Fuzzy Expert System to Assess Corrosion of Cast/Ductile Iron Pipes from Backfill Properties

Abstract:  Several factors may contribute to the structural failure of cast and ductile iron water mains, the most important of which is considered to be corrosion. The ANSI/AWWA C105/A21.5-99 10-point scoring (10-P) method is commonly used to predict the corrosivity potential of a given soil sample using certain soil properties. The 10-P and other scoring methods use binary logic to classify the soil as either corrosive or noncorrosive . Fuzzy logic extends binary logic in this context as it recognizes the real world phenomena using a certain degree of membership between 0 and 1. This article presents a fuzzy logic expert system capable of predicting the deterioration of cast and ductile iron water mains based on surrounding soil properties. The proposed model consists of two modules: a knowledge base and an inference mechanism. The knowledge base provides information for better decision making and is developed in a two-tier fuzzy modeling process. First in direct approach, the expert knowledge generates a subjective model to describe the characteristics of the system using fuzzy linguistic variables. Later in system identification, the field data are used to develop an objective model, which is eventually used in conjunction with the subjective model to provide a more reliable knowledge base for the expert system. The inference mechanism uses fuzzy approximate reasoning methods to process the encoded information of the knowledge base.     

Towards collaborative creative design

The paper presents a design support system for collaborative work based upon the composite knowledge representation. It addresses the main challenges of distributed environment: ensuring a convenient access to the common data by multiple users and maintaining consistency of such data. The main idea is to couple the design support system implemented in C/C++ with the knowledge database using the ODBC library developed by the Microsoft. The ability of the proposed system is demonstrated on several examples.     

Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related uncertainties

The scouring phenomenon is one of the major problems experienced in hydraulic engineering. In this study, an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches, including the ant colony optimization, genetic algorithm, teaching-learning-based optimization, biogeographical-based optimization, and invasive weed optimization for estimating the long contraction scour depth. The proposed hybrid models are built using non-dimensional information collected from previous studies. The proposed hybrid intelligent models are evaluated using several statistical performance metrics and graphical presentations. Besides, the uncertainty of models, variables, and data are inspected. Based on the achieved modeling results, adaptive neuro-fuzzy inference system–biogeographic based optimization (ANFIS-BBO) provides superior prediction accuracy compared to others, with a maximum correlation coefficient (R_test = 0.923) and minimum root mean square error value (RMSE_test = 0.0193). Thus, the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring, thus, contributing to the base knowledge of hydraulic structure sustainability.     

A VaFALCON neuro-fuzzy system for mining of incomplete construction databases

This paper tackles problems encountered in mining of incomplete data for knowledge discovery of construction databases. As historical construction data are expensive and time-consuming to collect, any waste of incomplete data means not only loss of knowledge but also increase of costs for knowledge discovery of construction engineering. Unfortunately, incompleteness is omnipresent in the existing construction databases. This paper proposes a VaFALCON (Variable-Attribute Fuzzy Adaptive Logic Control Network) neuro-fuzzy system that is based on the architecture of the original FALCON and equipped with capabilities for mining incomplete data. Three real world examples are selected to test the proposed VaFALCON. The testing results show that the proposed VaFALCON is able to improve the system accuracy up to 84.5% and recover accuracy at least 81% even under the severe data incompleteness case, where all datasets of the database are incomplete.     

基于BP-LSTM混合模型的钢-混组合桥面系空间温度场及温度效应实时评估及预测

考虑到传统健康监测系统与有限元方法的不足,为实现钢-混组合桥面系温度及温度效应的实时评估及预测,提出基于多层前馈——长短期记忆(BP-LSTM)混合模型的实时评估及预测方法。研究过程中,以某桥为工程背景,采用其健康监测系统数据,并利用有限元法热力耦合分析扩充数据,构建包含“结构特征、时间特征、环境特征——温度、温度效应”映射的样本库,并通过实测数据验证有限元计算的准确性;设计BP-LSTM混合模型结构,对LSTM网络以考虑时间权重的均方误差(MSE)损失函数加以改进,读入样本库数据进行训练、验证、测试,从而获得输出精度较高的混合模型;最后,分别给出利用BP-LSTM混合模型进行温度及温度效应实时评估与预测的方法。结果表明:基于气象数据的结构温度场有限元计算结果与实测基本一致,“结构特征、时间特征、环境特征——温度、温度效应”映射具有高度非线性,单独BP网络或LSTM网络效果不佳,而BP-LSTM混合模型精度较高;根据本文提出的实时评估及预测方法,混合模型输出值与目标值的决定系数最低为0.930,最高为0.993,精度较高,可用于钢-混组合桥面系空间温度场及温度效应实时评估及预测。     

