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Deformation is the most direct indicator of structural state changes in arch dams. Therefore, numerous deformation monitoring points are typically arranged on arch dams to obtain deformation data from each point. Considering the complex relationships between the deformation at each monitoring point, this study focuses on the internal structural relationships and information fusion within the dam. The Pearson correlation coefficient is used as a similarity index to determine significant linear correlations between the measuring points. Ward's cluster analysis method is then applied to group these points based on their similarities. To identify measuring points with strong nonlinear correlations, the Maximum Information Coefficient (MIC) method is employed. By integrating these linear and nonlinear correlations, a model is constructed to characterize the deformation at specific measurement points using data from strongly correlated points. The effectiveness of this model is verified through a concrete engineering case study, offering a novel approach for analyzing arch dam deformations. [ABSTRACT FROM AUTHOR]

Rapid urbanization and industrialization have caused water quality issues in urban rivers. Appropriate measures based on water quality monitoring systems and prediction methods are needed for water quality management. While South Korea has operated a water quality monitoring system that measures various environmental factors and has accumulated water quality data, a water quality prediction system is not in place. This study suggests a water quality prediction method based on a long short-term model using water quality and meteorological monitoring data. Additionally, we present a derived input set of the prediction model that can improve the prediction model performance. The prediction model's performance was evaluated by the coefficient of determination under various conditions, such as the hyperparameters, temporal resolution of input data, and application of upstream and downstream data. As a result, using the temporal resolution of the input data as hourly data improved predictions by an average of 25.6% over three days of the prediction period compared to daily data. Meanwhile, it was analyzed that the hyperparameters and using upstream and downstream data have a minor effect on the model performance. The results of this study underscore the crucial role of the number, duration, and temporal resolution of available monitoring data in water quality management. [ABSTRACT FROM AUTHOR]

Summary: Acceleration measurements are often used for model updating of civil engineering structures, especially in the case of seismic monitoring. It is yet unclear if accelerations alone would generate an accurate and robust finite‐element (FE) model. This study examines this notion and analyzes the possibility of using other vibration monitoring data for model updating of shear‐wall tall buildings. This study compares the accuracy and robustness of the FE models being optimized via accelerations, roof displacement, wall rotations, interstory drift ratios, and the linear combination of these measurements. A numerical case study is analyzed using Timoshenko beams for modeling the lateral vibration of a benchmark 42‐story building under seismic excitations. Results show that the acceleration response of the examined building is mostly governed by its higher vibration modes. Depending on the characteristics of ground motions, using accelerations alone may generate an FE model biased towards higher‐order modes without effectively capturing the lower‐order modes. For instance, the first modal frequency of the updated FE model could be 12.0% lower than the true value, and the reconstructed displacement and rotation responses are noticeably inaccurate. Employing multi‐source monitoring data for model updating, for example, the combinations of roof displacement and acceleration measurements, could reduce the normalized root‐mean‐square errors in displacements by more than 70%. This study also quantifies the robustness of the FE model under various measurement noise levels and 50 pairs of earthquake records. Finally, the effects of multi‐source data on FE model updating are validated via experiments on a 7‐story shear wall building. Analysis reveals that a more accurate and robust FE model can be determined via a combination of accelerations and top displacement than via acceleration alone. [ABSTRACT FROM AUTHOR]

Over the last decades, an expansion of the underground network has been taking place to cope with the increasing amount of moving people and freight. As a consequence, it is of vital importance to guarantee the full functionality of the tunnel network by means of preventive maintenance and the monitoring of the tunnel lining state over time. A new method has been developed for the real-time prediction of the utilization level in tunnel segmental linings based on input monitoring data. The new concept is founded on a framework, which encompasses an offline and an online stage. In the former, the generation of feedforward neural networks is accomplished by employing synthetically produced data. Finite element simulations of the lining structure are conducted to analyze the structural response under multiple loading conditions. The scenarios are generated by assuming ranges of variation of the model input parameters to account for the uncertainty due to the not fully determined in situ conditions. Input and target quantities are identified to better assess the structural utilization of the lining. The latter phase consists in the application of the methodological framework on input monitored data, which allows for a real-time prediction of the physical quantities deployed for the estimation of the lining utilization. The approach is validated on a full-scale test of segmental lining, where the predicted quantities are compared with the actual measurements. Finally, it is investigated the influence of artificial noise added to the training data on the overall prediction performances and the benefits along with the limits of the concept are set out. [ABSTRACT FROM AUTHOR]

Objective As the development and utilization of urban underground space advances towards the three-dimensional and multi-dimensional direction, there are more and more over-crossing and under-crossing urban rail transit projects. Among them, the close under-crossing construction of new stations with large cross-section has a significant impact on the surrounding environment. Therefore, it is urgent to carry out an in-depth study on the ground settlement and deformation law of existing stations during the new station construction process closely under-crossing existing stations by Pile-Beam-Arch (PBA) method. Method Based on the actual engineering case of Beijing Metro Line 6 Pinguoyuan Station, which closely under-crosses the existing station with same name on Metro Line 1, the law of ground settlement and deformation of the existing station is analyzed by adopting numerical simulation method in the study. By comparing the actual monitoring data, the actual situation of ground settlement and deformation of the existing station during the construction process is further verified, and the numerical simulation results are compared and analyzed with the measured data. Result & Conclusion The most unfavorable stages for ground settlement and existing structure deformation are the guide hole excavation stage and the second lining arch stage of PBA method. After the completion of second lining arch construction, both the ground settlement and the deformation of the existing structure show rebound phenomenon. In addition, the stiffness of the existing station has a significant inhibiting effect on the ground settlement above, which is manifested in the fact that the ground settlement value of the overlapping section is smaller than that of the non-overlapping section. The deformation of the existing station structure caused by new station constructed with PBA method is mainly the rigid body displacement, while it caused track structure deformation is mainly the overturning deformation, which is locally manifested as rigid body displacement.

