Adaptive neuro-fuzzy modeling for the swelling potential of compacted soils

This paper aims to present the usability of an adaptive neuro fuzzy inference system (ANFIS) for the prediction swelling potential of the compacted soils that are important materials for geotechnical purposes such as engineered barriers for municipal solid waste, earth dams, embankment and roads. In this study the swelling potential that is also one of significant parameters for compacted soils was modeled by ANFIS. For the training and testing of ANFIS model, data sets were collected from the tests performed on compacted soils for different geotechnical application in Nigde. Four parameters such as coarse-grained fraction ratio (CG), fine-grained fraction ratio (FG), plasticity index (PI) and maximum dry density (MDD) were presented to ANFIS model as inputs. The results obtained from the ANFIS models were validated with the data sets which are not used for the training stage. The analyses revealed that the predictions from ANFIS model are in sufficient agreement with test results.     

Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering

This paper presents a review of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in current use for geotechnical engineering-based studies, as well as some applications employed in resonant column testing, triaxial testing, and liquefaction triggering. Over the last few years, ANFIS has been used in many geotechnical engineering problems. A review of published literature reveals that ANFIS has been used successfully in footing response prediction, modeling of the friction angle of soils, tunnel stability analysis, estimating current-induced scour depth around pile groups, prediction of unconfined compressive strength, swelling potential of soils and permeability estimation. Some works have been selected to be described, as the others are acknowledged. The paper also presents ANFIS based models for coarse rotund sand–mica mixtures tested in triaxial and resonant column testing apparatuses and a modeling for liquefaction triggering.     

基于ANFIS的单桩竖向极限承载力预测模型

人工神经网络已应用在岩土工程的各个方面。针对常用的BP网络的不足之处,建立了基于自适应神经模糊推理系统（ANFIS）的单桩竖向极限承载力预测模型。利用文献中桩的载荷试验数据来训练ANFIS网络,确定了网络参数。研究结果表明,同常用的BP网络相比,ANFIS预测模型具有学习速度快,拟合能力较好,训练结果唯一等优点,该方法是一种有效地预测单桩极限承载力的方法。     

“Tunnel Information and Analysis System”: A Geotechnical Database for Tunnels

Existing experience from the design and construction of underground works is of major importance for the improvement of the construction methods and procedures in tunnelling, especially under adverse and complex geological and geotechnical conditions. This experience can be of great value to geotechnical engineers and engineering geologists, if data acquired through the ground investigation, the design and the construction is systematically collected, categorized and stored in a properly structured database that enables a targeted access to it, as well as to proceed to correlations and analysis, based on engineering criteria. Such a database should be carefully designed to “connect” all available data through all the phases of a tunnel project and premises deep knowledge from the geological and geotechnical investigation to the final design and construction. In order to make substantial use of the experience accumulated from the construction of a great number of tunnels, a database named Tunnel Information and Analysis System (TIAS) was developed. The data source for TIAS database was 62 tunnels of the Egnatia Highway in northern Greece, many of which have been constructed under difficult geological conditions in weak rock masses. The data processed by TIAS came from a variety of sources such as geological mapping, boreholes, laboratory and in situ testing, geotechnical classifications, engineering geological behaviour, groundwater, design parameters, information concerning immediate support measures, construction records and cost. The purpose of the system, besides incorporating extended and multi-source data for easy access, is to provide a tool for turning data into usable information for the comparison of anticipated and encountered geological and geotechnical conditions, the evaluation of geotechnical classification and design methods and the relations regarding rock mass conditions and behaviour and immediate support methods and types.     

Prediction of the unconfined compressive strength of compacted granular soils by using inference systems

Adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have been extensively used to predict different soil properties in geotechnical applications. In this study, it was aimed to develop ANFIS and ANN models to predict the unconfined compressive strength (UCS) of compacted soils. For this purpose, 84 soil samples with different grain-size distribution compacted at optimum water content were subjected to the unconfined compressive tests to determine their UCS values. Many of the test results (for 64 samples) were used to train the ANFIS and the ANN models, and the rest of the experimental results (for 20 samples) were used to predict the UCS of compacted samples. To train these models, the clay content, fine silt content, coarse silt content, fine sand content, middle sand content, coarse sand content, and gravel content of the total soil mass were used as input data for these models. The UCS values of compacted soils were output data in these models. The ANFIS model results were compared with those of the ANN model and it was seen that the ANFIS model results were very encouraging. Consequently, the results of this study have important findings indicating reliable and simple prediction tools for the UCS of compacted soils.     

Prediction of the Rock Mass Diggability Index by Using Fuzzy Clustering-Based,ANN and Multiple Regression Methods

Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.     

