Asian Journal of Civil Engineering最新文献

{"title":"Far-field earthquake response examination of RC buildings equipped with fluid viscous dampers","authors":"Abdelouahab Ras","doi":"10.1007/s42107-024-01194-7","DOIUrl":"10.1007/s42107-024-01194-7","url":null,"abstract":"<div><p>In the majority of seismic codes, the use of construction procedures to reduce the risks associated with seismic events is aimed at improving the resistance of structures by increasing their stiffness (as recommended in the Algerian anti-seismic code, RPA99/2003), while admitting a certain level of disorder. The recent history demonstrated (Boumerdes earthquake, Algeria, 2000 victims) that this method becomes ineffective when the structures responses are highly influenced by the shaking characteristics including the far-field, the near-fault, or quite high return frequency, especially in the case of major structures such as hospitals, civil defence barracks, etc., which must remain functional. The use of concepts to protect structures by reinforcing them with passive seismic energy dissipation devices has led to the appearance of a number of technics, such as the use of fluid viscous dampers (FVD). The present work aims to analyse the influence of passive seismic energy dissipation systems using fluid viscous dampers (FVD) on the maximum responses of reinforced concrete building exposed to far-field ground motions in terms of accelerations, displacements and different types of intern forces. Fast nonlinear time history analysis (FNA) with the employ of Boumerdes seismic signal of May 2003, were carried out using a computation software on a reinforced concrete building consisting of a ground floor and six storeys and fitted with FVD absorbers characterized by a linear force-velocity relationship. The results of the analyses performed to estimate the response of the RC frame with and without this type of energy dissipaters were discussed. The results highlighted the exceptional capacity of these absorbers to increase the dissipation potential of this type of building in far-field earthquake conditions without having to increase its rigidity by means of RC shear walls, thus reducing the quantity of materials required for its overall stability and its weight on the foundation soil.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"357 - 371"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple machine learning models for predicting annual energy consumption and demand of office buildings in subtropical monsoon climate","authors":"Jawad Ashraf,&nbsp;Rafi Azam,&nbsp;Asfia Akter Rifa,&nbsp;Md Jewel Rana","doi":"10.1007/s42107-024-01190-x","DOIUrl":"10.1007/s42107-024-01190-x","url":null,"abstract":"<div><p>Reducing a building’s energy use has many real-world applications. An early-stage design could have a quantitative foundation for energy-saving designs if energy consumption could be predicted quickly and accurately. The main issue that designers are currently dealing with is the incompatibility of building modelling and energy simulation software. In order to realize the flexibility of building energy systems, accurate and timely thermal load prediction for buildings is essential. Here, three machine learning (ML) models – Artificial Neural Network (ANN), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were used, for forecasting an office building’s load demand and energy usage. A case study building was selected and analysed via Autodesk Revit and Green Building Studio. For the modelling of ANN, 438 simulated data samples were created based on different design parameters considering different window, wall, roof materials and window to wall ratio, and meteorological conditions considering dew point, dry bulb, wet bulb temperature and relative humidity of seven major cities in Bangladesh. The findings show that the ANN model performs best the best in predicting annual electricity use with an R² value of 0.991 and annual load demand with an R² value of 0.995. The RMSE values ranged between 3.83 and 5.10 showing high accuracy of prediction between the three ML models. Afterwards SHAP analysis was used to analyse the input features effect on the energy consumption. Findings show that relative humidity, dry bulb temperature and pressure significantly affects the energy consumption.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"293 - 309"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study on the flexural behaviour and self-healing of fibre reinforced bacterial concrete beams","authors":"P. Sasikumar,&nbsp;M. A. Govindh,&nbsp;T. Subitha,&nbsp;A. Ananthakumar","doi":"10.1007/s42107-024-01201-x","DOIUrl":"10.1007/s42107-024-01201-x","url":null,"abstract":"<div><p>This study investigates reinforced concrete (RC) beams’ flexural behaviour, focusing on their strength and self-healing properties. The research explores how these beams respond under load and how they can recover from damage over time. The study aims to contribute valuable insights into concrete technology and structural engineering by analysing flexural behaviour and self-healing mechanisms. As the most crucial material for building construction, concrete inevitably develops cracks in its structures. These structural cracks diminish the lifespan of concrete elements and weaken their strength and durability. Traditional repair methods are commonly employed to