scholarly journals Prediction of Change Rate of Settlement for Piled Raft Due to Adjacent Tunneling Using Machine Learning

2021 ◽  
Vol 11 (13) ◽  
pp. 6009
Author(s):  
Dong-Wook Oh ◽  
Suk-Min Kong ◽  
Yong-Joo Lee ◽  
Heon-Joon Park
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Prediction Models ◽  
Laboratory Model ◽  
Change Rate ◽  
Piled Raft ◽  
Pile Diameter ◽  
Adjacent Structures ◽  
And Behavior ◽  
Pile Length

For tunneling in urban areas, understanding the interaction and behavior of tunnels and the foundation of adjacent structures is very important, and various studies have been conducted. Superstructures in urban areas are designed and constructed with piled rafts, which are more effective than the conventional piled foundation. However, the settlement of a piled raft induced by tunneling mostly focuses on raft settlement. In this study, therefore, raft and pile settlements were obtained through 3D numerical analysis, and the change rate of settlement along the pile length was calculated by linear assumption. Machine learning was utilized to develop prediction models for raft and pile settlement and change rate of settlement along the pile length due to tunneling. In addition, raft settlement in the laboratory model test was used for the verification of the prediction model of raft settlement, derived through machine learning. As a result, the change rate of settlement along the pile length was between 0.64 and −0.71. In addition, among features, horizontal offset pile tunnel had the greatest influence, and pile diameter and number had relatively little influence.

scholarly journals Prediction of Hourly Effect of Land Use on Crime

2018 ◽  
Vol 8 (1) ◽  
pp. 16 ◽  
Author(s):  
Irina Matijosaitiene ◽  
Peng Zhao ◽  
Sylvain Jaume ◽  
Joseph Gilkey Jr
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Logistic Regression ◽  
Hot Spots ◽  
Urban Areas ◽  
Prediction Models ◽  
Hot Spot ◽  
Time Range ◽  
Land Uses ◽  
Police Departments

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.

Piled Raft Structure Finite Element Analysis of Composite Foundation Settlement in Da XI Passenger Dedicated Line

2014 ◽  
Vol 937 ◽  
pp. 438-443
Author(s):  
Xiao Tong Ma ◽  
Guang Long Liu
Keyword(s):  
Finite Element ◽  
Composite Foundation ◽  
Foundation Settlement ◽  
Pile Spacing ◽  
Finite Element Software ◽  
Piled Raft ◽  
Pile Diameter ◽  
Foundation Treatment ◽  
Pile Length ◽  
Passenger Dedicated Line

Composite foundation settlement of piled raft structure in Da Xi passenger dedicated line is analyzed by the large finite element software MIDAS/GTS and established calculation model of foundation treatment. The problem of pile-soil contact is highlighted in the trail and analyzes the settlement nephogram and pile-soil stress nephogram. On this basis the foundation settlement factors was analyzed systematically that focus on the elastic modulus of pile, pile spacing, pile diameter and pile length in foundation treatment, especially for the characteristics parameters of contact element. Result shows that increasing the pile modulus, pile diameter, pile length and decreasing the pile spacing is all conducive to reducing settlement. The best advice is got that the pile diameter should be not more than 0.5m, pile length not more than 27m and the pile spacing be around 2m.

Elasto-Plastic Design of Piled Raft Foundation

2012 ◽  
Vol 446-449 ◽  
pp. 588-591
Author(s):  
Ai Hong Han ◽  
Hui Jun Zheng
Keyword(s):  
Small Diameter ◽  
Plastic Property ◽  
Plastic Design ◽  
Piled Raft ◽  
Pile Diameter ◽  
Piled Raft Foundation ◽  
Raft Foundation ◽  
Full Capacity ◽  
Pile Length ◽  
Better Than

When the loading sustained by the foundation is large, employing piled raft foundation is one of the best solutions. In the elasto-plastic design of piled raft, piles could improve the differential settlement and reduce raft thickness. As the raft sustains high earth and water pressures, by reducing the span length of raft and excavation depth, one can get economic design. Using elasto-plastic property of the pile is a better method to avoid increasing the pile length and pile diameter and making full capacity of the piled raft foundation in design compared to normal piled raft. With adoption of few small diameter piles, the raft thickness could be reduced significantly. This makes it much better than raft foundation.