Over the last decades, an expansion of the underground network has been taking place to cope with the increasing amount of moving people and freight. As a consequence, it is of vital importance to guarantee the full functionality of the tunnel network by means of preventive maintenance and the monitoring of the tunnel lining state over time. A new method has been developed for the real-time prediction of the utilization level in tunnel segmental linings based on input monitoring data. The new concept is founded on a framework, which encompasses an offline and an online stage. In the former, the generation of feedforward neural networks is accomplished by employing synthetically produced data. Finite element simulations of the lining structure are conducted to analyze the structural response under multiple loading conditions. The scenarios are generated by assuming ranges of variation of the model input parameters to account for the uncertainty due to the not fully determined in situ conditions. Input and target quantities are identified to better assess the structural utilization of the lining. The latter phase consists in the application of the methodological framework on input monitored data, which allows for a real-time prediction of the physical quantities deployed for the estimation of the lining utilization. The approach is validated on a full-scale test of segmental lining, where the predicted quantities are compared with the actual measurements. Finally, it is investigated the influence of artificial noise added to the training data on the overall prediction performances and the benefits along with the limits of the concept are set out.

This paper introduces a novel stochastic inverse method that utilizes perturbation theory and advanced intelligence techniques to solve the multi‐parameter identification problem of concrete dams using displacement field monitoring data. The proposed method considers the uncertainties associated with the dam displacement monitoring data, which are comprised of two distinct sources: the first is related to stochastic mechanical properties of the dam, and the second is due to observation errors. The displacements at different measuring points generated by dam mechanical properties exhibit spatial correlation, while the observation errors at different points can be considered statistically random. In this context, the inversion formulas are derived for unknown stochastic parameters of the dam by combining perturbation equations and Taylor expansion methods. An improved meta‐heuristic optimization method is employed to identify the mean of stochastic parameters, while mathematical and statistical methods are used to determine the variance of stochastic parameters. The feasibility of the proposed method is verified through numerical examples of a typical dam section under different conditions. Additionally, the paper discusses and demonstrates the applicability of this method in a practical dam project. Results indicate that this method can effectively capture the uncertainty of dam's mechanical properties and separates them from observation errors. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of pile foundation underpinning technology in a deep foundation pit of a subway Viaduct Project in Beijing. The study aims to address the engineering characteristics of the project, including a large number of new piles, a wide span of underpinning abutment, a long length of deep foundation pit, and a wide range of influences. This research utilizes field monitoring and numerical simulation methods to investigate the pile foundation underpinnings. The impact and management of road-deep foundation pit construction are considered, as well as their combined effect on subway viaducts and track structures. The primary accomplishments are as follows: (1) By analyzing the data from on-site deformation monitoring, it is evident that the pier exhibits maximum vertical deformation and maximum transverse deformation at the same location. The measuring locations are specifically situated on Pier 7# at the pile foundation underpinning. The maximum vertical and transverse deformations of the track are directly proportional to the maximum deformation of the pier. (2) By comparing the numerical simulation results with the field monitoring data, it is observed that although there is some discrepancy between the two, the deformation trend is largely consistent. This suggests that the numerical simulation analysis method is effective in reflecting the deformation of the bridge and track. (3) Through the numerical model and changing the values of the retaining structure parameters, the sensitivity of the pier deformation near the road foundation pit to the retaining structure parameters is systematically analyzed. The sensitivity of the pier deformation to the foundation pit parameters is as follows: the embedded depth insertion ratio of the retaining pile > the diameter of the retaining pile > the pile spacing. [ABSTRACT FROM AUTHOR]

Engineering constructions in coastal areas not only affect existing landslides, but also induce new landslides. Variation of the water level makes the coastal area a geological hazard-prone. Prediction of the slope displacement based on monitoring data plays an important role in early warning of potential landslide and slope failure, and supports the risk management of hazards. Given the complex characteristic of the slope deformation, we proposed a prediction model using random coefficient model under the frame of panel data analysis, so as to take the correlation among monitoring points into consideration. In addition, we classified the monitoring data using Gaussian mixture model, to take the temporal-spatial characteristics into consideration. Monitoring data of Guobu slope was used to validate the model. Results indicated that the proposed model have a better performance in prediction accuracy. We also compared the proposed model with the BP neural network model and temporal – temperature model, and found that the prediction accuracy of the proposed model is better than those of the two control models. [ABSTRACT FROM AUTHOR]

In order to investigate the analysis and processing methods for nonstationary signals generated in bridge health monitoring systems, this study combines the advantages of complete ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising algorithms to construct the CEEMD–wavelet threshold denoising algorithm. The algorithm follows the following steps: first, add noise to the monitoring data and obtain all the mode components through empirical mode decomposition (EMD), denoise the mode components with noise using the wavelet threshold function to remove the noise components, select the optimal stratification for denoising the monitoring data of the Guozigou Bridge in Xinjiang in January 2023, determine the wavelet type and threshold selection criteria, and reconstruct the denoised intrinsic mode function (IMF) components to achieve accurate extraction of the effective signal. By referencing the deflection, temperature, and strain data of the Guozigou Bridge in Xinjiang in January 2023 and comparing the data cleaned by different mode decomposition and wavelet threshold denoising methods, the results show that compared with empirical mode decomposition (EMD)–wavelet threshold denoising and variational mode decomposition (VMD)–wavelet threshold denoising, the signal-to-noise ratios and root-mean-square errors of the four types of monitoring data obtained by the algorithm proposed in this study are the most ideal. Under the premise of minimizing reconstruction errors when processing a large amount of data, it has better convergence, verifying the practicality and reliability of the algorithm in the field of bridge health monitoring data cleaning and providing a certain reference value for further research in the field of signal processing. The computational method constructed in this study will provide theoretical support for data cleaning and analysis of nonstationary and nonlinear random signals, which is conducive to further promoting the improvement of bridge health monitoring systems. [ABSTRACT FROM AUTHOR]