圆弧破坏边坡反演设计的ANN方法与ANFIS方法的比较研究

圆弧破坏边坡反演设计方法,是根据边坡的岩土力学参数及边坡高度,在确保安全系数满足要求的前提下,对圆弧破坏边坡的边坡角进行设计的一种新方法。建立圆弧破坏边坡反演设计方法的关键,就是要对大量稳定圆弧破坏边坡实例加以集成,以建立从{ , c, , H, F}到 的映射。文中收集了大量稳定圆弧破坏边坡实例,采用ANN和ANFIS对其进行了集成,分别建立了圆弧破坏边坡反演设计的ANN方法和ANFIS方法,进而又采用圆弧破坏边坡实例对两者的反演结果进行了比较。结果表明,ANFIS更适合于用来建立圆弧破坏边坡反演设计方法。     

Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data

This study explores the potential of adaptive neuro-fuzzy inference systems (ANFIS) for prediction of the ultimate axial load bearing capacity of piles (P_u) using cone penetration test (CPT) data. In this regard, a reliable previously published database composed of 108 datasets was selected to develop ANFIS models. The collected database contains information regarding pile geometry, material, installation, full-scale static pile load test and CPT results for each sample. Reviewing the literature, several common and uncommon variables have been considered for direct or indirect estimation of P_u based on static pile load test, cone penetration test data or other in situ or laboratory testing methods. In present study, the pile shaft and tip area, the average cone tip resistance along the embedded length of the pile, the average cone tip resistance over influence zone and the average sleeve friction along the embedded length of the pile which are obtained from CPT data are considered as independent input variables where the output variable is P_u for the ANFIS model development. Besides, a notable criticism about ANFIS as a prediction tool is that it does not provide practical prediction equations. To tackle this issue, the obtained optimal ANFIS model is represented as a tractable equation which can be used via spread sheet software or hand calculations to provide precise predictions of P_u with the calculated correlation coefficient of 0.96 between predicted and experimental values for all of the data in this study. Considering several criteria, it is represented that the proposed model is able to estimate the output with a high degree of accuracy as compared to those results obtained by some direct CPT-based methods in the literature. Furthermore, in order to assess the capability of the proposed model from geotechnical engineering viewpoints, sensitivity and parametric analyses are done.     

对岩土工程数值分析的几点思考

首先,介绍了笔者对我国岩土工程数值分析现状的调查结果及分析,然后,分析了采用连续介质力学分析岩土工程问题的关键,并讨论分析了岩土本构理论发展现状,提出对岩土本构理论发展方向的思考,最后对数值分析在岩土工程分析中的地位作了分析。分析表明,岩土工程数值分析结果是岩土工程师在岩土工程分析过程中进行综合判断的重要依据之一;采用连续介质力学模型求解岩土工程问题的关键是如何建立岩土的工程实用本构方程;建立多个工程实用本构方程结合积累大量工程经验才能促使数值方法在岩土工程中由用于定性分析转变到定量分析。     

土方直剪试验数据采集管理系统的设计与实践

直剪试验被广泛应用于工程勘察中用来测定土的抗剪强度。针对直剪试验中,试验持续时间长、产生的测量数据多等特点,设计了一种基于VB6.0与USB7660多功能数据采集模块的数据采集与管理系统。详细介绍了系统的设计原理和实现方法,并对系统各部分的设计思路及实现过程进行了详细说明。实验表明,该系统基本能够满足在岩土工程勘察土方直剪试验过程中对测量数据采集与分析的要求。     

岩土工程勘察中的质量控制探讨

本文阐述了岩土工程勘察成果质量的重要性,重点论述了岩土工程勘察工序流程的质量管理要点,这对于岩土工程勘察专业宏观管理到指导具有实际意义。     

计算机辅助岩土工程(CAGE)概论

本文简要论述了计算机辅助岩土工程(CAGE)的提出、发展、方法及主要内容, 最后给出了计算机辅助核电工程勘察实例。     

Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS

River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved.     

深圳轨道交通建设中的若干岩土工程问题

通过深圳轨道交通建设多年的具体实践,对该地区轨道交通（地铁）建设中涉及到的勘察、基坑支护、盾构施工等方面的岩土工程问题进行了总结,包括有关技术要点、适用工法、解决方案、经验教训和大量具体事例。     

Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran)

