The use of artificial neural networks in adiabatic curves modeling

Adiabatic hydration curves are the most suitable data for temperature calculations in concrete hardening structures. However, it is very difficult to predict the adiabatic hydration curve of an arbitrary concrete mixture. The idea of modeling adiabatic temperature rise during concrete hydration with the use of artificial neural networks was introduced in order to describe the adiabatic hydration of an arbitrary concrete mixture, depending on factors which influence the hydration process of cement in concrete. The influence of these factors was determined by our own experiments. A comparison between experimentally determined adiabatic curves and adiabatic curves, evaluated by proposed numerical model shows that artificial neural networks can be used to predict adiabatic hydration curves effectively. This model can be easily incorporated in the computer programs for prediction of the thermal fields in young concrete structures, implemented in the finite element or finite difference codes. New adiabatic hydration curves with some other initial parameters of the concrete mixture can be easily included in this model in order to expand the range of suitability of artificial neural networks to predict the adiabatic hydration curves.     

Fire analysis of steel frames with the use of artificial neural networks

The paper presents an alternative approach to the modelling of the mechanical behaviour of steel frame material when exposed to the high temperatures expected in fires. Based on a series of stress-strain curves obtained experimentally for various temperature levels, an artificial neural network (ANN) is employed in the material modelling of steel. Geometrically and materially, a non-linear analysis of plane frame structures subjected to fire is performed by FEM. The numerical results of a simply supported beam are compared with our measurements, and show a good agreement, although the temperature-displacement curves exhibit rather irregular shapes. It can be concluded that ANN is an efficient tool for modelling the material properties of steel frames in fire engineering design studies.     

Exploring the possibility of assessing the damage degree of liquefaction based only on seismic records by artificial neural networks

This study presents a new approach to determine the damage degree of liquefaction caused by a large earthquake. We propose an artificial neural network (ANN) model based only on the seismic records of ground and define the degree of liquefaction “DDL” as a damage index. This ANN model predicts the degree of excess pore water pressure increase as the correct output label based on the seismic records obtained from the three-dimensional shaking table test. The proposed model achieved high accuracy, and the outcomes from training data indicated that the ANN model is suitable to function as a liquefaction assessment system. Further, to evaluate the applicability of the proposed ANN model in the real world, the datasets of waves from three actual seismic records were input to the ANN as validation data. The DDL judgment obtained was a good fit with the real phenomena observed.     

A constrained neural network model for soil liquefaction assessment with global applicability

A constrained back propagation neural network (C-BPNN) model for standard penetration test based soil liquefaction assessment with global applicability is developed, incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships. For its development and validation, a comprehensive liquefaction data set is compiled, covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries. The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints, input data selection, and computation and calibration procedures. Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model, and are thus adopted as constraints for the C-BPNN model. The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice. The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.     

基于BP神经网络的砂土地震液化分析

基于已有的砂土地震液化资料，利用3层BP神经网络模型，对国外7个地震现场实例进行了预测，网络输出结果与实际情况十分吻合。实例研究表明，神经网络用于预测砂土地震液化是有效而可行的。     

神经网络在结构损伤识别中的应用

探讨了用神经网络对混凝土结构裂缝进行损伤识别和定位的方法,以一矩形截面悬臂梁为研究对象,通过完好结构和损伤结构的有限元分析,并进行了单处损伤和多处损伤的定位研究,数值仿真结果表明,该方法对于实际工程结构的损伤识别具有一定的指导意义。     

A neural networks approach for cost flow forecasting

Artificial neural networks, which simulate neuronal systems of the brain, are useful methods that have attracted the attention of researchers in many disciplinary areas. They have many advantages over traditional methods in situations where the input-output relationship of the system under study is not explicitly known. This paper investigates the feasibility of using neural networks for predicting the cost flow of construction projects, explains the need for cost flow forecasting, and demonstrates the limitation of the existing models. It then introduces neural networks as an alternative approach to those mathematical and statistical methods. The method used in collecting data and modelling the cost flow is described. Results of the testing are presented and discussed.     