Predicting soil deformation is critical for the success of building construction projects. The traditional methods used for this task, which rely on theoretical calculations and numerical simulations, require detailed information on soil characteristics and geological conditions. These essential details are often challenging to obtain in practical engineering, thereby limiting the accuracy of these methods in building construction contexts. Deep learning (DL) provides a direct approach for modeling soil deformation without having a detailed understanding of the soil properties and geological conditions. However, the existing DL algorithms mainly focus on modeling deformation directly. With advancements in monitoring technology, integrating diverse monitoring data has become crucial for accurately predicting deformation, a need often overlooked in current practices. This paper introduces a monitoring data fusion (MDF) model aimed at enhancing the utilization efficiency of diverse monitoring data. Validated against real-world engineering scenarios, this model significantly outperforms traditional single-feature and multi-feature long short-term memory (LSTM) models. It achieves a mean absolute percentage error (MAPE) of approximately 2.12%, representing reductions of 30% and 63%, and a root mean square error (RMSE) of around 12.5 mm, with reductions of 36% and 77%. Additionally, the DL interpretability method, Shapley additive explanations (SHAP), is utilized to elucidate how various model features contribute to generating predictions. [ABSTRACT FROM AUTHOR]

Featured Application: This work is intended to provide additional approaches for analyzing and interpreting dam monitoring data. Pore water pressure (PWP) response is significant for evaluating the earth dams' stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order to detect a pathology or anomaly in the behavior of an embankment dam. This study endeavors to tackle this challenge through the application of diverse machine learning (ML) techniques in estimating the PWP within an existing earth dam. The methods employed include random forest (RF) combined with simulated annealing (SA), multilayer perceptron (MLP), standard recurrent neural networks (RNNs), and gated recurrent unit (GRU). The prediction capability of these techniques was gauged using metrics such as the coefficient of determination (R2), mean square error (MSE), and CPU training time. It was found that all the considered ML methods could give satisfactory results for the PWP estimation. Upon comparing these methods within the case study, the findings suggest that, in this study, multilayer perceptron (MLP) gives the most accurate PWP prediction, achieving the highest coefficient of determination (R2 = 0.99) and the lowest mean square error (MSE = 0.0087) metrics. A sensitivity analysis is then presented to evaluate the models' robustness and the hyperparameter's influence on the performance of the prediction model. [ABSTRACT FROM AUTHOR]

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3- σ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3- σ method. [ABSTRACT FROM AUTHOR]

Acetamiprid is a pesticide active substance with insecticidal action whose approval was renewed by Commission Implementing Regulation (EU) 2018/113. In January 2022, the EFSA PPR Panel published a statement following a request from the European Commission to advise on human health or the environment based on new scientific evidence presented by France during the decision‐making phase. In July 2022, by means of a further mandate received from the European Commission, EFSA was requested to provide advice if new information and any other scientific evidence that has become available since the assessment conducted for the renewal in 2018 warrant re‐evaluation of (i) toxicological parameters used for the risk assessment of acetamiprid during the renewal process, including toxicological endpoints; (ii) the residue definition for acetamiprid in products of plant origin; and (iii) the safety of existing maximum residue levels (MRLs). Meanwhile, the applicant of acetamiprid in the EU submitted new toxicology studies regarding the toxicological profile of the metabolite IM‐2‐1. Furthermore, the European Commission was made aware that several recent publications in scientific literature were made available after the literature searches conducted by EFSA. As the new data could affect the advice that EFSA was expected to deliver through the 2022 mandate, EFSA was further requested to consider this information by means of a revised mandate received in September 2023. As regards re‐evaluation of point (i) in this statement, this was addressed by an EFSA Working Group integrating all the available evidence. The results of the weight of evidence indicated that there are major uncertainties in the body of evidence for the developmental neurotoxicity (DNT) properties of acetamiprid and further data are therefore needed to come to a more robust mechanistic understanding to enable appropriate hazard and risk assessment. In view of these uncertainties, the EFSA WG proposed to lower the acceptable daily intake (ADI) and acute reference dose (ARfD) from 0.025 to 0.005 mg/kg body weight (per day). A revised residue definition for risk assessment was proposed for leafy and fruit crops as sum of acetamiprid and N‐desmethyl‐acetamiprid (IM‐2‐1), expressed as acetamiprid. Regarding pulses/oilseeds, root crops and cereals, the new data received did not indicate a need to modify the existing residue definition for risk assessment, which therefore remains as parent acetamiprid. Regarding the residue definition for enforcement, the available data did not indicate a need to modify the existing definition because acetamiprid is still a sufficient marker of the residues in all crop groups. Considering the new health‐based guidance values derived in the present statement, a risk for consumer has been identified for 38 MRLs currently in place in the EU Regulation. Consequently, EFSA recommended to lower the existing MRLs for 38 commodities based on the assessment of fall‐back Good Agricultural Practices received within an ad hoc data call. Some fall‐back MRLs proposals require further risk management considerations. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Considering the importance of calculating surface deformation based on monitoring data, this paper proposes a method for calculating horizontal deformation based on horizontal displacement monitoring data. This study first analyzes the characteristics of horizontal displacement monitoring data, then proposes a scheme for obtaining the surface horizontal displacement field through corresponding discrete point interpolation. Subsequently, the calculation method for surface horizontal strain is introduced, along with relevant examples. The study also systematically summarizes the calculation methods for surface curvature and surface tilt deformation values, forming a set of surface deformation calculation methods based on monitoring data. The research results indicate that when there is a large number of on-site monitoring points, effective monitoring points can be selected based on the direction of horizontal displacement. When interpolating the surface horizontal displacement field, the interpolation accuracy of the radial basis function method is slightly higher than that of ordinary Kriging. The form of coordinate expression has a significant impact on interpolation accuracy. The accuracy of interpolation using horizontal displacement vectors expressed in polar coordinates is higher than that using vectors expressed in Cartesian coordinates. The calculated surface horizontal strain has effective upper and lower limits, with lower-limit strain on the contour line conforming to the typical surface deformation patterns around mined-out areas. [ABSTRACT FROM AUTHOR]

There are several large karst caves at haunch part of the Lidong Tunnel during construction, together with inrush water due to high pressure within these caves. In light of it, this paper takes YK342+113 section as an example and adopts finite difference software FLAC 3D, so as to analyze tunnel deformation when crossing karst caves under six different working conditions, including with or without karst cave, before and after karst treatment, along with support locations. According to analysis results: First, the wall rock mainly had deformation at tunnel vault when evacuating at the third bench, which is a critical monitoring focus for tunnel construction; Second, karst cave treatment contributed to better conduct forces on both sides of wall rock, thus reducing vault settlement, while not affecting horizontal convergence and upturn of vaults; Third, treatment measures were proved to be effective in minimizing wall rock deformation by comparing deformation curves under different conditions; Fourth, after treatment measures, the angular points within the cave’s chamber had stress concentration, which might cause secondary collapse. Field monitoring data revealed that the final settlement of the tunnel vault was relatively consistent with the numerical analysis results, with a distinct change in daily settlement after initial support construction. By integrating numerical analysis and field monitoring, the rationality of the karst treatment plan was fully verified, providing a valuable reference for similar projects.

