile, interference-aware optimization. Key contributions include an end-to-end digital twin methodology for rapid what-if analysis and a systematic comparison of lightweight machine learning predictors for antenna orientation. This comprehensive method provides a pragmatic and scalable solution for the data-driven optimization of wireless systems in real-world settings. Full article

In this study, acoustic emission (AE) analysis was carried out to evaluate and quantify the cracking behavior of large-scale multi-layered reinforced concrete beams under flexural tests. Four normal concrete beams were repaired by adding a layer of crumb rubberized engineered cementitious composites (CRECCs) or powder rubberized engineered cementitious composites (PRECCs), in either the tension or compression zone of the beam. Additional three unrepaired control beams, fully cast with either normal concrete, CRECCs, or PRECCs, were tested for comparison. Flexural tests were performed on all the tested beams in conjunction with AE monitoring until failure. AE raw data obtained from the flexural testing was filtered and then analyzed to detect and assess the cracking behavior of all the tested beams. A variety of AE parameters, including number of hits and cumulative signal strength, were utilized to study the crack propagation throughout the testing. Furthermore, b-value and intensity analyses were implemented and yielded additional parameters called b-value, historic index [H (t)], and severity (S_r)_. The analysis of the changes in the AE parameters allowed the identification of the first crack in all tested beams. Moreover, varying the rubber particle size (crumb rubber or powder rubber), repair layer location, or AE sensor location showed a significant impact on the number of hits and signal amplitude. Finally, by using the results of the study, it was possible to develop a damage quantification chart that can identify different damage stages (first crack and ultimate load) related to the intensity analysis parameters (H (t) and S_r). Full article

Modified phosphogypsum (MPG) is a new type of solid waste, which could show unique mechanical properties in complex stress conditions. In this study, the effects of different loading rates (0.05, 0.1, 0.5, and 1 MPa/s) on the mechanical properties and acoustic emission (AE) characteristics of modified phosphogypsum were systematically studied through uniaxial compression tests combined with AE technology. The results showed that (1) the peak strength and elastic modulus of MPG increased as a power function of the loading rate, while the peak strain gradually decreased. (2) The cumulative event count of AE decreased as a power function with an increasing loading rate. Compared to the lowest loading rate, the cumulative event count was reduced by nearly two orders of magnitude. (3) An increase in the loading rate resulted in greater large-scale macroscopic failure in MPG specimens, along with an increased proportion of low-frequency AE signals and tensile cracks. (4) The b-value of AE decreased with an increasing loading rate, suggesting that microcrack-dominated small-scale damage prevailed at low loading rates, whereas large-scale damage became more pronounced at high loading rates. The abrupt drop in the b-value served as a precursor signal for macroscopic failure. This study presents an innovative methodology combining variable loading rates with AE technology to investigate the mechanical response of MPG, and the findings reveal the influence of the loading rate on the mechanical properties and AE characteristics of MPG, providing a theoretical basis for its engineering application under different loading environments. Full article

The instability and collapse of surrounding rock in mine goaf areas often lead to the destabilization of geological structures, surface subsidence, and mining safety accidents. To investigate the evolutionary mechanisms and precursor characteristics of rock instability and failure processes, uniaxial loading and cyclic loading–unloading tests were conducted on red sandstone using a rock mechanics loading system. These experiments aimed to explore the mechanical behavior of the rock and the development process of internal fractures. The characteristics of infrasonic signals generated during red sandstone fracturing and the laws governing damage evolution were analyzed with an infrasonic acquisition system. The research results indicate that the infrasonic signal activity generated by rock under loading conditions can be characterized by three distinct stages, namely the relative stability period, the active period, and the pre-failure precursor period. Prior to peak strength, a substantial number of infrasonic signals are generated in rocks with significant activity; this characteristic is independent of the loading path but dependent on the stress magnitude. The variation in cumulative infrasonic energy reflects the accumulation of damage in rock specimens during the loading process, and as damage accumulates, the stress–strain curve exhibits hysteresis effects and nonlinear increases, accompanied by a rapid rise in infrasonic energy. By analyzing the characteristics of infrasonic parameters and characterizing the damage and its evolutionary features in red sandstone based on infrasonic energy, the internal crack damage evolution process in rocks can be effectively characterized. This approach provides theoretical foundations and technical support for early warning and monitoring prior to rock failure. Full article