A multi‐time‐delay compensation controller using a Takagi–Sugeno fuzzy neural network method for high‐rise buildings with an active mass damper/driver system

An active mass damper/driver (AMD) control system with a single mass has such problems as the excessive weight of the auxiliary mass and the insufficient capacity of its driving equipment. It is necessary to work through multiple subsystems to achieve effective control of high‐rise buildings. However, the time‐delay effect in each subsystem impedes its application in engineering practices. In the paper, an augmented system based on a zero‐order hold is proposed for discrete‐time systems with multiple time delays, and then the system is designed according to the compensation strategy using a classical linear quadratic regulator algorithm. After that, the sample data obtained from the zero‐order hold compensation controller is trained through a Takagi–Sugeno fuzzy neural network method. Finally, a new simplified compensation controller is designed to further shorten the time consuming calculation on the premise of guaranteeing its control effects and parameters. To verify its effectiveness, an AMD system in a high‐rise building is regarded as an example, and the proposed methodology is also applied to an experiment of a four‐story frame. Both results demonstrate that the method can enhance the performance of an AMD system with multiple time delays.     

Acquisition, Representation, and Validation of Subjective Knowledge

Abstract: This paper concentrates on the three main stages of expert system development: knowledge acquisition, knowledge representation (which includes the treatment of uncertainties), and verification and validation. The findings of a survey of expert system applications in the construction management domain are reported. A model is presented that regulates the process of creating an expert system and that eliminates the shortcomings of existing techniques used with knowledge of subjective nature. This model is based on the Delphi method, a tool used for collecting data and information to solve nonanalytical problems. It is proposed that the use of the Delphi method in the knowledge acquisition, knowledge representation, and validation stages of expert system development will improve user confidence in the final product.     

Contractors' perception of the use of costs of quality system in Malaysian building construction projects

While conforming on the findings of prior researches regarding quality cost system in building companies, current research successfully illustrates the contractors' perceptions on the importance of the quality cost system and the barriers that may constrain the implementation of the system for recording and collecting quality cost data. A postal and email surveys were undertaken on Malaysian building companies, focusing on the benefits and difficulties associated with the implementation of quality cost system. Statistical analyses based on Chi-Squared test and Relative Importance Index techniques were used to investigate the significance of the findings and determine the relative importance of the factors. The most important benefit of measuring quality costs is “getting management attention and increase quality awareness” as perceived by the sample of the study. The possible barriers that may affect the management's decision to implement quality cost system are identified and grouped into three categories, which are culture and knowledge; system; and company. The study suggests that the level of the site staff's knowledge should be as important as that of the management to successfully collect and record quality costs data. The findings of this research will raise the level of awareness and sensitize managers and those involved with building industry about the importance of quality cost system and collecting quality costs data.     

基于行动学习的建筑企业知识管理研究

应用系统观点分析知识管理的概念,基于社会系统中软系统方法的行动学习观点,初步建立了建筑企业知识管理的行动学习框架,为建筑企业实施知识管理提供了一个具体的行动指南。     

基于数据挖掘的地下硐室围岩稳定性判别

 地下硐室围岩的稳定性评价是个极其复杂、知识匮乏的问题。首次将数据挖掘方法应用到地下硐室围岩稳定性判别知识的自学习中。鉴于现有数据挖掘方法未能考虑负属性的挖掘, 提出了可以考虑负属性的一种新的数据挖掘方法, 它从硐室围岩稳定性的实例数据中挖掘出知识, 并将得到的知识输入专家系统, 进行不确定性推理, 对地下硐室围岩的稳定性进行合理的判别。     

Graph‐Based Modeling of Information Flow Evolution and Propagation under V2V Communications‐Based Advanced Traveler Information Systems