The bench slope within an open-pit coal mine plays an important supporting role for coal resource production and transportation. The stability evaluation of the slope is of great significance in guiding continuous mining operations within the mine. This study leverages monitoring data and geological surveys of the slope, employing the analytic hierarchy process (AHP) to assign weights to slope stability analysis factors. Additionally, it employs the principle of maximum membership degree, taking into account various parameters such as slope geometry, geological data, meteorological and hydrological information, and field monitoring data. This paper establishes an analytic hierarchy process - fuzzy comprehensive evaluation model for slope stability assessment. Subsequently, the model is applied to analyze the stability of the north slope at the Zhahanao’er open-pit coal mine. The research indicates that, accordingly to the established slope analytic hierarchy process - fuzzy comprehensive evaluation model, the northern slope is deemed to be fundamentally stable. The numerical simulated FOS (factor of safety) of 1.121 aligns remarkably well with model results. It is comprehensive and accurate since the model can fully consider the contribution of multiple information to the stability of slope.

The ability to measure strains quasi‐continuously with high spatial resolution makes distributed fiber optic sensing a promising technology for structural health monitoring as it allows to locate and measure damages in concrete structures, such as cracks. Depending on whether the distributed fiber optic sensor (DFOS) is embedded into the concrete matrix or bonded to the reinforcement, different approaches for crack width calculation exist. The high spatial resolution of DFOS quickly leads to a large amount of data, especially with time continuous monitoring. Scalable, automated analysis approaches are required to handle such big data and to derive a gain in knowledge from the measurements. Thus, in a first step, the Python framework fosanalysis is presented and made available to other researchers or monitoring specialists as free software. The most important input parameters for crack width calculation are discussed for concrete strain and steel strain DFOS. Accurate crack monitoring for a 4 m long reinforced concrete beam is demonstrated by using fosanalysis. The calculated crack widths are in good agreement with digital image correlation measurements. [ABSTRACT FROM AUTHOR]

Introduction: Taking a phosphate mine in Yunnan as the engineering research background, the fractured rock mass of the No. 1 belt roadway is selected as the research object. Methods: To reduce the cost of tunnel surrounding rock support and optimize construction technology, a combination of numerical simulation software Midas and on-site experiments was used to compare and analyze the distribution characteristics of the plastic zone of tunnel surrounding rock, deformation characteristics of tunnel surrounding rock, and stress characteristics of tunnel support anchor bolts under two support methods: double-layer spray anchor mesh and single-layer spray anchor mesh with steel bar. Results: Research findings indicate that adopting the single-layer spray anchor mesh with steel bar support method results in a more uniform distribution of the plastic zone at the top of the tunnel surrounding rock, with a smaller range. The settlement of the roof and bulging of the bottom plate in the roadway's surrounding rock, supported by a single-layer spray anchor mesh and steel bars, measure at 75.033 mm and 82.165 mm, respectively. Additionally, the internal bulging on both sides of the roadway's surrounding rock is recorded as 37.835 mm and 37.470 mm, respectively. On the other hand, when utilizing a double-layer spray anchor mesh for support, the settlement of the roof and bulging of the bottom plate in the roadway's surrounding rock are measured at 84.199 mm and 85.255 mm, respectively. Correspondingly, the internal bulging on both sides of the roadway's surrounding rock records measurements of 41.670 mm and 41.756 mm, respectively. Furthermore, adopting the single-layer spray anchor mesh with steel bar support method ensures a more even transfer of axial forces to the steel bars, thereby increasing the overall load-bearing capacity. In contrast, the anchor bolts in the double-layer spray anchor mesh support method experience higher axial forces, leading to more pronounced stress concentration. Discussion: By comparing the field monitoring data, it is evident that the single-layer spray anchor mesh with steel bar support method exhibits significantly better control over the tunnel surrounding rock than the double-layer spray anchor mesh support method. Therefore, replacing the double-layer spray anchor net support with a single-layer spray anchor net with steel bar support method is entirely feasible and can meet on-site practical production requirements. The research results hold significant value in providing guidance and reference for selecting cost-effective support schemes for tunnel surrounding rock. [ABSTRACT FROM AUTHOR]

The increasingly large server clusters of state grid corporation generate a large amount of production operation data, and real-time analysis of the massive monitoring data generated by various devices and systems has become a new challenge in power IT operation and maintenance work. As a key technology of intelligent grid information operation and maintenance work, anomaly detection technology can effectively detect operation and maintenance faults and provide timely alarms to avoid damage to sensitive equipment. Currently, some traditional anomaly detection methods have few types of anomalies and low precision, resulting in delayed fault detection. To address this challenge, this article proposes a multi-dimensional time series anomaly detection method based on capsule networks, NNCapsNet. Firstly, the unsupervised algorithm is applied in combination with expert knowledge to preprocess and label the performance monitoring data of grid marketing business application servers. Secondly, the capsule network is introduced for classification and anomaly detection. Experimental results obtained through five-fold cross-validation show that NNCapsNet achieves an average classification accuracy of 91.21% on a dataset containing 15 types of anomalies. At the same time, compared with four benchmark models on the dataset containing 20 000 monitoring data, NNCapsNet achieves good results in key evaluation indicators. [ABSTRACT FROM AUTHOR]