address these cracks. However, these conventional rehabilitation techniques often incur high maintenance expenses and exacerbate environmental and health challenges. To tackle this, researchers have turned to the self-healing mechanism of calcite-precipitating bacteria. These bacteria automatically repair micro-cracks by filling them with calcite through a process known as Microbiologically Induced Calcite Precipitation (MICP). The resulting bacterial concrete possesses self-healing capabilities, allowing it to rectify cracks and maintain structural integrity while reducing maintenance costs. This innovative approach also extends the overall lifetime of concrete structures. Self-healing concrete promises to create more sustainable and durable structures, benefiting the environment and human health. An experimental and analytical study was conducted on the flexural behaviour of RC beams with the addition of steel fibres and <i>Bacillus licheniformis</i> bacteria. A total of six beams were examined, each with dimensions of 150 mm x 180 mm x 2500 mm. Two of these six RC beams were selected for the self-healing process. Steel fibres (ranging from 0.2 to 1.2%) and concentrated bacteria dosages (3%, 5%, and 7%) were added to the concrete mix based on the weight of the cement. The experimental study revealed that the optimal percentages for steel fibres and bacteria were 1% and 5%, respectively. Adding steel fibres and bacteria significantly enhanced the strength properties of the concrete. The flexural behaviour of the RC beams was investigated with various shear reinforcement spacings, including 100 mm and 80 mm. The main objectives of this study were to explore the load-carrying capacity, load-deflection behaviour, ductility, stiffness, and mode of failure related to self-healing in the RC beams. Notably, the inclusion of <i>Bacillus licheniformis</i> bacteria led to an improvement in the flexural behaviour of the RC beams. Additionally, a finite element model was developed and analyzed using ANSYS software. The results from the finite element model closely aligned with the experimental findings. Remarkably, the <i>Bacillus licheniformis</i> bacteria facilitated the healing of the RC beams within 112 days, making them a practical recommendation for crack healing applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"491 - 503"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142994790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: IoT based structural health monitoring of bridges using wireless sensor networks","authors":"Dathathreya Chakali,&nbsp;Hemaraju Pollayi,&nbsp;Praveena Rao","doi":"10.1007/s42107-024-01199-2","DOIUrl":"10.1007/s42107-024-01199-2","url":null,"abstract":"","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"451 - 451"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven advancements in structural health monitoring: comprehensive multi-state classification for three-dimensional structures","authors":"Sathish Polu,&nbsp;M. V. N. Sivakumar,&nbsp;Rathish Kumar Pancharathi","doi":"10.1007/s42107-024-01193-8","DOIUrl":"10.1007/s42107-024-01193-8","url":null,"abstract":"<div><p>Applying Machine Learning (ML) in Structural Health Monitoring (SHM) has proven to be highly effective. ML’s ability to handle large datasets and provide accurate predictions has made it a powerful tool in SHM. This study utilizes ML algorithms to categorize structural states within a three-dimensional frame. Nine structural states are examined in this research for dynamic analysis and classification. Three ML classifiers - Decision Tree, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms - were employed for the analysis. Python libraries are used to train and test the data with these algorithms. Dynamic tests were performed, exciting the model using a uniaxial Shake table with a 40 kg payload capacity and recording accelerometer responses. The classifiers’ performance was compared based on various classification metrics, such as accuracy, precision, recall, F1 score, and specificity, using confusion matrices and Receiver operating characteristic(ROC) curves. The study’s primary objective is to classify the structural states of a three-storied building frame through ML algorithms. The decision tree algorithm exhibits exceptional performance, achieving an impressive 94% accuracy rate with a specific 0.80 train-test split for set 1D data. Meanwhile, KNN impressively achieves a 92% accuracy, even with a 0.66 split for set 1 C data, maintaining a consistently high 90% accuracy level at lower splits of 0.50 and 0.33. The transition to three-channel data significantly enhances the decision tree’s accuracy by an impressive 23%, reaching 94%. A consistent 0.67 train-test split consistently yields reliable accuracy across all three algorithms. While the F1 score favours the decision tree (94%) for set 1 data and KNN (93%) for set 2 data, it’s important to note that ROC, Area under the curve(AUC) values, may vary due to class imbalances. This study provides valuable insights into algorithm selection, optimal split ratios, and relevant metrics for an efficient and robust approach to structural health monitoring.