Modeling and predicting building's energy use with artificial neural networks: Methods and results

This paper discusses how neural networks, applied to predict energy consumption in buildings, can advantageously be improved, guided by statistical procedures, such as hypothesis testing, information criteria and cross validation. Recent literature has provided evidence that such methods, commonly used independently, when exploited together, can improve the selection and estimation of neural models.We use such an approach to design feed forward neural networks for modeling energy use and predicting hourly load profiles, where both the relevance of input variables and the number of free parameters are systematically treated. The model building process is divided in three parts: (a) the identification of all potential relevant input, (b) the selection of hidden units for this preliminary set of inputs, through an additive phase and (c) the remove of irrelevant inputs and useless hidden units through a subtractive phase.The predictive performance of short term predictors is also examined with regard to prediction horizon. A comparison of the predictive ability of a single-step predictor iteratively used to predict 24 h ahead and a 24-step independently designed predictor is presented.The performance of the developed models and predictors was evaluated using two different data sets, the energy use data of the Energy Prediction Shootout I contest, and of an office building, located in Athens. The results show that statistical analysis as an integral part of neural models, gives a valuable tool to design simple, yet efficient neural models for building energy applications.     

The use of artificial neural networks to predict the effect of sulphate attack on the strength of cemented paste backfill

The growing use of cemented paste backfill (CPB) as a ground support method in mining and also as an environmentally friendly alternative for mine waste disposal demands a better understanding of the different processes that affect its strength. Due to its nature as cement based material, CPB is prone to the progressive loss of strength with sulphate attacks under certain conditions. The paper provides a background to sulphate attacks in CPB and artificial neural networks (ANN) and presents a model to predict the unconfined compressive strength of a CPB under sulphate attack, based on different water cement ratios, binder composition and binder content.     

用人工神经网络预测饱和砂土的液化势

介绍了预测饱和砂土的液化势的人工神经网络法，结合工程实例详细阐述了该方法的建模、预测结果与实测值较为吻合，表明在工程抗震中运用这一方法的有效性。     

The applicability of neural networks in the determination of soil profiles

The drilling of a number of boreholes to determine the soil profile of a given area is time consuming and costly. This paper describes estimated soil profiles obtained using a model based on artificial neural networks (ANN). ANN is a powerful data-modelling tool capable of capturing and representing complex relationships between input and output. It deals with many multi-variate problems for which an exact analytical model does not exist or is very difficult and time consuming to develop. The main settlement in the Adapazari region was selected to demonstrate the capability of such model. The results obtained using ANN are promising when compared with the soil profile obtained from boreholes.      

人工神经网络在结构工程中的应用和发展

依据神经网络原理及其自身的特点，对其应用在结构优化设计、结构分析及可靠度分析等方面进行了综述和研究，并在此基础上分析了神经网络在结构工程中的研究方向。     

Deep convolutional neural networks for uncertainty propagation in random fields

The development of a reliable and robust surrogate model is often constrained by the dimensionality of the problem. For a system with high‐dimensional inputs/outputs (I/O), conventional approaches usually use a low‐dimensional manifold to describe the high‐dimensional system, where the I/O data are first reduced to more manageable dimensions and then the condensed representation is used for surrogate modeling. In this study, a new solution scheme for this type of problem based on a deep learning approach is presented. The proposed surrogate is based on a particular network architecture, that is, convolutional neural networks. The surrogate architecture is designed in a hierarchical style containing three different levels of model structures, advancing the efficiency and effectiveness of the model in the aspect of training. To assess the model performance, uncertainty quantification is carried out in a continuum mechanics benchmark problem. Numerical results suggest the proposed model is capable of directly inferring a wide variety of I/O mapping relationships. Uncertainty analysis results obtained via the proposed surrogate have successfully characterized the statistical properties of the output fields compared to the Monte Carlo estimates.     

The application of neural networks to rock engineering systems (RES)

This paper proposes a new approach for applying neural networks in Rock Engineering Systems (RES) based on the learning abilities of neural networks. By considering the analysis of the coding methods for the interaction matrix in RES and the learning processes of neural networks such as the Back Propagation (BP) method, neural networks can provide a useful mapping from system inputs to system outputs for rock engineering, so that the influence of inputs on outputs can be obtained. Then the results of the neural network analysis can be presented in a similar way to the global interaction matrix used in RES to present the fully-coupled system results. The neural network procedures are explained first, with illustrative demonstrations for simultaneous equations. Then, the link with the RES type of analysis is explained, together with some demonstration examples for rock engineering data sets. The specific analysis procedure is presented and then wider rock engineering examples are given relating to the characteristics of rock masses and engineering parameters. The main presentation tools used in this neural network approach are the Relative Strength Effect (RSE) and the Global Relative Strength Effect (GRSE) matrix. There is discussion of the value of this approach and an indication of the likely areas of future development.     