Two significant earthquakes struck eastern Taiwan on consecutive days in mid-September of 2022. The first, the Guanshan earthquake, occurred on the night of September 17 (local time) with a local magnitude (ML) of 6.4, and the second, the Chihshang earthquake, occurred on the afternoon of September 18 with a mainshock of ML of 6.8. The strong motion of these two events resulted in a series of ground surface ruptures, 2 collapsed buildings, two collapsed bridges, and more than 100 partially damaged structures along the Chihshang and Yuli faults around the Longitudinal Valley in eastern Taiwan. The observed response spectra at the damage sites of the Chihshang earthquake were significantly larger than the design response spectra of the 1997 Taiwan building code. Near-fault velocity pulses with a maximum value of 132 cm/s were observed along the faults, destructively impacting the damaged structures. The details and causes of the structural damage are presented in this article according to the findings of the on-site reconnaissance and ground motion analysis. Finally, the behavior of selected structures that have a structural health monitoring system at the time of these two destructive earthquakes is also evaluated. [ABSTRACT FROM AUTHOR]

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Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Acetamiprid is a pesticide active substance with insecticidal action whose approval was renewed by Commission Implementing Regulation (EU) 2018/113. In January 2022, the EFSA PPR Panel published a statement following a request from the European Commission to advise on human health or the environment based on new scientific evidence presented by France during the decision‐making phase. In July 2022, by means of a further mandate received from the European Commission, EFSA was requested to provide advice if new information and any other scientific evidence that has become available since the assessment conducted for the renewal in 2018 warrant re‐evaluation of (i) toxicological parameters used for the risk assessment of acetamiprid during the renewal process, including toxicological endpoints; (ii) the residue definition for acetamiprid in products of plant origin; and (iii) the safety of existing maximum residue levels (MRLs). Meanwhile, the applicant of acetamiprid in the EU submitted new toxicology studies regarding the toxicological profile of the metabolite IM‐2‐1. Furthermore, the European Commission was made aware that several recent publications in scientific literature were made available after the literature searches conducted by EFSA. As the new data could affect the advice that EFSA was expected to deliver through the 2022 mandate, EFSA was further requested to consider this information by means of a revised mandate received in September 2023. As regards re‐evaluation of point (i) in this statement, this was addressed by an EFSA Working Group integrating all the available evidence. The results of the weight of evidence indicated that there are major uncertainties in the body of evidence for the developmental neurotoxicity (DNT) properties of acetamiprid and further data are therefore needed to come to a more robust mechanistic understanding to enable appropriate hazard and risk assessment. In view of these uncertainties, the EFSA WG proposed to lower the acceptable daily intake (ADI) and acute reference dose (ARfD) from 0.025 to 0.005 mg/kg body weight (per day). A revised residue definition for risk assessment was proposed for leafy and fruit crops as sum of acetamiprid and N‐desmethyl‐acetamiprid (IM‐2‐1), expressed as acetamiprid. Regarding pulses/oilseeds, root crops and cereals, the new data received did not indicate a need to modify the existing residue definition for risk assessment, which therefore remains as parent acetamiprid. Regarding the residue definition for enforcement, the available data did not indicate a need to modify the existing definition because acetamiprid is still a sufficient marker of the residues in all crop groups. Considering the new health‐based guidance values derived in the present statement, a risk for consumer has been identified for 38 MRLs currently in place in the EU Regulation. Consequently, EFSA recommended to lower the existing MRLs for 38 commodities based on the assessment of fall‐back Good Agricultural Practices received within an ad hoc data call. Some fall‐back MRLs proposals require further risk management considerations.

This study presents a phenomenon in photovoltaic field installations we call voltage ear. PV installations exhibit the voltage ear under specific conditions where modules generate more power than the inverter can handle. When inverters exceed their power limit it results in inverter clipping, and the solar modules are regulated to operate at a voltage above that at maximum power. This kind of voltage rise leads to a specific pattern in the monitoring data – the voltage ear. In the present study, data from a solar park in France was analyzed. This park featured a high rate of inverter malfunctions, including overheating and fires, resulting in an estimated 5% failure rate and, ultimately, in a complete replacement of inverters. An automated analysis of the monitoring data revealed 32.000 occurrences of excess voltage in 1.65 million data points (2%). Instances of the phenomena were detected and segmented using a K-means sorting algorithm, which yielded a perfect recall value. Analysis of the identified voltage ear conditions showed, that it occurs predominantly during seasons of high irradiance and cool temperatures. Excess voltages of up to 161 V in single strings were observed, increasing the probability for PID and inverter damage. The direct yield loss due to deviation from maximum power point was between 0.02% in January and 0.9% in June. We assume, that the voltage ear is a symptom of system design issues that can result in malfunctioning of inverters and hope that it will be useful as an indicator for identifying design and operation issues.

The efficient operation of cellular networks requires careful tuning of configuration parameters, such as the transmit power or antenna tilts, to adequately balance interference while providing the necessary capacity to the connected UEs. As manual tuning of these parameters is typically unfeasible, several automated coverage and capacity optimization methods have been proposed. However, most existing solutions are either based on poorly scalable black-box optimization methods or solely consider interference management, while omitting the potential of congested cells. In this work, we instead propose a differentiable framework for cellular network optimization, centered around the end-user throughput, that enables load-aware tuning of network parameters through gradient descent. Hereby, we approach the problem from a data-driven perspective, and include dedicated model subcomponents derived from monitoring data, which enable the calibration to site-specific traffic patterns and KPI measurements. We validate our approach for joint transmit power optimization in a real-world network layout with $\approx 150$ cells in two frequency bands. In our evaluation, the gradient descent-based optimization reliably reduces the outage ratio for different levels of demand, while the black-box baseline struggles to explore the large search space. Our results further reveal substantial differences between the proposed load-aware and commonly used SINR-based objectives, for which we repeatably obtain unbalanced network configurations with severely congested cells. In contrast, the proposed end-user throughput objective promotes a balanced network configuration, providing adequate resources to the connected UEs while also limiting interference.

