scholarly journals Predicting Excavation-Induced Tunnel Response by Process-Based Modelling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Linlong Mu ◽  
Jianhong Lin ◽  
Zhenhao Shi ◽  
Xingyu Kang
Keyword(s):  
Soil Properties ◽  
Urban Areas ◽  
Field Measurements ◽  
Inverse Modelling ◽  
Machine Learning Techniques ◽  
Interaction Patterns ◽  
Support Structures ◽  
Interaction Processes ◽  
Excavation Support ◽  
Artificial Neural Network Ann

Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries.

scholarly journals Analysis of displacement of excavation based on inclinometer measurements

2012 ◽  
Vol 34 (4) ◽  
pp. 3-16 ◽  
Author(s):  
Karolina Gorska ◽  
Marek Wyjadłowski
Keyword(s):  
Theoretical Calculations ◽  
Back Analysis ◽  
Field Measurements ◽  
Soil Medium ◽  
Geotechnical Parameters ◽  
Angle Of Internal Friction ◽  
Material Parameters ◽  
Bored Pile ◽  
Excavation Process ◽  
Excavation Support

Abstract The article presents back analysis to estimate geotechnical parameters of fill layer. The agreement between field measurements and theoretical calculations was examined. Displacements of a cantilever CFA bored pile wall were monitored. The inclinometric measurements were taken directly after pile construction and according to excavation process. Over 200 calculation series were performed, with changing fill parameters. The calculations employed the actual geometric and material parameters of the pile wall, as well as geotechnical parameters of layered soil. The parameters estimated through back analysis were the angle of internal friction and Young’s modulus of fill layer. In the case discussed, pile wall cap displacement was the response of the system, and soil medium parameters were the input data. The agreement between theoretical calculations and inclinometer measurements was assessed in accordance with two functions. The measured horizontal displacements of excavation support structure assumed different values at the two inclinometer sites analysed. Back analysis results for these sites are approximately convergent for a final excavation depth.

Application of geophysical methods to evaluate hydrology and soil properties in urban areas

Urban Water ◽  
2001 ◽  
Vol 3 (3) ◽  
pp. 205-216 ◽  
Author(s):  
Larisa Pozdnyakova ◽  
Anatoly Pozdnyakov ◽  
Renduo Zhang
Keyword(s):  
Soil Properties ◽  
Urban Areas ◽  
Geophysical Methods

scholarly journals On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD-2 Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fengqin Chen ◽  
Jinbo Huang ◽  
Xianjun Wu ◽  
Xiaoli Wu ◽  
Arash Arabmarkadeh
Keyword(s):  
Least Squares ◽  
Machine Learning Techniques ◽  
Lower Percentage ◽  
Support Vector ◽  
Mean Deviation ◽  
Group Management ◽  
Learning Techniques ◽  
Data Points ◽  
Artificial Neural Network Ann ◽  
Rhamnolipid Biosurfactant

Biosurfactants are a series of organic compounds that are composed of two parts, hydrophobic and hydrophilic, and since they have properties such as less toxicity and biodegradation, they are widely used in the food industry. Important applications include healthy products, oil recycling, and biological refining. In this research, to calculate the curves of rhamnolipid adsorption compared to Amberlite XAD-2, the least-squares vector machine algorithm has been used. Then, the obtained model is formed by 204 adsorption data points. Various graphical and statistical approaches are applied to ensure the correctness of the model output. The findings of this study are compared with studies that have used artificial neural network (ANN) and data group management method (GMDH) models. The model used in this study has a lower percentage of absolute mean deviation than ANN and GMDH models, which is estimated to be 1.71%.The least-squares support vector machine (LSSVM) is very valuable for investigating the breakthrough curve of rhamnolipid, and it can also be used to help chemists working on biosurfactants. Moreover, our graphical interface program can assist everyone to determine easily the curves of rhamnolipid adsorption on Amberlite XAD-2.

scholarly journals Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3100
Author(s):  
Anusha Mairpady ◽  
Abdel-Hamid I. Mourad ◽  
Mohammad Sayem Mozumder
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Major Factors ◽  
The Cost ◽  
Design Factors

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

scholarly journals Traffic calming measures and their effect on the variation of speed

2016 ◽  
Author(s):  
Hernán Gonzalo Orden
Keyword(s):  
Urban Areas ◽  
Field Measurements ◽  
Speed Reduction ◽  
The Road ◽  
The North ◽  
Traffic Calming ◽  
Road Users ◽  
Technical Basis ◽  
Serious Injuries ◽  
North Of Spain

In recent years the number of deaths and serious injuries is decreasing in Spain, but, although the reduction outside the cities has been very strong, inside the urban areas, it has been smaller. This is especially hard if you look at the most vulnerable road users such as pedestrians and cyclists. In many accidents the speed factor appears closely linked not only to the number, but also to the severity of the accidents suffered inside the urban areas. Therefore, a reduction in the speed would improve the road safety. There are different measures known as "traffic calming measures" whose objectives are to reduce both the number and severity of accidents that occur on urban areas, by reducing the traffic flow through the streets, as well as the speed of the vehicles. However, the efficiency in speed reduction of each measure is not entirely known. That's the reason why they are implanted, in many cases, with no technical basis. The aim of this article is to show the effectiveness in reducing speed of some of the traffic calming measures. To this effect, field measurements were done on street sections with different types of traffic calming measures, in different places of a city of Burgos, in the north of Spain. These measurements were compared with other ones sited on other streets sections of similar characteristics but without traffic calming measures. Finally the conclusions are shown and some recommendations for improving their effectiveness are given.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4217