A vehicle equipped with a vehicle‐to‐vehicle (V2V) communications capability can continuously update its knowledge on traffic conditions using its own experience and anonymously obtained travel experience data from other such equipped vehicles without any central coordination. In such a V2V communications‐based advanced traveler information system (ATIS), the dynamics of traffic flow and intervehicle communication lead to the time‐dependent vehicle knowledge on the traffic network conditions. In this context, this study proposes a graph‐based multilayer network framework to model the V2V‐based ATIS as a complex system which is composed of three coupled network layers: a physical traffic flow network, and virtual intervehicle communication and information flow networks. To determine the occurrence of V2V communication, the intervehicle communication layer is first constructed using the time‐dependent locations of vehicles in the traffic flow layer and intervehicle communication‐related constraints. Then an information flow network is constructed based on events in the traffic and intervehicle communication networks. The graph structure of this information flow network enables the efficient tracking of the time‐dependent vehicle knowledge of the traffic network conditions using a simple graph‐based reverse search algorithm and the storage of the information flow network as a single graph database. Further, the proposed framework provides a retrospective modeling capability to articulate explicitly how information flow evolves and propagates. These capabilities are critical to develop strategies for the rapid flow of useful information and traffic routing to enhance network performance. It also serves as a basic building block for the design of V2V‐based route guidance strategies to manage traffic conditions in congested networks. Synthetic experiments are used to compare the graph‐based approach to a simulation‐based approach, and illustrate both memory usage and computational time efficiencies.     

Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports

In the United States like in many other countries throughout the globe, construction workers are more likely to be injured on the job than workers in any other industry. This poor safety performance is responsible for huge human and financial losses and has motivated extensive research. Unfortunately, safety improvement in construction has decelerated in the last decade and traditional safety programs have reached saturation. Yet major construction companies and federal agencies possess a wealth of empirical knowledge in the form of huge databases of digital construction injury reports. This knowledge could be used to better understand, predict, and prevent the occurrence of construction accidents. Unfortunately, due to the lack of a clear methodology and the high costs of manual large-scale content analysis, these valuable data have yet to be extracted and leveraged. Recently, researchers have proposed a framework allowing meaningful empirical data to be extracted from accident reports. However, the resource limitations inherent to manual content analysis still remain. The present study tested the proposition that manual content analysis of injury reports can be eliminated using natural language processing (NLP). This paper describes (1) the overall strategy and methodology used in developing the system, and specifically how key challenges with decoding unstructured reports were overcome; (2) how the system was built through an iterative process of coding and testing against manual content analysis results from a team of seven independent analysts; and (3) the implications and potential uses of the data extracted. The results indicate that the NLP system is capable of quickly and automatically scanning unstructured injury reports for 101 attributes and outcomes with over 95% accuracy. The main contribution of this research is to empower any organization to quickly obtain a large and highly reliable structured attribute and outcome data set from their databases of unstructured accident reports. Such structured data are a necessary prerequisite to the application of statistical modeling techniques, allowing the extraction of new safety knowledge and finally the amelioration of safety management.     

供水管网状态估计研究进展

系统地总结了供水管网状态估计的研究内容，包括参数估计、校核评估、数据（样本）设计。20年来国内外学者根据优化和统计分析理论并结合现场试验，提出了系统的理论分析和数值方法，近年来管网不确定分析理论被引入到管网校核评估和管网率定的样本数据设计中。此外还介绍了国外供水管网状态估计的精度标准。指出虽然相关理论研究有了较大进展，但应用研究相当薄弱，大型管网模型成功率定的实例还鲜有报道，今后应加强实用研究。     

Measuring traffic lane‐changing by converting video into space–time still images

Empirical data is needed in order to extend our knowledge of traffic behavior. Video recordings are used to enrich typical data from loop detectors. In this context, data extraction from videos becomes a challenging task. Setting automatic video processing systems is costly, complex, and the accuracy achieved is usually not enough to improve traffic flow models. In contrast “visual” data extraction by watching the recordings requires extensive human intervention. A semiautomatic video processing methodology to count lane‐changing in freeways is proposed. The method allows counting lane changes faster than with the visual procedure without falling into the complexities and errors of full automation. The method is based on converting the video into a set of space–time still images, from where to visually count. This methodology has been tested at several freeway locations near Barcelona (Spain) with good results. A user‐friendly implementation of the method is available on http://bit.ly/2yUi08M .     