This study investigated the changes in the acoustic emission (AE) activity emitted in fiber-reinforced concrete (FRC) under flexure at two temperatures (25 °C and −20 °C). Seven concrete mixtures were developed with different water-binder ratios (w/b) (0.4 and 0.55), different fiber materials (steel fiber (SF) and synthetic polypropylene fiber (Syn-PF)), different fiber lengths (19 mm and 38 mm), and various Syn-PF contents (0%, 0.2%, and 1%). Prisms with dimensions of 100 × 100 × 400 mm from each mixture underwent a four-point monotonic flexure load while collecting the emitted acoustic waves via attached AE sensors. AE parameter-based analyses, including b-value, improved b-value (Ib-value), intensity, and rise time/average signal amplitude (RA) analyses, were performed using the raw AE data to highlight the change in the AE activity associated with different stages of damage (micro- and macro-cracking). The results showed that the number of hits, average frequency, cumulative signal strength (CSS), and energy were higher for the waves released at −20 °C compared to those obtained at 25 °C. The onset of the first visible micro- and macro-cracks was noticed to be associated with a significant spike in CSS, historic index (H (t)), severity (S_r) curves, a noticeable dip in the b-value curve, and a compression in bellows/fluctuations of the Ib-value curve for both testing temperatures. In addition, time and load thresholds of micro- and macro-cracks increased when samples were cooled down and tested at −20 °C, especially in the mixtures with higher w/b, longer fibers, and lower fiber content. This improvement in mechanical performance and cracking threshold limits was associated with higher AE activity in terms of an overall increase in CSS, S_r, and H (t) values and an overall reduction in b-values. In addition, varying the concrete mixture design parameters, including the w/b ratio as well as fiber type, content, and length, showed a significant impact on the flexural behavior and the AE activity of the tested mixtures at both temperatures (25 °C and −20 °C). Intensity and RA analysis parameters allowed the development of two charts to characterize the detected AE events, whether associated with micro- and macro-cracks considering the temperature effect. Full article

Acoustic emission (AE) analysis was utilized to assess the cracking behavior of six lightweight self-consolidating concrete (LWSCC)–engineering cementitious composite (ECC) beams under flexural loading. Two control beams were fully cast with ECC containing either polyvinyl alcohol (PVA) fibers or steel fibers (SF). The remaining four beams were ECC-LWSCC composite beams, with the ECC layer containing PVA fibers or SF placed on either the tension or compression side. The results showed that the control beams had the highest ultimate load capacity, followed by beams repaired in tension, and then beams repaired in compression. PVA fibers exhibited higher performance compared to steel fibers at the first crack load, while steel fibers enhanced the beam’s performance at the ultimate load stage. During the flexural testing, AE parameters such as the number of hits, signal amplitude, and cumulative signal strength (CSS) were collected until failure. The analysis of these AE parameters was effective in detecting the first crack and evaluating cracking propagation in all beams. Changing the type of fibers (PVA and SF) in the ECC layer showed a significant effect on AE parameters. Moreover, adding a new ECC layer to an existing LWSCC layer resulted in variations in the signal amplitude. Finally, the flexural failure mode was confirmed with the aid of the rise time/maximum amplitude vs. average frequency analysis. Full article

Accurate landslide displacement prediction is an essential prerequisite for early warning systems aimed at mitigating geological hazards. However, the inherent nonlinearity and dynamic complexity of landslide evolution often hinder forecasting performance. Previous studies have frequently combined signal decomposition techniques with individual machine learning methods to enhance prediction reliability. To address the limitations and uncertainties associated with individual models, this study presents a hybrid framework for displacement forecasting that combines variational mode decomposition (VMD) with multiple deep learning (DL) methods, including long short-term memory neural network (LSTM), gated recurrent unit neural network (GRU), and convolutional neural network (CNN), using a cloud model-based weighted strategy. Specifically, VMD decomposes cumulative displacement data into trend, periodic, and random components, thereby reducing the non-stationarity of raw data. Separate DL networks are trained to predict each component, and the forecasts are subsequently integrated through the cloud model-based combination strategy with optimally assigned weights. The proposed approach underwent thorough validation utilizing field monitoring data from the Baishuihe landslide in the Three Gorges Reservoir (TGR) region of China. Experimental results demonstrate the framework’s capacity to effectively leverage the strengths of individual forecasting methods, achieving RMSE, MAPE, and R values of 12.63 mm, 0.46%, and 0.987 at site ZG118, and 20.50 mm, 0.52%, and 0.990 at site XD01, respectively. This combined approach substantially enhances prediction accuracy for landslides exhibiting step-like behavior. Full article