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"341 - 356"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RSM, SVM and ANN modeling of the properties of self-compacting concrete with natural mordenite-rich tuff and recycled glass","authors":"M. A. Bouzidi,&nbsp;A. Bouziane,&nbsp;N. Bouzidi","doi":"10.1007/s42107-024-01177-8","DOIUrl":"10.1007/s42107-024-01177-8","url":null,"abstract":"<div><p>The present paper is based on the prediction and the modeling of slump flow, l-box ratio and compressive strength of self-compacting concrete, containing natural mordenite-rich tuff as cement substitute and recycled glass as a partial replacement of fine aggregate. The study was carried out on experimental data constructed with a central composite design plan using response surface methodology (RSM), support vector machine (SVM) and artificial neural networks (ANN). Three variable process modelings were used for modeling and optimization: fine aggregate replacement from 0% to 50%, water cement ratio variation from 0.38 to 0.5 and cement substitution with natural mordenite-rich tuff from 0 to 30 %. The RSM, SVM and ANN models were evaluated and compared on the basis of the coefficient of determination (R²), adjusted coefficient of determination (R²_adj), mean square error (MSE) and root mean square error (RMSE). The model’s predictions were accurate with the experimental data with an R² close to 1. The results showed that the slump flow, l-box ratio and compressive strength were strongly influenced (p &lt; 0.01) by the chosen design parameters. The models were found to be robust tools to predict and capture the effects of the design parameters. The ANN outperforms all the regression models. The SVM models for slump flow, l-box ratio were more precise in their estimations in comparison to RSM models. However, in terms of compressive strength the RSM model approach was more accurate. The best optimization setting in terms of concrete properties and environmental consideration corresponds to a high tuff and recycled glass content (30% and 50 % respectively) and low W/C ratio (0.38).</p><h3>Graphical abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"89 - 106"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial neural networks and noncontact microwave NDT for evaluation of polypropylene fiber concrete","authors":"Hamsa Nimer,&nbsp;Rabah Ismail,&nbsp;Hashem Al-Mattarneh,&nbsp;Mohanad Khodier,&nbsp;Yaser Jaradat,&nbsp;Adnan Rawashdeh,&nbsp;Mohammad Rawashdeh","doi":"10.1007/s42107-024-01189-4","DOIUrl":"10.1007/s42107-024-01189-4","url":null,"abstract":"<div><p>Polypropylene fibers are extensively incorporated into reinforced concrete to enhance performance aspects such as crack resistance, flexural and tensile strength, fire resistance, and overall durability. However, current methods for evaluating factors like fiber inclusion percentage, distribution, and orientation within the concrete matrix are often limited, destructive, and time-consuming. This study explores developing and applying a non-contact microwave non-destructive method (NMNDT) for assessing polypropylene fiber-reinforced concrete. The NMNDT system measures the reflection and transmission characteristics of microwave signals through the concrete, correlating these properties with the physical and mechanical characteristics of the material. Key findings indicate a strong correlation between microwaves’ reflection and transmission properties, the quality of fiber distribution, and the fiber content within the concrete. For instance, the study found that the reflection coefficient (S₁₁) increased from 0.36 to 0.39, with fiber content varying from 0.5 to 1.5 kg/m³, while the transmission coefficient (S₂₁) decreased from 0.46 to 0.38 over the same range. The compressive strength of fiber-reinforced concrete was predicted with a correlation coefficient (R) of 0.98 using artificial neural networks (ANN). These microwave properties can predict mechanical properties such as tensile and compressive strength, with the ANN model achieving more than 97% accuracy. The study highlights the innovative potential of microwave technology as a non-invasive evaluation technique for polypropylene fiber-reinforced concrete, offering a promising avenue for rapid and non-destructive quality control and performance assessment. The integration of ANN further enhances the predictability of the strength properties of polypropylene fiber-reinforced concrete, significantly contributing to advancements in the field of fiber-reinforced concrete evaluation and quality control.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"273 - 292"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic response control of RC buildings by using tuned liquid dampers","authors":"Mohammed Hanif Yatnatti,&nbsp;M. B. Patil","doi":"10.1007/s42107-024-01191-w","DOIUrl":"10.1007/s42107-024-01191-w","url":null,"abstract":"<div><p>In today’s building industry, it is essential to manage increasing structural vibrations resulting from lower damping levels since taller, lighter, and more flexible structures are needed. The purpose of this study is to determine how well tuned liquid dampers, or TLDs, can reduce these vibrations. A 3D frame model with a liner TLD was subjected to a thorough computational study utilizing Finite Element Method (FEM) software under various seismic loading scenarios. The 1989 Loma Prieta Earthquake (PGA = 4.69 g), the 1994 North-ritz Earthquake (PGA = 5.92 g), the 2001 Bhuj Earthquake (PGA = 0.68 g), the 2011 New Zealand Earthquake (PGA = 7.86 g), and the 1995 Kobe