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Evaluation of earthquake-induced liquefaction potential is crucial in the design phase of construction projects. Although several machine learning models achieve good prediction accuracy on their particular datasets, they may not perform well in other liquefaction datasets. To address this issue, we proposed a novel hybrid classifier ensemble to improve generalizability by combining the predictions of seven base classifiers using the weighted voting method. The applied base classifiers include back propagation neural network, support vector machine, decision tree, k-nearest neighbours, logistic regression, multiple linear regression and naïve Bayes. The hyperparameters and weights of the base classifiers were tuned using the genetic algorithm. To verify the robustness of the classifier ensemble, its performance was tested on three datasets collected from previous published researches. The results show that the proposed classifier ensemble outperforms the base classifiers in terms of a variety of performance metrics including accuracy, Kappa, precision, recall, F1 score, AUC and ROC on the three datasets. In addition, the importance of influencing variables was achieved by the classifier ensemble on the three datasets to facilitate the future data collecting work. This robust ensemble method can be extended to solve other classification problems in civil engineering.

This research presents a new model for finding optimal conditions in the concrete technology area. To do that, results of a series of laboratory investigations on concrete samples were considered and used to design several artificial intelligence (AI) models. The data samples include 8 parameters i.e., silica fume replacement ratio, fly ash replacement ratio, fine aggregate, water content, high rate water reducing agent, coarse aggregate, total cementitious material, and age of samples, were used to predict and optimize the compressive strength of concrete samples. For optimization purposes, this study used a human learning optimization (HLO) algorithm to find the optimal results as well as optimizing the kernel coefficients of the support vector regression (SVR) models. Initially, to form the core of this research, various models were constructed and proposed to design the required relationship between the data using SVR. Since different SVR kernels have their own coefficients, using optimization theory, the probability of error in the models was reduced and the models were identified and executed with the highest accuracy. Finally, the polynomial model was selected as the model with the lowest computational error and the highest accuracy for evaluating the compressive strength of the concrete samples. The accuracy of the proposed SVR model for training and testing data was obtained as the coefficient of determination (R2) = 0.9694 and R2 = 0.9470, respectively. This function was considered as a relation, to be developed by the HLO algorithm to find optimal options under different conditions. The results for 14 samples, which are the most important examples of this research, showed that the optimal states are obtained with a high level of accuracy. This confirms the proper use/develop of the SVR-HLO algorithm in designing the predictive model as well as finding optimal conditions in the concrete technology area.

The reinforced concrete (RC) infrastructure can be retrofitted by adhesively bonding fiber-reinforced polymers (FRPs) to the tension face. In the FRP-to-concrete bonding system, the debonding of the FRP plate from the member is the most common failure type. Predicting the bond strength of FRP-to-concrete joints using traditional predictive models is far from being satisfactory because of the highly nonlinear relationships between the bond strength and a large number of influencing variables. To address this issue, this study proposes a metaheuristic-optimized least-squares support vector regression (LSSVR) model to predict the bond strength of FRP-to-concrete joints. The hyperparameters of the LSSVR model are tuned using a recently proposed beetle antennae search (BAS) algorithm. In addition, the Levy flight is incorporated in the BAS algorithm to improve its searching efficiency. The proposed model is then trained on a dataset collected from internationally published literature. To understand the importance of each input variable on the bond strength, the variable importance is calculated using the random forest algorithm. The results show that the proposed LBAS-LSSVR model has comparatively high prediction accuracy, as indicated by a high correlation coefficient (0.983) and low root mean square error (1.99 MPa) on the test set. Width of FRP is the most sensitive variable to the bond strength. The proposed model can be extended to solve other regression problems in structural engineering.

Shear design of RC beams with and without stirrups using laboratory experiments is difficult or even impossible as a large number of variables need to be considered simultaneously, such as the span-to-depth ratio, web width and reinforcement ratio. In addition, due to the complex shear failure mechanism, empirical approaches for shear design are derived within the boundaries of their own testing regimes. Thus, the generalization ability and applicability of these approaches are limited. To address this issue, this study uses machine learning approaches for shear design. A random forest model is constructed to predict the shear strength of RC beams. The hyperparameters of RF are tuned using beetle antennae search algorithm modified by Levy flight and inertia weight. The developed model is trained on two data sets of RC beams with and without stirrups containing 194 and 1849 samples, respectively. The obtained model has high prediction accuracy with correlation coefficients of 0.9367 and 0.9424 on these two test data sets, respectively. The proposed method is powerful and efficient in shear design of RC beams with and without stirrups and therefore paves the way to intelligent construction.

Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index.ObjectiveTo explore the utility of Internet of Things in power transformer fault diagnosis system.MethodsA total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system.ResultsThe detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

A Fe-modified black peanut shell biochar (Fe@BC) was fabricated using FeCl 3 as a magnetic modifier, its sorption for Pb(II) in mixed Pb(II), Cu(II), Ni(II) solutions was examined. It was showed that Fe@BC had better adsorption for Pb(II). While the optimal pH was 4.0, and equilibrium time was 8 h. The adsorption capacity of Fe@BC for Pb(II) was 22.535 mg/g in mixed systems. Interestingly, the adsorption of Fe@BC for Pb(II) and Ni(II) exhibited a decreasing trend. In contrast, its adsorption for Cu(II) increased with the rising temperature. The fitting of theoretical models showed that the adsorption of Fe@BC for them followed quasi-second kinetic model, and such an adsorption was a spontaneous process. Additionally, the adsorption of Fe@BC for Pb(II) fitted well with Freundlich isotherm model. Conversely, its adsorption for Cu(II) and Ni(II) obeyed Langmuir–Freundlich isotherm model. Mechanisms of Fe@BC for metal ions mainly enclosed ion exchange, surface physic-sorption, pore adsorption, groups’ adsorption, and bonding effect. Regeneration of Fe@BC for Pb(II) showed that it can be cycled four times using HCl as a desorbent. Especially in the simulated wastewater, Fe@BC exhibited remarkable adsorption for metal ions and organic matters, demonstrating that it was a promising adsorbent for removing Pb(II) from wastewater.

How to capture the high quality light field photography was one of important issue in computational photography. In fact, light field could be captured directly for all views or compressively reconstructed for each view just through one coded image. The latter kind of method was more feasible since only one exposure was needed for all views, among which dictionary-based light field reconstruction had been shown its effectiveness. In this paper, a more effective light field reconstruction method based on tensor dictionary was created. The proposed method is efficient because the trained tensor form dictionary can make better use of the rich structure of light field. Specifically, multiple small dictionaries were trained at the same time, and then were combined to a big dictionary using Kronecker product. Experimental results demonstrate the proposed method outperforms a state-of-the-art reconstruction method with the vector-form dictionary, in terms of higher reconstruction PSNR while reducing the scale of dictionary substantially.

Rubberized concrete (RC) has attracted more attention these years as it is an economical and environmental-friendly construction material. Normally, the uniaxial compressive strength (UCS) of RC needs to be evaluated before application. In this study, an evolutionary random forest model (BRF) combining random forest (RF) and beetle antennae search (BAS) algorithms was proposed, which can be used for establishing the relationship between UCS of RC and its key variables. A total number of 138 cases were collected from the literature to develop and validate the BRF model. The results showed that the BAS can tune the RF effectively, and therefore, the hyperparameters of RF were obtained. The proposed BRF model can accurately predict the UCS of RC with a high correlation coefficient (0.96). Furthermore, the variable importance was determined, and the results showed that the age of RC is the most significant variable, followed by water-cement ratio, fine rubber aggregate, coarse rubber aggregate, and coarse aggregate. This study provides a new method to access the strength of RC and can efficiently guide the design of RC in practice.

In this paper, we propose a new method to quantitatively evaluate the quality of the carrier phase observation signals of the BeiDou Navigation Satellite System (BDS) during weak and moderate geomagnetic storms. We take a moderate geomagnetic storm that occurred on 12 May 2021 during the 25th solar cycle as an example. The results show that the newly defined PAS (Percentage of Affected Satellites) index shows significant anomaly changes during the moderate geomagnetic storm. Its variation trend has good correlations with the geomagnetic storm Kp index and Dst index. The anomaly stations are mainly distributed in the equatorial region and auroral region in the northern and southern hemispheres. The proposed PAS index has a good indication for both BDS2 and BDS3 satellites. We further validated this index by calculating the Precise Point Position (PPP) positioning error. We found that the anomaly period of PAS has strong consistency with the abnormal period of PPP positioning accuracy. This study could provide methodological support for the evaluation of the signal quality and analysis of positioning accuracy for the BeiDou satellite navigation system under different space weather conditions.

Forest ecosystem services are crucial for sustaining ecological balance and supporting human well-being. This study quantified and analyzed ecosystem services—carbon storage, water conservation, and productivity—across four developmental stages (I, II, III, and IV) of poplar (Populus davidiana)–birch (Betula platyphylla) mixed natural secondary forests (MPB) in Weichang County, China, over the year 2022 using the InVEST and biomass models. Synergies and trade-offs between these ecosystem services were assessed using the constraint line method. The results showed that as the stand developed, carbon storage values gradually increased, while productivity remained relatively low during the initial three stages but exhibited a significant upward trend by Stage IV (p < 0.05). In contrast, water conservation did not exhibit a clear pattern with stand development. Across all stages, carbon storage exhibited a synergistic relationship with productivity, but a trade-off was observed with water conservation. In the first three stages, productivity and water conservation were in trade-off, yet by Stage IV, this relationship shifted to a weak synergy. The constraint line analysis revealed dynamic trade-offs between productivity, carbon storage, and water conservation. The findings emphasize the importance of adopting adaptive management strategies for MPB at different developmental stages to maximize the synergistic effects among ecosystem services.