Instantaneous pavement condition evaluation using non-destructive neuro-evolutionary approach

In this paper, a hybrid neural network (NN)-genetic algorithm (GA) based non-destructive pavement auscultation method for instantaneous airfield infrastructure condition assessment is discussed. NNs are employed for finite element aided forward prediction of pavement surface deflections resulting from non-destructive test impulse loading and the GAs are used for global optimisation of the pavement structural parameters by matching the NN predicted deflections with the measured pavement response. This hybrid approach takes advantage of the non-linear estimation capability provided by neural networks trained using finite element (FE) solutions in modelling the stress-dependent behaviour of unbound pavement geo-materials while improving the robustness to measurement uncertainty through the application of genetic algorithms. The performance of the developed hybrid pavement auscultation technique is evaluated through extensive field studies conducted at a state-of-the-art full-scale airfield pavement test facility. The results show that this approach is promising for real-time condition evaluation of airfield pavement infrastructure systems.     

基于PNN神经网络的地下水水质评价及应用

概率神经网络是一种训练速度快、网络稳定、应用相当广泛的人工神经网络方法,它通过利用线性学习算法来解决非线性问题,在模式识别的分类问题中得到了广泛的应用。本文在阐述概率神经网络(PNN)原理的基础上,以我国地下水环境质量标准(GB/T14848-93)为训练样本,建立概率神经网络(PNN)模型,并将该网络模型运用于地下水水质评价。通过与灰色聚类法、模糊评判法和指标分类法比较,验证了该模型更为准确、可靠。     

An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary,Canada

This paper explores a hybrid wavelet, bootstrap and neural network (WBNN) modeling approach for daily (1, 3 and 5 day) urban water demand forecasting in situations with limited data availability. This method was tested using 3 years of daily water demand and meteorological data for the city of Calgary, Alberta, Canada. The performance of the WBNN method was compared to that of three other methods: traditional neural networks (NN), wavelet NNs (WNN), and bootstrap-based NN (BNN) models. While the hybrid WBNN and WNN models equally provided 1-day lead-time forecasts of greater accuracy than those obtained with other methods, for longer lead-time (3- or 5-day) forecasts the WBNN model alone outperformed the other models. The confidence bands generated using the WBNN model displayed the uncertainty associated with the forecasts.     

针对倒塌房屋瓦砾分布的模糊神经网络预测

房屋倒塌后的瓦砾分布范围与楼层层高、总层数、房屋重量等因素有关，具有一定的随机性，要精确预测其倒塌后的堆积物分布具有一定困难。而模糊神经网络系统的预测功能可以解决这一问题。本文作者结合模糊推理系统和辐射基函数网络，提出了建立多层前向神经网络模型，来输出倒塌房屋堆积物分布范围及平均高度的模糊预测系统。     

基于神经网络的结构地震反应仿真研究进展

总结了利用神经网络进行结构地震反应仿真的研究进展,针对线性结构地震仿真的网络模型选择、网络结构确定及仿真精度控制等关键问题进行了探讨并提出了建议,指出了非线性结构地震反应仿真研究所面临的主要问题及解决方向。     

A neural network based system for predicting earthmoving production

An artificial neural network based system (NN earth) is developed for construction practitioners as a simple tool for predicting earthmoving operations, which are modelled by back propagation neural networks with four expected parameters and seven affecting factors. These networks are then trained using the data patterns obtained from simulation because there are insufficient data available from industrial sources. The trained network is then incorporated as the computation engine of NN earth. To engender confidence in the results of neural computation, a validation function is implemented in NN earth to allow the user to apply the engine to historic cases prior to applying it to a new project. An equipment database is also implemented in NN earth to provide default information, such as internal cost rate, fuel cost, and operator's cost. User interfaces are developed to facilitate inputting project information and manipulating the system. The major functions and use of NN earth are illustrated in a sample application. In practice, NN earth can assist the user either in selecting a crew to minimize the unit cost of a project or in predicting the performance of a given crew.     

Co-variance matrix adaptation evolution strategy for pavement backcalculation

The falling weight deflectometer (FWD) is the foremost and widely accepted tool for characterizing the deflection basins of pavements in a non-destructive manner. The FWD pavement deflection data are used to determine the in situ mechanical properties (elastic moduli) of the pavement layers through inverse analysis, a process commonly referred to as backcalculation (B/C). Several B/C methodologies have been proposed over the years, each with individual strengths and weaknesses. Hybrid methods (combining two methods or more) are recently proposed for overcoming problems posed by stand-alone methods, while extracting and compounding the benefits that are individually offered. This paper proposes a novel hybrid strategy that integrates co-variance matrix Adaptation (CMA) evolution strategy, Finite element (FE) modeling with neural networks (NN) non-linear mapping for backcalculation of non-linear, stress dependent pavement layer moduli. The resulting strategy, referred as CMANIA (CMA with neural networks for inverse analysis) is applied for asphalt pavement moduli backcalculation and is compared with a conventional B/C approach. Results demonstrate the superiority of this method in terms of higher accuracy, achieving nearer to global solutions, better computational speed, and robustness in predicting the pavement layer moduli over the conventional methods.     