Deformation is the most direct indicator of structural state changes in arch dams. Therefore, numerous deformation monitoring points are typically arranged on arch dams to obtain deformation data from each point. Considering the complex relationships between the deformation at each monitoring point, this study focuses on the internal structural relationships and information fusion within the dam. The Pearson correlation coefficient is used as a similarity index to determine significant linear correlations between the measuring points. Ward’s cluster analysis method is then applied to group these points based on their similarities. To identify measuring points with strong nonlinear correlations, the Maximum Information Coefficient (MIC) method is employed. By integrating these linear and nonlinear correlations, a model is constructed to characterize the deformation at specific measurement points using data from strongly correlated points. The effectiveness of this model is verified through a concrete engineering case study, offering a novel approach for analyzing arch dam deformations.

Rapid urbanization and industrialization have caused water quality issues in urban rivers. Appropriate measures based on water quality monitoring systems and prediction methods are needed for water quality management. While South Korea has operated a water quality monitoring system that measures various environmental factors and has accumulated water quality data, a water quality prediction system is not in place. This study suggests a water quality prediction method based on a long short-term model using water quality and meteorological monitoring data. Additionally, we present a derived input set of the prediction model that can improve the prediction model performance. The prediction model’s performance was evaluated by the coefficient of determination under various conditions, such as the hyperparameters, temporal resolution of input data, and application of upstream and downstream data. As a result, using the temporal resolution of the input data as hourly data improved predictions by an average of 25.6% over three days of the prediction period compared to daily data. Meanwhile, it was analyzed that the hyperparameters and using upstream and downstream data have a minor effect on the model performance. The results of this study underscore the crucial role of the number, duration, and temporal resolution of available monitoring data in water quality management.

Insects are key components of food chains, and monitoring data provides new opportunities to identify trophic relationships at broad spatial and temporal scales. Here, combining two monitoring datasets from Great Britain, we reveal how the population dynamics of the blue tit Cyanistes caeruleus are influenced by the abundance of moths – a core component of their breeding diet. We find that years with increased population growth for blue tits correlate strongly with high moth abundance, but population growth in moths and birds is less well correlated; suggesting moth abundance directly affects bird population change. Next, we identify moths that are important components of blue tit diet, recovering associations to species previously identified as key food sources such as the winter moth Operoptera brumata. Our work provides new evidence that insect abundance impacts bird population dynamics in natural communities and provides insight into spatial diet turnover at a national‐scale. [ABSTRACT FROM AUTHOR]

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The presence of emerging contaminants (ECs) originating from pharmaceutical waste in water, wastewater, and marine ecosystems at various geographical locations has been clearly publicised. This review paper presents an overview of current monitoring data on the occurrences and distributions of ECs in coastal ecosystem, tap water, surface water, ground water, treated sewage effluents, and other sources. Technological advancements for EC removal are also presented, which include physical, chemical, biological, and hybrid treatments. Adsorption remains the most effective method to remove ECs from water bodies. Various types of adsorbents, such as activated carbons, biochars, nanoadsorbents (carbon nanotubes and graphene), ordered mesoporous carbons, molecular imprinting polymers, clays, zeolites, and metal–organic frameworks have been extensively used for removing ECs from water sources and wastewater. Extensive findings on adsorptive performances, process efficiency, reusability properties, and other related information are thoroughly discussed in this mini review. [ABSTRACT FROM AUTHOR]

To explore the settlement patterns of the existing tunnel structure inducted by underneath crossing of deep drainage shield tunnel and its relationship with construction control methods, a case study is conducted on the Shenzhen Qianhai- Nanshan drainage tunnel crossing underneath the existing Line 11. The essential construction factors affecting the settlement of the existing tunnel are analyzed based on field monitoring data and site conditions. The results reveal the following: (1) The settlement and deformation of the existing Line 11 tunnel is prominently correlated to construction stages and methods. (2) The unenven settlement occurs on the horizontal direction of the existing tunnel, whereas it cannot be found on roadbed. The settlement curve on the longitudinal direction presents a V shape or W shape. (3) Selecting rational soil chamber and grouting pressures and using secondary grouting and frequent supplementary grouting is conductive to settlement control of the existing structure. [ABSTRACT FROM AUTHOR]

This study focuses on the shaft power at the neutral angle of a controllable pitch propeller to analyze the change in the required power due to propeller fouling. It is named and defined as a power for keeping propeller's rotation (RP) because a propeller does not generate the net thrust to push the hull but dissipates the power to keep its constant speed. Statistical analysis was conducted using a decade of the observed data from a research vessel. This study assumed that the change in RP can be considered as an indicator showing the state of propeller fouling. In addition, the state of fouling on the hull can be estimated indirectly based on RP because the data correlated the shaft power under several operating conditions with RP on the same voyage. The fouling state was classified into three conditions based on RP. The comparison of shaft power at the same ship speed between the clean condition and the serious fouled condition showed that the shaft power was increased by 35% on average due to fouling. When the ship navigates under 10 knots, propeller fouling has a larger impact on the increase in shaft power than hull fouling. [ABSTRACT FROM AUTHOR]

In this study, we introduce a weather data independent self‐referencing algorithm for performance analyses of PV power stations based on monitoring data. We introduce this method as an alternative to standard performance ratio analyses, in the case that no meteorological data are available. The self‐referencing algorithm utilizes the string with maximum output as a reference. The output of other strings is set in relation to the corresponding value of the reference string, resulting in a quantity we call self‐referencing ratio SR. We benchmark this ratio against the established performance ratio, which requires solar irradiance data for calculation. Based on a 9 MWp PV power station with 1,719 strings, we show that we are able to quantify, locate, and visualize the performance of each string in the PV power station. About 10% of the strings are underperforming; most of them are located in the bottom rows of the mounting racks. Shading from adjacent mounting racks is a possible explanation for this finding. Furthermore, IR‐images and voltage data indicate a frequent occurrence of strings with a reduced number of modules. An underperformance of less than 2% is challenging to detect, due to the uncertainty range of the chosen dataset. [ABSTRACT FROM AUTHOR]