Drought is accounted as one of the most natural hazards. Studying on drought is important for designing and managing of water resources systems. This research is carried out to evaluate the ability of Wavelet-ANN and adaptive neuro-fuzzy inference system (ANFIS) techniques for meteorological drought forecasting in southeastern part of East Azerbaijan province, Iran. The Wavelet-ANN and ANFIS models were first trained using the observed data recorded from 1952 to 1992 and then used to predict meteorological drought over the test period extending from 1992 to 2011. The performances of the different models were evaluated by comparing the corresponding values of root mean squared error coefficient of determination (R ²) and Nash–Sutcliffe model efficiency coefficient. In this study, more than 1,000 model structures including artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and Wavelet-ANN models were tested in order to assess their ability to forecast the meteorological drought for one, two, and three time steps (6 months) ahead. It was demonstrated that wavelet transform can improve meteorological drought modeling. It was also shown that ANFIS models provided more accurate predictions than ANN models. This study confirmed that the optimum number of neurons in the hidden layer could not be always determined using specific formulas; hence, it should be determined using a trial-and-error method. Also, decomposition level in wavelet transform should be delineated according to the periodicity and seasonality of data series. The order of models with regard to their accuracy is as following: Wavelet-ANFIS, Wavelet-ANN, ANFIS, and ANN, respectively. To the best of our knowledge, no research has been published that explores coupling wavelet analysis with ANFIS for meteorological drought and no research has tested the efficiency of these models to forecast the meteorological drought in different time scales as of yet.     

Site Soundings Density for Geotechnical Investigation with Combined Fuzzy and Probabilistic Input Information

A fuzzy sets decision support system is proposed for geotechnical site investigation. The system considers parameters such as geology and soil variability that affect the required number of soundings to adequately characterize a site. It permits also to consider vagueness and lack of information. On the basis of available qualitative and quantitative information, the system allows estimating, for common projects, the number of site soundings. Monte Carlo simulations of entry ranges, where each point has a uniform probability distribution, permit to arrange the output in form of histograms fitted with probability functions. The cases presented show that the fuzzy inference system can be used as a systematic decision support for engineers dealing with site characterization.     

Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data

Soil temperature has an important role in agricultural, hydrological, meteorological and climatological studies. In the present research, monthly mean soil temperature at four different depths (5, 10, 50 and 100 cm) was estimated using artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). The monthly mean soil temperature data of 31 stations over Iran were employed. In this process, the data of 21 and 10 stations were used for training and testing stages of used models, respectively. Furthermore, the geographical information including latitude, longitude and altitude as well as periodicity component (the number of months) was considered as inputs in the mentioned intelligent models. The results demonstrated that the ANN and ANFIS models had good performance in comparison with the GEP model. Nevertheless, the ANFIS generally performed better than ANN model.     

自适应神经模糊推理系统(ANFIS)在水文模型综合中的应用

由于目前已有很多比较成熟的流域水文模型,因此我们可以选用几个流域水文模型进行并行运算,来同时模拟流域降雨—径流关系。在相同的降雨输入情况下,不同模型得到的模拟流量必然会有所不同,模型效率系数和模拟精度也会不同。因此,如何将不同模型的模拟结果进行综合以进一步提高流量模拟精度是一个关键问题。本文选用自适应神经模糊推理系统(ANFIS)作为水文模型综合平台,以牧马河流域为试验区域,对两个并行运算水文模型(三水源新安江模型和总径流响应模型)的结果进行综合处理,得到了更稳健的流量模拟结果,大大提高了模型效率和模拟精度。该方法值得在实践中借鉴。     

岩土工程勘察、设计与施工一体化模式探讨研究

建筑工程勘察、设计和施工一体化模式,逐渐成为国内大型勘察设计企业新的核心竞争力,亦是国际国内经济一体化和市场经济发展的必然趋势。本文结合目前国内主要从事岩土工程的勘察、设计或施工企业对项目的运作情况,提出了岩土工程勘察、设计和施工一体化运作模式。通过对目前国内岩土工程勘察、设计和施工独立运作模式和一体化模式的对比研究和分析,结合典型的工程实例,总结和分析了采用一体化或近似一体化模式运作项目的优劣和经验。针对目前国内大型勘察设计企业实施岩土工程勘察、设计和施工一体化模式的困难进行了探讨并提出相关建议,供相关部门或同行参考。     

Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process

Urban, industrial, and tourist developments are considered of high priority in Egypt. In the current research, the site suitability investigation for rating the different environmental, geological, and geotechnical conditions facing civil engineering projects were assessed using a geographic information system (GIS) multi-criteria approach. The study area is one of the most promising areas for urban and touristic as well as industrial developments in Egypt, which is located on the NW coast of the Gulf of Suez. This area may face several geo-environmental problems that will limit its suitability for civil projects. Weighted GIS model, which integrates different types of data sources, such as land use/cover, geological, geomorphological, geophysical, environmental, remote sensing, and field data, can be achieved to create a site suitability map. In this paper, an analytical hierarchy process approach has been used to develop the weighted model for different factors. As a result of this study, areas of potential geotechnical and geo-environmental hazards that could impact the design and construction of civil projects were identified. Therefore, changes can be made early in the design process before significant design efforts are being invested.