基于神经网络与专家系统的智能评估系统

在智能评估系统中单独使用神经网络或传统专家系统技术都有其局限性,二者必须相互取长补短。将神经网络与专家系统相结合是开发智能评估系统的一条比较实际的途径。采用相对独立法,应用黑箱模块结构形成了一种新的智能评估系统结构框架,并据此原理提供了一种开发更接近人类思维过程的智能评估系统的编程实现方法。     

Safety climate in conditions of construction subcontracting: a multi‐level analysis

A multi‐level safety climate model was tested in the Australian construction industry. Subcontracted workers’ perceptions of the organizational safety response (OSR) and supervisor safety response (SSR) in their own organization and that of the principal contractor were measured using a safety climate survey administered at a large hospital construction project in Melbourne. One hundred and fourteen construction workers completed the survey, representing nine subcontractors engaged at the project. Two requisite conditions for the existence of group‐level safety climates, i.e. (1) within‐group homogeneity; and (2) between‐group variation were satisfied for perceptions of subcontractors’ OSR and SSR. This supports the contention that subcontractors working in a single construction project exhibit a unique group‐level safety climate. Subcontracted workers also discriminated between group‐level safety climates (i.e. the SSR) in their own and in the principal contractor’s organizations. The results suggest some cross‐level influence. Perceptions of the SSR were positively predicted by perceptions of the OSR in both the principal and subcontractor organizations. Perceptions of the OSR of the principal contractor were also a significant predictor of the perceived OSR and SSR in the subcontractor organizations. Perceptions of the subcontractors’ SSR were a significant predictor of the rate of lost‐time and medical treatment incidents reported by the subcontractor. Although perceptions of the principal contractor’s SSR were not directly related to subcontractors’ injury rates, they were a significant predictor of subcontractors’ SSR, revealing an indirect link. The results suggest that supervisory personnel (e.g. foremen and leading hands) play an important role in shaping safety performance in subcontracted workgroups.     

小波神经网络在水库流量预测中的应用

针对河南省某水库监测点实测的1991~2013年每月的平均流量样本进行归一化处理作为训练样本,构建了使用Morlet、Mexican hat以及高斯一阶导数小波基函数小波神经网络的预测模型实现对2014年的月平均流量的预测,并通过均方误差(MSE)和平均绝对误差(MAE)两项指标对每种网络的预测结果进行评价,从而选择较好的小波基函数作为小波神经网络的隐含层传递函数。研究表明,采用Morlet小波作为神经网络的隐含层基函数对该水库的月平均流量的预测效果要好于其他两种神经网络。     

基于LM方法的神经网络用于线性时不变系统的广义状态转移矩阵的识别

用神经网络方法对系统状态转移矩阵进行识别的研究,首先建立结构动力学运动方程的状态转移矩阵的识别与神经网络的权值矩阵识别间的相互等价关系,其次利用神经网络功能强大的并行运算能力和丰富的学习功能以单层线性神经网络来识别线性时不变系统的广义状态转移矩阵以获取结构的状态参数.最后通过识别获得的神经网络仿真系统结构在任意激励下的响应,可用以抗震性能评估或振动控制.     

神经网络故障诊断技术应用在暖通空调系统的可实现性

归纳了神经网络在故障诊断中的运用方式，探讨了故障诊断的神经网络方法和专家系统方法的联系和区别，以及两种方法的转化；最后，给出了神经网络故障诊断技术在暖通空调系统里的应用情况。     

The impacts of industrialization on construction subcontractors: a resource based view

Industrialization of the construction process is increasing around the world due to its potential to improve safety, sustainability, effectiveness, productivity and efficiency. While there has been research into the impacts of various forms of industrialized construction on the construction sector, surprisingly there has been little research into the impacts on subcontractors. The lack of subcontractor’s voice in the industrialization debate is important to address since they operate at the coalface of the industry where the impacts of such changes will have a significant impact. The resource based view of the firm (RBV) is used as a theoretical lens to study these potential impacts through interviews with senior executives and managers of six major subcontracting firms which have worked with off-site bathroom pod technologies in Australia. It is found that the key subcontractor resources affected by this off-site technology are human, financial, intellectual and social and that subcontractors will need to pursue strategies which develop new skills, knowledge, networks and deeper supply chain collaborations if they are to turn the potential risks associated with off-site into potential opportunities to achieve competitive advantage.     