Evaluation of quarternary aggregate resources in Norway

The paper refers to some results of recent research in Norway on aggregate/concrete interaction. The objective has been to establish criteria for classification, based on knowledge of the geological origin of the aggregates. The evaluation system developed incorporates an automatic data processing information system and a new method of mineralogical quality classification of sand aggregates. The latter refers primarily to the properties of aggregates determining the water requirement and strength of concrete. The system has been used for evaluating a large number of Quaternary aggregate deposits throughout the country, and the results have been correlated with relevant concrete testing.     

A Natural‐Language‐Based Approach to Intelligent Data Retrieval and Representation for Cloud BIM

As the information from diverse disciplines continues to integrate during the whole life cycle of an Architecture, Engineering, and Construction (AEC) project, the BIM (Building Information Model/Modeling) becomes increasingly large. This condition will cause users difficulty in acquiring the information they truly desire on a mobile device with limited space for interaction. The situation will be even worse for personnel without extensive knowledge of Industry Foundation Classes (IFC) or for nonexperts of the BIM software. To improve the value of the big data of BIM, an approach to intelligent data retrieval and representation for cloud BIM applications based on natural language processing was proposed. First, strategies for data storage and query acceleration based on the popular cloud‐based database were explored to handle the large amount of BIM data. Then, the concepts “keyword” and “constraint” were proposed to capture the key objects and their specifications in a natural‐language‐based sentence that expresses the requirements of the user. Keywords and constraints can be mapped to IFC entities or properties through the International Framework for Dictionaries (IFD). The relationship between the user's requirement and the IFC‐based data model was established by path finding in a graph generated from the IFC schema, enabling data retrieval and analysis. Finally, the analyzed and summarized results of BIM data were represented based on the structure of the retrieved data. A prototype application was developed to validate the proposed approach on the data collected during the construction of the terminal of Kunming Airport, the largest single building in China. The case study illustrated the following: (1) relationships between the user requirements and the data users concerned are established, (2) user‐concerned data can be automatically retrieved and aggregated based on the cloud for BIM, and (3) the data are represented in a proper form for a visual view and a comprehensive report. With this approach, users can significantly benefit from requesting for information and the value of BIM will be enhanced.     

试论项目管理

详细介绍了项目概念的四要素 ,针对项目管理的特点 ,提出在项目管理各阶段的基本内容 ,并指出项目管理是一种专业知识和方法体系 ,它有相对统一的内容、要求和技术     

基于BIM的多尺度建筑消防信息检索方法研究

提出一种面向BIM 数据的多尺度建筑消防信息检索方法。首先利用知识图谱技术构建一个全面、可查询的建筑消防语义图谱,提高数据的可理解性;然后通过自然语言处理技术,对查询语句进行分词和语义消歧,实体识别和句法分析,识别查询意图,构建查询的逻辑表达式;最终映射到不同尺度对应的查询模板,实现多尺度信息检索。实验结果表明,所提多尺度方法在多尺度信息检索上可实现0.929 的查准率和0.795 的查全率,在单尺度信息检索（设备检索）上较现有基于关键词的方法提升0.293 准确率和0.240 的查全率,推动了新型消防管理系统检索能力的优化和升级。     

Asynchronous online collaboration in BIM generation using hybrid client-server and P2P network

This paper proposes a system to enable Internet-based modeling collaboration among teams from multiple disciplines for the development of a Building Information Model (BIM). The proposed networked system allows geographically separated design teams to work simultaneously on a single multi-disciplinary BIM, using a distributed system to achieve design integration and conflict resolution. An online collaborative modeling platform for inter-disciplinary collaboration was developed in this study. By using the proposed platform, members of various design teams can work in the same virtual modeling space, monitor the progress of other teams' work, and cooperate in real-time via the Internet. All design data is maintained in a central database providing secure and systematic data management. A hybrid client–server and peer-to-peer (P2P) network was proposed to enable such modeling collaboration. The network has two levels: specialized teams are connected through a client–server network; and the members of each team are connected through a P2P network. A BIM generation workflow with various support functions was proposed based on this hybrid network. A prototype system was developed to demonstrate the feasibility of the proposed collaboration model. Appropriate operating mechanisms are imposed to maintain the integrity of the system. Validation through a case study shows that the proposed collaboration model and system can reduce modeling time and improve modeling efficiency when compared to common modeling workflow.