Tunnels are used for numerous purposes; therefore, proper evaluation and prediction of tunnel behavior, influenced by the surrounding environment's characteristics, is of great concern for civil engineers. In this regard, two water convey and railway tunnels in rock medium were modelled to investigate the influence of various constitutive models on tunnels' behaviors prediction. The tunnel convergence predicted by each constitutive model was compared with the reported monitoring data. Then, the most appropriate constitutive models for tunnel analysis in each rock category were proposed according to the strength of each category's rocks. The results indicated that the Mohr-Coulomb criteria for very weak rocks, the Generalized Hoek-Brown for weak rocks and the Generalized Hoek-Brown with the residual parameter criteria for modelling medium rocks, had more reliable predictions of tunnel convergence. Also, utilizing shear strength parameters correlated from rock mass specific parameters to analyze tunnels in weak and medium rocks was not satisfactory. [ABSTRACT FROM AUTHOR]

INTRODUCTION： With the continuous development of oil and gas pipeline network monitoring and control technology, the need for data transmission and communication is becoming more and more prominent. In this context, OPC UA has attracted wide attention. This study aims to explore the application of OPC UA in data forwarding for oil and gas pipeline network monitoring in order to improve the efficiency, reliability and security of data transmission. PURPOSE: The purpose of this study is to evaluate the applicability of OPC UA in oil and gas pipeline network monitoring and to verify its performance in data forwarding through empirical studies. By gaining an in-depth understanding of the characteristics of OPC UA, it aims to provide a more advanced and efficient monitoring data transfer solution for the oil and gas industry. METHOD: The study adopts a combination of field monitoring and laboratory simulation. First, the essential characteristics and requirements of monitoring data in oil and gas pipeline networks were collected. Subsequently, a monitoring system with OPC UA as the communication protocol was established and field tested. In the laboratory environment, data transmission scenarios under different working conditions were simulated, and the performance of OPC UA under different conditions was analyzed. RESULT: The field monitoring results show that the data transmission efficiency is significantly improved by using OPC UA as the communication protocol for data forwarding in oil and gas pipeline network monitoring. Meanwhile, the system performs well in different environments with high reliability and security. The laboratory simulation results further verify the stability and adaptability of OPC UA under complex working conditions. CONCLUSION: OPC UA is an effective communication protocol that can meet the data transmission requirements for oil and gas pipeline network monitoring. Its efficient, reliable, and secure characteristics make it an ideal choice for improving the communication performance of monitoring systems in the oil and gas industry. The empirical results of this study provide reliable technical support for the oil and gas industry in the field of data transmission and a vital reference for the optimization and upgrading of monitoring systems in the future.

The existing rockburst risk assessment methods generally belong to static assessment method which normally depends on the geological and mining conditions that are revealed prior to mining. There is commonly a certain gap between the assessment results and the real situation. How to combine with the monitoring data in the mining process to get more realistic evaluation results is a problem that needs to be further solved. To this end, this work proposes a static-dynamic coupling rockburst risk assessment method taking into account the geological and mining conditions as well as the monitoring data, and the method has been being applied in typical panels in near-vertical coal seams. Firstly, a static evaluation method used before mining based on the improved comprehensive index method is proposed. The evaluation indicators contained in the comprehensive index method are linearly normalized to obtain the single indicator risk index. The AHP method is used to assign weights to each evaluation indicator, and the weighted summation of all indicator risk indexes is calculated to obtain the risk indexes determined by geological factors and mining technology factors, respectively. Then the weighted mean of the two risk indexes is used as the static evaluation risk index. Secondly, a dynamic evaluation method used during mining by utilizing the monitoring data is constructed. According to the characteristics of microseismic monitoring and borehole stress monitoring and their relationship with rockburst risk, the dynamic evaluation indicators are constructed based on the microseismic energy density and borehole stress variation. The evaluation indicators are linearly normalized to obtain two evaluation indexes. The weighted mean of the two indexes is used as the dynamic evaluation risk index. Finally, the two proposed methods are combined to get a static-dynamic coupling evaluation model. The coupling evaluation risk index is obtained by weighted average of the static evaluation risk index and the dynamic evaluation risk index, and then the risk grade and risk area are determined. The method is applied in +450B3+6 panel of Wudong Coal Mine. Results show that the static evaluation method demarcates 4 medium risk areas and 1 weak risk area before the mining of the panel, the dynamic evaluation method determines that the rock pillar side has high risk level in the mining process of the panel, and the static dynamic coupling evaluation re-evaluates the weak risk area of the static evaluation to the medium risk area. By investigating the monitoring data of support pressure, the numerical simulation results and the rockburst occurrence on site during the mining process of the panel, the evaluation results are verified, and it is found that the static and dynamic coupling evaluation results are more consistent with the actual situation. This work provides a new method and a new idea for the risk assessment of rock burst.

In view of the fact that most industrial and mining enterprises are in digital transformation at present, the digitized safety production monitoring data has the characteristics of strong real-time and huge data volume. The data volume of some systems, such as steel wire rope flaw detection system, is up to several hundred thousand pieces per second. The commonly used SQL database has been unable to cope with such a huge data storage and analysis task. A high-density data acquisition system with high concurrency and high density data processing capability based on NoSQL is constructed through NoSQL database, load balancing, multithreading, SOCKET and other technical systems. The system realizes the high-density access of safety production related data of industrial and mining enterprises, and meets the hard demand of high-density data sampling cycle.

This paper focuses on the application of pile foundation underpinning technology in a deep foundation pit of a subway Viaduct Project in Beijing. The study aims to address the engineering characteristics of the project, including a large number of new piles, a wide span of underpinning abutment, a long length of deep foundation pit, and a wide range of influences. This research utilizes field monitoring and numerical simulation methods to investigate the pile foundation underpinnings. The impact and management of road-deep foundation pit construction are considered, as well as their combined effect on subway viaducts and track structures. The primary accomplishments are as follows: (1) By analyzing the data from on-site deformation monitoring, it is evident that the pier exhibits maximum vertical deformation and maximum transverse deformation at the same location. The measuring locations are specifically situated on Pier 7# at the pile foundation underpinning. The maximum vertical and transverse deformations of the track are directly proportional to the maximum deformation of the pier. (2) By comparing the numerical simulation results with the field monitoring data, it is observed that although there is some discrepancy between the two, the deformation trend is largely consistent. This suggests that the numerical simulation analysis method is effective in reflecting the deformation of the bridge and track. (3) Through the numerical model and changing the values of the retaining structure parameters, the sensitivity of the pier deformation near the road foundation pit to the retaining structure parameters is systematically analyzed. The sensitivity of the pier deformation to the foundation pit parameters is as follows: the embedded depth insertion ratio of the retaining pile > the diameter of the retaining pile > the pile spacing.