Earthquake (PGA = 0.37 g) were the five notable historical earthquake scenarios that were simulated. The analysis includes a five-bay building with 10, 15, 20, 25, and 30 stories. It is situated on flat and sloping ground, with inclination of 15°, 20°, 25°, and 30°. The TLD’s efficiency was assessed using shear force, bending moments, and top-story displacement acceleration. The results demonstrate that TLDs significantly reduce structural vibrations; optimal performance is attained when the TLD is properly positioned at the top story and perfectly tuned to the structure’s associated frequency. Significantly, mistuning led to a significant decrease in damping efficiency, highlighting the vital requirement for accurate tuning. These results highlight TLDs’ potential as a useful tool for improving structural performance and resilience under dynamic loading scenarios. Guidelines for the best use of TLDs in seismic design procedures are provided by this research, which offers insightful information on the development and application of TLDs<b>.</b></p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"311 - 339"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification and parametric analysis of large overhang RC planar frames subjected to horizontal and combined horizontal–vertical earthquake loading","authors":"Dhiraj D. Ahiwale,&nbsp;Ajit N. Patil,&nbsp;Denise-Penelope N. Kontoni","doi":"10.1007/s42107-024-01197-4","DOIUrl":"10.1007/s42107-024-01197-4","url":null,"abstract":"<div><p>Eurocode 8 (Part 1) and Indian Standard IS 1893 (Part 1) recommend accounting for the impact of vertical earthquakes on structures with large overhangs, but they do not provide specific guidance for modeling large overhang reinforced concrete (RC) planar frames, particularly regarding the representation of vertical masses during vertical seismic events. This paper quantifies the modeling of vertical masses in large overhang RC planar frames subjected to Vertical Ground Motion (VGM) using eight near-fault seismic excitations in SAP2000 software. At first, a modal analysis was performed to determine the horizontal and vertical time periods of the large overhang RC planar frames. The nonlinear time-history analysis utilized both the horizontal earthquake component and the combined horizontal and vertical components. In accordance with ASCE 7-05 and ATC-63 standards, eight near-fault earthquake records were selected from the PEER NGA database, comprising four pulse-type and four non-pulse-type seismic excitations. The response of the large overhang RC planar frames was evaluated using parameters such as vertical displacement, column axial force, base shear, and base moments. Furthermore, a methodology has been proposed for the identification of critical parameters for large overhang RC planar frames subjected to vertical ground motion (VGM). Additionally, a parametric study has been conducted on single-story to four-story large overhang RC planar frames using twelve non-pulse near-fault vertical earthquake events. The critical earthquake-related parameters selected include the vertical peak ground acceleration, the horizontal peak ground acceleration, the vertical-to-horizontal peak ground acceleration ratio, the predominant spectral time period for vertical ground acceleration, and the predominant spectral time period for horizontal ground acceleration. The structure-related critical parameters considered are vertical displacement at top joints, axial force, base shear, and base moments. This study demonstrates that the critical parameters related to a large overhang RC planar frame and the vertical ground motion should be considered in modern seismic design codes.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"401 - 430"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recycling glass waste into concrete aggregates: enhancing mechanical properties and sustainability","authors":"Govardhan Naik B,&nbsp;Nakkeeran G,&nbsp;Dipankar Roy","doi":"10.1007/s42107-024-01181-y","DOIUrl":"10.1007/s42107-024-01181-y","url":null,"abstract":"<div><p>Glass trash management is a major global concern. This waste glass is sourced from various sources, including glass containers, flat glass, and household glass. The situation is becoming more challenging as the amount of waste glass continues expanding and available landfill space grows limited. This study explores the challenges faced in reusing and recycling glass in concrete, concentrating on the principles of the Circular Economy. In addition to this comprehensive review, it collects 2000 publications from 2000 to 2023 originating from 50 nations and appears in 150 research journal articles concentrating on using recycled glass material in concrete. This review offers a thorough examination of research on the substitution of recycled glass trash for aggregates when producing concrete. It focuses on analyzing the substitution that will affect these materials' mechanical and new characteristics. In addition, the review seeks to expand knowledge regarding the possible uses of recycled glass as a sustainable resource in the concrete industry. The results show that adding glass trash to concrete improves its qualities and helps create a more circular and sustainable economy.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"1 - 19"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}