Prediction of the optimal FSW process parameters for joints using machine learning techniques

2021 ◽  
Vol 63 (12) ◽  
pp. 1104-1111
Author(s):  
Furkan Sarsilmaz ◽  
Gürkan Kavuran
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Friction Stir ◽  
Learning Techniques ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Fsw Parameters

Abstract In this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical properties such as ultimate tensile strength (UTS) and hardness of welded joints were determined experimentally. Secondly, these data were evaluated through modeling and the optimization of the FSW process as well as an optimal parametric combination to affirm tensile strength and hardness using a support vector machine (SVM) and an artificial neural network (ANN). In this study, a new ANN model, including the Nelder-Mead algorithm, was first used and compared with the SVM model in the FSW process. It was concluded that the ANN approach works better than SVM techniques. The validity and accuracy of the proposed method were proved by simulation studies.

scholarly journals Accuracy of the Dynamic Acoustic Map in a Large City Generated by Fixed Monitoring Units

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 412 ◽  
Author(s):  
Roberto Benocci ◽  
Chiara Confalonieri ◽  
Hector Eduardo Roman ◽  
Fabio Angelini ◽  
Giovanni Zambon
Keyword(s):  
Traffic Flow ◽  
Urban Areas ◽  
Low Cost ◽  
Traffic Noise ◽  
Large City ◽  
Field Measurements ◽  
Life Project ◽  
Noise Data ◽  
Comparable Accuracy ◽  
Monitoring Stations

DYNAMAP, a European Life project, aims at giving a real image of the noise generated by vehicular traffic in urban areas developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. The system has been implemented in two pilot areas located in the agglomeration of Milan (Italy) and along the Motorway A90 (Rome-Italy). The paper reports the final assessment of the system installed in the pilot area of Milan. Traffic noise data collected by the monitoring stations, each one representative of a number of roads (groups) sharing similar characteristics (e.g., daily traffic flow), are used to build-up a “real-time” noise map. In particular, we focused on the results of the testing campaign (21 sites distributed over the pilot area and 24 h duration of each recording). It allowed evaluating the accuracy and reliability of the system by comparing the predicted noise level of DYNAMAP with field measurements in randomly selected sites. To this end, a statistical analysis has been implemented to determine the error associated with such prediction, and to optimize the system by developing a correction procedure aimed at keeping the error below some acceptable threshold. The steps and the results of this procedure are given in detail. It is shown that it is possible to describe a complex road network on the basis of a statistical approach, complemented by empirical data, within a threshold of 3 dB provided that the traffic flow model achieves a comparable accuracy within each single groups of roads in the network.

scholarly journals Can Soil Electrical Resistivity Measurements Aid the Identification of Forest Areas Prone to Windthrow Disturbance?

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 234
Author(s):  
Marián Homolák ◽  
Erika Gömöryová ◽  
Viliam Pichler
Keyword(s):  
Electrical Resistivity ◽  
Soil Properties ◽  
Clay Content ◽  
Field Measurements ◽  
Beech Forest ◽  
Root Penetration ◽  
Predictive Tool ◽  
Entire Study ◽  
Resistivity Measurements ◽  
Soil Electrical Resistivity

This study investigates how certain forest soil properties influence the propensity of beech forests to windthrow disturbances. The field measurements of soil electrical resistivity were carried out in an old-growth natural beech forest where the soil has developed from Cainozoic sedimentary rock with mudstone–claystone stratigraphy. In 2014, the forest was hit by a severe windstorm, and dispersed windthrow occurred at certain plots. Apparent electrical resistivity measurements were performed to investigate whether some soil properties could influence the forest trees’ predisposition to windthrow. The increases in the clay content and soil bulk density below 30 cm were associated with weathered claystone and mudstone, which created a physiological barrier for deeper root penetration. The result of the χ 2 test suggested that the windthrown spots were not distributed evenly over the entire study area. They were mainly concentrated over approximately 50% of the area, and their positions coincided with low resistivity values, indicating low soil skeleton content, high clay content and soil moisture. Therefore, electrical resistivity tomography could be considered a useful predictive tool for reducing the risk of natural disturbances by preventive forest management.

scholarly journals Modeling the hydrophysical soil properties and comparative analysis for three systems of functions

2020 ◽  
Vol 175 ◽  
pp. 09016
Author(s):  
Vitaly Terleev ◽  
Roman Ginevsky ◽  
Viktor Lazarev ◽  
Aleksandr Nikonorov ◽  
Alexander Topaj ◽  
...  
Keyword(s):  
Porous Medium ◽  
Soil Moisture ◽  
Comparative Analysis ◽  
Hydraulic Conductivity ◽  
Soil Properties ◽  
Water Retention ◽  
Field Measurements ◽  
Practical Significance ◽  
Water Retention Capacity ◽  
Retention Capacity

A functional description of the hydrophysical properties of the soil as a capillary-porous medium is presented. The described functions of water retention capacity and hydraulic conductivity of the soil have common parameters, which are interpreted within the framework of physical and statistical concepts. The practical significance of the proposed functions lies in the fact that the volume of labor-intensive field measurements necessary, for example, for modeling the dynamics of soil moisture, is significantly reduced. To identify the parameters of these functions, it is sufficient to use data only on the water retention capacity of the soil. The parameters identified in this way can be used to predict the ratio of the hydraulic conductivity of the soil to the moisture filtration coefficient. The presented system of the hydrophysical functions of the soil is compared with world analogues using literature data on soils of different texture.

Sign in / Sign up

Export Citation Format

Share Document