Modeling oblique load carrying capacity of batter pile groups using neural network,random forest regression and M5 model tree

M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length (L), angle of oblique load (α), sand density (ρ), number of batter piles (B), and number of vertical piles (V) as input and oblique load (Q) as output was used. Results suggest improved performance by RF regression for both pile groups. M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also. Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data. NN based approach was found performing equally well with both smooth and rough piles. Sensitivity analysis using all three modelling approaches suggest angle of oblique load (α) and number of batter pile (B) affect the oblique load capacity for both smooth and rough pile groups.     

Potential benefits for collaborating formwork subcontractors based on co-operative game theory

Formwork subcontractors that hire open shop workers, rather than union workers can win more contracts and earn more profits from general contractors because of greater agility and lower costs. A subcontractor may earn even more profit if it collaborates with others in a coalition. Payoff functions for individual subcontractors and a group of subcontractors in a coalition are formulated. Profit can also be reasonably allocated to each subcontractor in a coalition using the Shapley value and nucleolus.     

改进BP神经网络在软土地基沉降量中的应用

利用神经网络强大的非线性映射能力,提出了一种基于BP神经网络模型的软土地基沉降量的预测方法,对不同情况下软土路基沉降量进行合理地预测,实例检验证明,该方法收敛速度快,预测的可靠性高。     

Neural network model for resilient modulus of emulsified asphalt mixtures

This paper explores the potential use of neural networks (NNs) in the field of emulsified asphalt mixtures. A neural network model is developed for predicting, with sufficient approximation, relationship between the factors affecting resilient modulus (inputs: curing time, cement addition level, and residual asphalt content) and the resilient modulus (output) of emulsified asphalt mixture. A backpropagation neural network of three layers is employed. First resilient modulus data are obtained by conducting laboratory resilient modulus tests on emulsified asphalt samples, and then the results are used to train the neural network. The effectiveness of different neural network configurations is investigated. Effect of parameters such as curing time, cement addition level and residual asphalt content that influence the resilient modulus is also explored. Results indicate that NN predicts the resilient modulus with high accuracy. It is also demonstrated that NN is an excellent method that can reduce the time consumed and can be used as an important tool in evaluating the factors affecting resilient modulus of emulsified asphalt mixture at the design stage.     

Relationship quality in construction projects: A subcontractor perspective of principal contractor relationships

One of the defining characteristics of the construction industry is its subcontracting model of project organisation. Surprisingly, despite the criticality of the subcontractor/principal contractor relationship to successful project outcomes, research into this important relationship has been fragmented, disorganised and under-theorised. Mobilising theories of relationship quality to address this gap in knowledge, a survey of three hundred and thirty seven tier-one subcontractors in the Australian construction industry was undertaken to explore what factors influence relationship quality between subcontractors and principal contractors, whether these factors vary across the supply chain and how they affect overall relational satisfaction. The findings give subcontractors a missing voice in this important debate and provide a more nuanced understanding of this critical project relationship from a novel theoretical perspective. They indicate that from a subcontractor perspective, subcontractor/principal contractor relationship quality is determined by six key factors: integrity respect and fairness; prompt payment; willingness to negotiate risk and price; effective communication; concern for worker health, safety and well-being; and opportunities for early involvement in planning and design. Conceptually, when these factors are organised under the three dimensions of relationship quality theory (trust, satisfaction, and commitment), the results show that trust is by far the most important determinant of relationship quality. However, subcontractors cannot be treated as a homogeneous group and the factors that influence relationship quality and the importance of trust vary significantly between large and small subcontractors and by the amount of turnover-dependency in a relationship. Conceptually, the findings are important for refining the concept of relationship quality in a project management context and for helping construction project managers to improve often tenuous subcontractor relationships in an increasingly competitive industry where the quality of these relationships is a critical determinant of project outcomes and competitive advantage.     

Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network

An accurate prediction of earth pressure balance (EPB) shield moving performance is important to ensure the safety tunnel excavation. A hybrid model is developed based on the particle swarm optimization (PSO) and gated recurrent unit (GRU) neural network. PSO is utilized to assign the optimal hyperparameters of GRU neural network. There are mainly four steps: data collection and processing, hybrid model establishment, model performance evaluation and correlation analysis. The developed model provides an alternative to tackle with time-series data of tunnel project. Apart from that, a novel framework about model application is performed to provide guidelines in practice. A tunnel project is utilized to evaluate the performance of proposed hybrid model. Results indicate that geological and construction variables are significant to the model performance. Correlation analysis shows that construction variables (main thrust and foam liquid volume) display the highest correlation with the cutterhead torque (CHT). This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.