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors. Full article

New applications such as augmented reality/virtual reality (AR/VR), Internet-of-Things (IOT), autonomous mobile robot (AMR) services, etc., require high reliability and high accuracy real-time positioning and tracking of persons and devices in indoor areas. Among the different visible-light-positioning (VLP) schemes, such as proximity, time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), and received-signal-strength (RSS), the RSS scheme is relatively easy to implement. Among these VLP methods, the RSS method is simple and efficient. As the received optical power has an inverse relationship with the distance between the LED transmitter (Tx) and the photodiode (PD) receiver (Rx), position information can be estimated by studying the received optical power from different Txs. In this work, we propose and experimentally demonstrate a real-time VLP system utilizing long short-term memory neural network (LSTM-NN) with principal component analysis (PCA) to mitigate high positioning error, particularly at the positioning unit cell boundaries. Experimental results show that in a positioning unit cell of 100 × 100 × 250 cm³, the average positioning error is 5.912 cm when using LSTM-NN only. By utilizing the PCA, we can observe that the positioning accuracy can be significantly enhanced to 1.806 cm, particularly at the unit cell boundaries and cell corners, showing a positioning error reduction of 69.45%. In the cumulative distribution function (CDF) measurements, when using only the LSTM-NN model, the positioning error of 95% of the experimental data is >15 cm; while using the LSTM-NN with PCA model, the error is reduced to <5 cm. In addition, we also experimentally demonstrate that the proposed real-time VLP system can also be used to predict the direction and the trajectory of the moving Rx. Full article

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP’s contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs’ contribution, including APs’ health states and APs’ positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs’ discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance. Full article

Urban underground engineering is generally buried at a shallow depth and suffers long-term environmental water effects such as rainfall, rivers, underground pipeline leakage, and groundwater. The mechanical properties of the structures are affected by constant deterioration, which seriously hinders the safe, healthy, and sustainable development of the city. On the basis of on-site investigation of civil defense engineering, this article simulates the water environment conditions of mortar in underground engineering in the laboratory and conducts manual sample preparation in the laboratory. Then, water, H₂CO₃, NaCl, and Na₂CO₃ solution or wet–dry cycles are used to corrode the sample, respectively. A uniaxial compression test, Brazilian splitting test, analyses of the acoustic emission signals and electromagnetic signals, and magnetic imaging testing are performed, respectively. The results show that an increase in the action time of environmental water leads to a gradual increase in the uniaxial compressive strength, tensile strength, and elastic modulus of cement mortar, but it will decrease over a long period of time. Different environmental water components can also lead to a different performance of soaked mortar. The uniaxial compressive strength R, tensile strength σ_t, and elastic modulus E of mortar samples exhibit values in different solutions in the order of H₂CO₃ solution < NaCl solution < Na₂CO₃ solution < water. A moderate solution soak time can enhance the mechanical properties of the mortar, but this effect decreases at long time scales. The effect of wet–dry cycles on the mechanical properties and degradation process of mortar is significant. With the increase in wet–dry cycles, the porosity of mortar continuously increases. The cumulative ringing count, energy, amplitude, and impact number of acoustic emission signals always increase when the samples are loaded to failure. The uniaxial compressive strength, tensile strength, and elastic modulus first increase and then decrease. The experimental results lay the foundation for further investigating the performance changes in mortar under complex water environments in underground engineering. Full article

In nature, rock masses often exhibit fissures, and varying external forces lead to different rates of loading on fissured rock masses. By studying the influence of the loading rate on the mechanical properties of fractured rock mass and AE characteristic parameters, it can provide a theoretical basis for the safety and stability prediction of engineering rock mass. To investigate the influence of loading rates on fissured rock masses, this study utilizes surrogate rock specimens resembling actual rock bodies and prefabricates two fissures. By conducting uniaxial compression acoustic emission tests at different loading rates, the study explores changes in their mechanical properties and acoustic emission characteristic parameters. Research findings indicate the following: (1) Prefabricated fissures adversely affect the stability of specimens, resulting in lower strength compared to intact specimens. Under the same fissure inclination angle, peak strength, elastic modulus, and loading rate exhibit a positive correlation. When the fissure inclination angle varies from 0° to 60° under the same loading rate, the peak strength of specimens generally follows a “V”-shaped trend, decreasing initially and then increasing, with the minimum peak strength observed at α = 30°. (2) Prefabricated fissure specimens primarily develop tensile cracks during loading, gradually transitioning to shear cracks, ultimately leading to shear failure. (3) The variation patterns of AE (acoustic emission) characteristic parameters under the influence of loading rate differ: AE event count, AE energy, and cumulative AE energy show a positive correlation with loading rate, while cumulative AE event count gradually decreases with increasing loading rate. (4) AE characteristic parameters exhibit good correlation with the stress–strain curve and can be divided into four stages. The changes in AE characteristic parameters correspond to the changes in the stress–strain curve. With increasing loading rate, AE signals in the first three stages gradually stabilize, focusing more on the fourth stage, namely the post-peak stage, where the specimens typically experience maximum AE signals accompanying final failure. Full article