Predicting soil deformation is critical for the success of building construction projects. The traditional methods used for this task, which rely on theoretical calculations and numerical simulations, require detailed information on soil characteristics and geological conditions. These essential details are often challenging to obtain in practical engineering, thereby limiting the accuracy of these methods in building construction contexts. Deep learning (DL) provides a direct approach for modeling soil deformation without having a detailed understanding of the soil properties and geological conditions. However, the existing DL algorithms mainly focus on modeling deformation directly. With advancements in monitoring technology, integrating diverse monitoring data has become crucial for accurately predicting deformation, a need often overlooked in current practices. This paper introduces a monitoring data fusion (MDF) model aimed at enhancing the utilization efficiency of diverse monitoring data. Validated against real-world engineering scenarios, this model significantly outperforms traditional single-feature and multi-feature long short-term memory (LSTM) models. It achieves a mean absolute percentage error (MAPE) of approximately 2.12%, representing reductions of 30% and 63%, and a root mean square error (RMSE) of around 12.5 mm, with reductions of 36% and 77%. Additionally, the DL interpretability method, Shapley additive explanations (SHAP), is utilized to elucidate how various model features contribute to generating predictions.

In order to investigate the analysis and processing methods for nonstationary signals generated in bridge health monitoring systems, this study combines the advantages of complete ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising algorithms to construct the CEEMD–wavelet threshold denoising algorithm. The algorithm follows the following steps: first, add noise to the monitoring data and obtain all the mode components through empirical mode decomposition (EMD), denoise the mode components with noise using the wavelet threshold function to remove the noise components, select the optimal stratification for denoising the monitoring data of the Guozigou Bridge in Xinjiang in January 2023, determine the wavelet type and threshold selection criteria, and reconstruct the denoised intrinsic mode function (IMF) components to achieve accurate extraction of the effective signal. By referencing the deflection, temperature, and strain data of the Guozigou Bridge in Xinjiang in January 2023 and comparing the data cleaned by different mode decomposition and wavelet threshold denoising methods, the results show that compared with empirical mode decomposition (EMD)–wavelet threshold denoising and variational mode decomposition (VMD)–wavelet threshold denoising, the signal-to-noise ratios and root-mean-square errors of the four types of monitoring data obtained by the algorithm proposed in this study are the most ideal. Under the premise of minimizing reconstruction errors when processing a large amount of data, it has better convergence, verifying the practicality and reliability of the algorithm in the field of bridge health monitoring data cleaning and providing a certain reference value for further research in the field of signal processing. The computational method constructed in this study will provide theoretical support for data cleaning and analysis of nonstationary and nonlinear random signals, which is conducive to further promoting the improvement of bridge health monitoring systems.

Engineering constructions in coastal areas not only affect existing landslides, but also induce new landslides. Variation of the water level makes the coastal area a geological hazard-prone. Prediction of the slope displacement based on monitoring data plays an important role in early warning of potential landslide and slope failure, and supports the risk management of hazards. Given the complex characteristic of the slope deformation, we proposed a prediction model using random coefficient model under the frame of panel data analysis, so as to take the correlation among monitoring points into consideration. In addition, we classified the monitoring data using Gaussian mixture model, to take the temporal-spatial characteristics into consideration. Monitoring data of Guobu slope was used to validate the model. Results indicated that the proposed model have a better performance in prediction accuracy. We also compared the proposed model with the BP neural network model and temporal – temperature model, and found that the prediction accuracy of the proposed model is better than those of the two control models.

Pore water pressure (PWP) response is significant for evaluating the earth dams’ stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order to detect a pathology or anomaly in the behavior of an embankment dam. This study endeavors to tackle this challenge through the application of diverse machine learning (ML) techniques in estimating the PWP within an existing earth dam. The methods employed include random forest (RF) combined with simulated annealing (SA), multilayer perceptron (MLP), standard recurrent neural networks (RNNs), and gated recurrent unit (GRU). The prediction capability of these techniques was gauged using metrics such as the coefficient of determination (R2), mean square error (MSE), and CPU training time. It was found that all the considered ML methods could give satisfactory results for the PWP estimation. Upon comparing these methods within the case study, the findings suggest that, in this study, multilayer perceptron (MLP) gives the most accurate PWP prediction, achieving the highest coefficient of determination (R2 = 0.99) and the lowest mean square error (MSE = 0.0087) metrics. A sensitivity analysis is then presented to evaluate the models’ robustness and the hyperparameter’s influence on the performance of the prediction model.

Considering the importance of calculating surface deformation based on monitoring data, this paper proposes a method for calculating horizontal deformation based on horizontal displacement monitoring data. This study first analyzes the characteristics of horizontal displacement monitoring data, then proposes a scheme for obtaining the surface horizontal displacement field through corresponding discrete point interpolation. Subsequently, the calculation method for surface horizontal strain is introduced, along with relevant examples. The study also systematically summarizes the calculation methods for surface curvature and surface tilt deformation values, forming a set of surface deformation calculation methods based on monitoring data. The research results indicate that when there is a large number of on-site monitoring points, effective monitoring points can be selected based on the direction of horizontal displacement. When interpolating the surface horizontal displacement field, the interpolation accuracy of the radial basis function method is slightly higher than that of ordinary Kriging. The form of coordinate expression has a significant impact on interpolation accuracy. The accuracy of interpolation using horizontal displacement vectors expressed in polar coordinates is higher than that using vectors expressed in Cartesian coordinates. The calculated surface horizontal strain has effective upper and lower limits, with lower-limit strain on the contour line conforming to the typical surface deformation patterns around mined-out areas.