scholarly journals Assessing the effect of governing parameters of bearing capacity determination of small piled rafts on clay soil

2021 ◽  
Vol 14 (22) ◽  
Author(s):  
Shivanand Mali ◽  
Baleshwar Singh
Keyword(s):  
Shear Force ◽  
Bending Moment ◽  
Pile Spacing ◽  
Piled Raft ◽  
Pile Diameter ◽  
Piled Raft Foundation ◽  
Raft Foundation ◽  
Maximum Shear Force ◽  
Settlement Ratio ◽  
Pile Length

Abstract In the present study, a small piled raft foundation has been simulated numerically through PLAXIS 3-D software. The objective of this study was to investigate the effect of governing parameters such as pile length, pile spacing, pile diameter, and number of piles on the settlement and load-bearing behavior of piled raft, so as to achieve the optimum design for small piled raft configurations. An optimized design of a piled raft is defined as a design with allowable center and differential settlements and satisfactory bearing behavior for a given raft geometry and loading. The results indicated that, with increase in pile length, pile spacing, pile diameter, and number of piles, both the center settlement ratio and differential settlement ratio decreased. The load-bearing capacity of piled raft increased with increase in pile length, pile spacing, pile diameter, and number of piles. Furthermore, the percentage load carried by the piles increased as the pile length, pile spacing, pile diameter, and number of piles increased. The bending moment and shear force in corner pile are noted to be more, and they decreased towards the center pile. With increase in pile length, the maximum raft bending moment decreased, whereas the maximum shear force in the raft increased. Further, with increase in pile spacing, pile diameter, and number of piles, the maximum bending moment and maximum shear force in the raft increased. The optimum parameters for the piled raft foundation can be selected efficiently with the consideration of maximum bending moment and maximum shear force while designing the piled raft foundation. Thus, the results of this study can be used as guidelines for achieving optimum design for small piled raft foundation.

scholarly journals Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Huajing Zhao ◽  
Wei Liu ◽  
Hao Guan ◽  
Chunqing Fu
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Short Term Memory ◽  
Back Propagation ◽  
Machine Learning Algorithms ◽  
Diaphragm Wall ◽  
Computational Time ◽  
Prediction Errors ◽  
Wall Deflection ◽  
Adjacent Structures

For the concrete diaphragm wall (CDW) supported excavation, excessive wall deflection may pose a potential risk to adjacent structures and utilities in urban areas. Therefore, it is of significance to predict the CDW deformation with high accuracy and efficiency. This paper investigates three machine learning algorithms, namely, back-propagation neural network (BPNN), long short-term memory (LSTM), and gated recurrent unit (GRU), to predict the excavation-induced CDW deflection. A database of field measurement collected from an excavation project in Suzhou, China, is used to verify the proposed models. The results show that GRU exhibits lower prediction errors and better robustness in 10-fold cross validation than BPNN and executes less computational time than LSTM. Therefore, GRU is the most suitable algorithm for CDW deflection prediction considering both effectiveness and efficiency, and the predicted results can provide reasonable assistance for safety monitoring and early warning strategies conducted on the construction site.

PERAN MASYARAKAT TERHADAP PEMBAYARAN RETRIBUSI SAMPAH DI KOTA SAMARINDA

2018 ◽  
Vol 2 (1) ◽  
pp. 33
Author(s):  
Abd Rachim AF,
Keyword(s):  
Waste Management ◽  
Population Growth ◽  
Frequency Analysis ◽  
Urban Areas ◽  
Relative Frequency ◽  
Environmental Problems ◽  
Living Standards ◽  
The Public ◽  
And Behavior

One of the environmental problems in urban areas is the pollution caused by garbage. The waste problem is caused by various factors such as population growth, living standards changes, lifestyles and behavior, as well as how the waste management system. This study aims to determine how the role of society to levy payments garbage in Samarinda. This research was descriptive; where the data is collected then compiled, described and analyzed used relative frequency analysis. The participation of the public to pay a "levy junk", which stated to pay 96.67%, for each month and the rates stated society cheap, moderate and fairly, respectively 46.08%, 21.21%, 21.04%. Base on the data , the role of the community to pay "levy junk" quite high.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

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