Nuclear power is a high-quality clean energy source, but nuclear waste is generated during operation. The waste continuously releases heat during disposal, increasing the adjoining rock temperature and affecting the safety of the disposal site. Basalt is widely considered a commonly used rock type in the repository. This study of basalt’s mechanical characteristics and damage evolution after thermal damage, with its far-reaching engineering value, was conducted by combining experimental work and theory. Uniaxial compression tests were conducted on basalt exposed to 25 °C, 500 °C, 700 °C, 900 °C, and 1100 °C conditions, and acoustic emission (AE) equipment was utilized to observe the acoustic emission phenomenon during deformation. This study was carried out to examine the mechanical characteristics, the sound emission features, the progression of damage laws, and the stress–strain framework of basalt after exposure to different types of thermal harm. As the temperature rises, the rock’s maximum strength declines steadily, the peak strain rises in tandem, the rock sample’s ductility is augmented, the failure mode changes from shear to tensile failure, and cracks in the failure area are observed. At room temperature, the acoustic emission signal is more vigorous than in the initial stage of rock sample loading due to thermal damage; however, after the linear elastic stage is entered, its activity is lessened. In cases where the rock approaches collapse, there is a significant surge in acoustic emission activity, leading to the peak frequency of acoustic emission ringing. The cumulative ring count of acoustic emission serves as the basis for the definition of the damage variable. At room temperature, the damage evolution of rock samples can be broken down into four distinct stages. This defined damage variable is more reflective of the entire failure process. After exposure to high temperatures, the initial damage of the rock sample becomes more extensive, and the damage variable tends to be stable with strain evolution. The stress–strain constitutive model of basalt deformation is derived based on the crack axial strain law and acoustic emission parameters. A powerful relationship between theoretical and experimental curves is evident. Full article

In this paper, mobile devices were used to estimate the received signal strength indicator (RSSI) of wireless channels with three wireless access points (APs). Using the RSSI, the path loss exponent (PLE) was adapted to calculate the estimated distance among the test points (TPs) and the APs, through the root mean square error (RMSE). Moreover, in this paper, the proposed adaptive PLE (APLE) of the TPs was obtained by minimizing the positioning errors of the PLEs. The training samples of RSSI were measured by TPs for 6 days, and different surge processing methods were used to obtain APLE and to improve the positioning accuracy. The surge signals of RSSI were reduced by the cumulated distribution function (CDF), hybrid Kalman filter (KF), and threshold filtering methods, integrating training samples and APLE. The experimental results show that with the proposed APLE, the position accuracy can be improved by 50% compared to the free space model for six TPs. Finally, dynamic real-time indoor positioning was performed and measured for the performance evaluation of the proposed APLE models. The experimental results show that, the minimum dynamic real-time positioning error can be improved to 0.88 m in a straight-line case with the hybrid method. Moreover, the average positioning error of dynamic real-time indoor positioning can be reduced to 1.15 m using the four methods with the proposed APLE. Full article

Optimizing the mechanical characteristics of cemented tailings backfill (CTB) and quickly identifying its damage state under external loading, this study compares and prepares CTB specimens without fiber, doped with polypropylene fiber (PF), doped with glass fiber (BL), and doped with polypropylene and glass blended fiber (PB). Uniaxial compression and acoustic emission (AE) monitoring experiments are also conducted. Based on the cumulative energy of AE, the damage ontology model of CTB was developed. As shown by the study’s findings, adding various fibers can greatly enhance the filler body’s uniaxial compressive strength (UCS). BL has the greatest effect, followed by PB, while PFs have the least effect. Furthermore, the fibers primarily prevent the growth of crack extension by extending or breaking themselves, The results of the tests on acoustic emission revealed that the fiberless filler’s signals were more active prior to the peak point and less intense in the later stages of the damage, whereas the fiber-doped filler’s signals began to increase following the peak point and remained high. Thus, the damage model curves of various fiber-filled bodies are constructed based on the cumulative energy of acoustic emission, and the experimental data verification shows that the two have good consistency, suggesting that the established theoretical model can serve as a basis of reference for assessing the filled bodies’ damage state. Full article