基于径向基函数神经网络模型的砂土液化概率判别方法

以国内外25次大地震中的344组场地液化实测资料为基础,通过径向基函数神经网络模型的训练和检验,分析了修正标准贯入击数N1与饱和砂土抗液化强度之间的非线性关系,建立了饱和砂土液化极限状态曲线或抗液化强度临界曲线经验公式。经统计分析,给出了液化和非液化的概率密度函数以及抗液化安全系数与液化概率之间的经验公式,最后导出了具有概率意义的饱和砂土抗液化强度经验公式。当液化概率水平为50%时,即等价于传统的确定性砂土液化判别,该方法预测液化和非液化的可靠性分别为90.4%和81.2%,具有较高的可靠性。本文提出的砂土液化概率判别方法,使工程场地的砂土液化概率判别如同确定性砂土液化判别一样简单、方便,从而使砂土液化概率判别方法用于工程实践和纳入有关规范成为可能。     

The use of screening models in determining water supply reliability

Accurate estimation of water supply reliability is often hindered by a paucity of historic streamflow data and the expense of adequate system modelling. A procedure using synthetic streamflow generation and screening models in determining water supply reliability in a cost-effective manner is presented. A series of screening models are developed to evaluate numerous synthetic streamflow data to select from them desired flows for incorporation into an optimization model. Results from the optimization model are used to generate cumulative distribution functions of system reliability. The water supply system serving Seattle, Washington is used as a case study.     

Mix proportions for HPC incorporating multi-cementitious composites using artificial neural networks

High performance concrete (HPC) is defined in terms of both strength and durability performance under anticipated environmental conditions. HPC can be manufactured involving up to 10 different ingredients whilst having to consider durability properties in addition to strength. The number of ingredients and the number of properties of HPC, which needs to be considered in its design, are more than those for ordinary concrete. Therefore, it is difficult to predict the mix proportions and other properties of this type of concrete using statistical empirical relationship. An alternative approach is to use an artificial neural network (ANN). Based on the experimentally obtained results, ANN has been used to establish its applicability to the prediction and optimization of mix proportioning for HPC. It was demonstrated that mix proportioning for HPC can be predicted using ANN. However, some trial mixes are necessary for better performance and elimination of material variability factors from place to place. ANN procedure provides guidelines to select appropriate material proportions for required strength and rheology of concrete mixes and will reduce the number of trial mixes.     

Artificial neural networks and naturally ventilated buildings

The author found that the results obtained were reliable and indicates that neural networks can be used as a predictor for investigating window opening configurations to study the effects on interior air motion. Further study is needed in the development of the database to cover wider architectural parameters and the implementation of new types of network is also needed, as well as the need to consider variation spatial coefficients more fully.     

Application of neural networks for the structural health monitoring in curtain-wall systems

In a curtain-wall system, the main and the most possible cause of failures, is the total or partial destruction of its connections with the bearing structure. The present paper deals with the respective health monitoring problem and proposes an Artificial Neural Network (ANN) in order to identify possible imperfections in a typical curtain-wall system. Several Finite Element (FE) models of the curtain-wall system were developed and a parametric analysis was carried out dealing with the loss of rigidity in the aforementioned connections. During the numerical investigations, datasets containing the deflections of the columns of the curtain-wall structure were computed. The obtained results were used to create the Patterns Database, which, in turn, was used as the input for the training of the ANNs. Due to the relatively small number of training patterns, the regularization technique was also employed in order to improve the network generalization. The number of sensors and their optimal placement for appropriate network training were investigated. A wide variety of network architectures was studied and their influence on the network training was analyzed. The obtained results showed that ANNs can be an efficient method for the identification and localization of imperfections in curtain-wall systems.     

基于神经网络理论的机场沥青道面状况评定

为了简便、有效地进行机场沥青道面状况的评定,应用神经网络理论建立了道面平整度、摩擦系数与道面状况指数PCI的网络模型。通过对国内12个机场道面平整度、摩擦系数的实测和道面损坏状态的调查,所得数据作为样本对网络进行训练。结果表明,当目标误差为1×10-4时,计算值与实测值间的相对误差均小于1.8%。