Condition assessment for bridges: a hierarchical evidential reasoning (HER) framework

Infrastructure risk management practices enable decision-makers to effectively monitor and assess structural condition for repairing/replacing elements before major damage or collapse state is reached. Improved techniques have enhanced inspection and monitoring of infrastructure, but assessment and interpretation of the collected data remains a challenge. In this article, a hierarchical evidential reasoning (HER) framework is proposed for the condition assessment of bridges. The approach involves using a HER framework for classifying bridge data into primary, secondary, tertiary and life safety-critical elements. The proposed HER framework combines different distress indicators (bodies of evidence) at different hierarchical levels. The information is aggregated using Dempster–Shafer (D–S) and Yager rule of combination to propagate both aleatory and epistemic uncertainties throughout the model. Furthermore, importance and reliability factors (collectively termed ‘‘credibility factor’) are introduced for discounting evidence based on importance of bridge element and reliability of the collected data. The data are systematically combined to obtain primary/secondary/tertiary/life safety-critical condition indices. Finally, an overall bridge condition index is obtained. The indices are based on information from multiple sources thereby providing a more reliable assessment of bridge condition. The HER framework is applied to data from an existing bridge in order to demonstrate application of the proposed approach.     

Back-analysis of rock mass strength parameters using AE monitoring data

Most back-analyses in geotechnical engineering are based on methods that utilize field displacement monitoring data. In the present study, a novel method is developed to back-calculate rock mass strength parameters from AE (acoustic emission) monitoring data in combination with FEM stress analysis. The method is based on the important concept of generalized AE initiation threshold of rock masses, established from comprehensive data analysis of laboratory test and underground monitoring programs using AE and microseismic (MS) techniques. An easy-to-use Wizard is developed in Microsoft Excel™ to assist site engineers to perform the back-analysis. The efficient solver in Excel is utilized to reach the optimization solution of an objective function with constraints. The Wizard allows the user to complete the analysis process in an interactive fashion. One example is given to demonstrate the back-analysis process using AE monitoring data recorded from a cavern site. The rock mass strength parameters identified from this approach compare well with field test data, suggesting that the tool can be used effectively to back-calculate rock mass strength parameters from AE monitoring data.     

Estimation and evaluation of uncertainty: a minimalist first pass approach

This paper describes an approach to the estimation and evaluation of uncertainty designed for ease of use. It is designed to be easier to use than probability impact grid based approaches, and it links qualitative approaches in this format to quantitative approaches. It is based on a general view of uncertainty which incorporates ambiguity as well as variability and lack of data. A concern for identifying sources of uncertainty is one characteristic of the approach which flows from this perspective. Other characteristics include deliberate conservative bias to counteract persistent underestimation of uncertainty. It is set in the context of an iterative approach to an overall uncertainty management process. The example used to illustrate the approach involves project duration and cost estimation for bidding purposes.     

Automated performance measurement for 3D building modeling decisions

Building information modeling (BIM) is instrumental in documenting design, enhancing customer experience, and improving product functionality in capital projects. However, high-quality building models do not happen by accident, but rather because of a managed process that involves several participants from different disciplines and backgrounds. Throughout this process, the different priorities of design modelers often result in conflicts that can negatively impact project outcomes. To prevent such unwanted outcomes from occurring, the modeling process needs to be effectively managed. This effective management requires an ability to closely monitor the modeling process and correctly measure the modelers' performance. Nevertheless, existing methods of performance monitoring in building design practices lack an objective measurement system to quantify modeling progress. The widespread utilization of BIM tools presents a unique opportunity to retrieve granular design process data and conduct accurate performance measurements. This research improves upon previous efforts by presenting a novel application programming interface (API)-enabled approach to (a) automatically collect detailed model development data directly from BIM software packages in real-time, and (b) efficiently calculate several modeling performance measures during schematic and design development phases of building projects. These indicators can be used to properly arrange modeling teams in the quest for high-quality building models. The specific objectives of this study to examine the feasibility of a proposed automated design performance measurement framework, and to identify optimal modeling team configurations using empirical performance information. A passive data recording approach allows for the real-time capture of comprehensive user interface (UI) interaction and model element modification events. The proposed framework is implemented as an Autodesk Revit plugin. Next, an experiment is conducted to capture data using the developed Revit plugin. Experiment participants' individual production rates are estimated to establish the validity of the proposed approach to identify the optimal design team configuration. The presented approach uses the earliest due date (EDD) sequencing rule in combination with the critical path method (CPM) to calculate the maximum lateness for different design team arrangements.     

Thermal simulation software outputs: a conceptual data model of information presentation for building design decision-making

Building simulation outputs are inherently complex and numerous. Extracting meaningful information from them requires knowledge which mainly resides only in the hands of experts. Initiatives to address this problem tend either to provide very constrained output data interfaces or leave it to the user to customize data organisation and query. This work proposes a conceptual data model from which meaningful dynamic thermal simulation information for building design decision-making may be constructed and presented to the user. It describes how the model was generated and can become operational, with examples of its applications to practical problems. The paper therefore contains useful information for software developers to help in specifying and designing simulation outputs which better respond to building designers’ needs.     

Design of a construction management data visualization environment: A top-down approach

Explored in this paper is the topic of designing a construction management (CM) data visualization environment with emphasis on its use for supporting the time management function during the planning and execution phases of construction projects which are characterized by sizeable volumes of data of different types. A brief overview of recent construction data visualization work is first provided. Then, as part of a top-down design approach, we introduce concepts and useful terminology related to a structured way of thinking about analytical reasoning and visual analytics, and their relationship with construction management functions. The focus of the latter then shifts to how a construction data visualization environment can support project participant analytical reasoning needs for the management of time, specifically planning/predicting and monitoring/diagnosing/controlling construction conditions and time performance. A case study of aspects of an actual project examined using the construction data visualization environment developed to date is then presented. Purposes served include demonstrating the breadth of support that can be offered for reasoning by such an environment, and providing a test case for demonstrating the kind of evaluation process one should engage in to assess how well an environment conforms to the requirements set out for it. Time management functions treated for this case study include assessing quality of a baseline schedule, assessing actual vs. planned construction conditions and time performance, and assessing reasons for deviations. An evaluation of the current environment is then made to assess conformance/non-conformance with the requirements established for it and to identify worthwhile extensions to it. The paper concludes with a discussion of lessons learned from work performed to date, and their application to create a more comprehensive visualization environment that supports multiple CM functions.     

Temporal trend and source apportionment of water pollution in different functional zones of Qiantang River, China

The increasingly serious river water pollution in developing countries poses great threat to environmental health and human welfare. The assignment of river function to specific uses, known as zoning, is a useful tool to reveal variations of water environmental adaptability to human impact. Therefore, characterizing the temporal trend and identifying responsible pollution sources in different functional zones could greatly improve our knowledge about human impacts on the river water environment. The aim of this study is to obtain a deeper understanding of temporal trends and sources of water pollution in different functional zones with a case study of the Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites in four categories of functional zones during the period 1996-2004. An exploratory approach, which combines smoothing and non-parametric statistical tests, was applied to characterize trends of four significant parameters (permanganate index, ammonia nitrogen, total cadmium and fluoride) accounting for differences among different functional zones identified by discriminant analysis. Aided by GIS, yearly pollution index (PI) for each monitoring site was further mapped to compare the within-group variations in temporal dynamics for different functional zones. Rotated principal component analysis and receptor model (absolute principle component score-multiple linear regression, APCS-MLR) revealed that potential pollution sources and their corresponding contributions varied among the four functional zones. Variations of APCS values for each site of one functional zone as well as their annual average values highlighted the uncertainties associated with cross space-time effects in source apportionment. All these results reinforce the notion that the concept of zoning should be taken seriously in water pollution control. Being applicable to other rivers, the framework of management-oriented source apportionment is thus believed to have potentials to offer new insights into water management and advance the source apportionment framework as an operational basis for national and local governments.     

Methodology for WEEE assessment in Algeria

Waste electrical and electronic equipment (WEEE) generated in emerging countries is increasing. This study presents a methodology to improve assessment and monitoring WEEE in Algeria. The proposed methodology is a two-step approach. The first step is the collection and collation of existing data from different national and international sources. In the second step, different assessment and forecasting methods are applied. Forecasting methods were selected from those models which provided minimum error indices. The paper considers also the availability and reliability of data in order to provide a future reliable assessment of WEEE in Algeria. The study revealed that the forecasting methods do not have a big influence on the results contrary to the inputs of the model.     

Heritage site preservation with combined radiometric and geometric analysis of TLS data

Damage mapping is considered a critical stage in the correct diagnosis of the state of conservation of Cultural Heritage manifestations. The common approach generally implies a large user interaction to map the different pathological processes presented on 2D documents such as elevations or sections, among others. In contrast with this practice, the present paper proposes a semiautomatic 3D-methodology, with a minimum user interaction, able to accurately analyse both radiometric and geometric data recovered by a non-contact technology (Terrestrial Laser Scanning), allowing the extraction and quantification of a wide diversity of pathological processes from biological colonization to deformations. This approach is therefore a potential tool for the rapid and accurate diagnosis of Cultural Heritage. To confirm the applicability and potential offered by the proposed methodology, a highly affected historical masonry, the San Francisco Master Gate of the Almeida Fortress (Portugal), was evaluated by using the Faro Focus 3D 120 Laser Scanner.     

Hydrological Data for Wetland Management

Hydrology is the prime control in wetland ecosystems, but little formal hydrological data are gathered in wetlands by hydrometric authorities. Normally, most of the hydrological data for wetlands can be obtained from informal sources, non-hydrological organizations, the local population and archival information - especially photographs. Some short-term field monitoring can be useful in augmenting the large hydrological data sets which can be gathered - even when initial enquiries yield minimum information. A case study from the North Kent Marshes is used to illustrate this fact.     

Multisensor data fusion for on-site materials tracking in construction

Automated materials tracking and locating on construction sites can significantly impact construction productivity. The ability to automatically detect the locations and multi-handling of thousands of items can improve the performance of material distribution, and ultimately improve project performance. Deploying a cost-effective, scalable, and easy to implement materials location sensing system in real world construction sites has very recently become technically and economically feasible. However, much opportunity still exists to improve accuracy, precision and robustness. In this study a data fusion model is used on an integrated solution for automated identification, location estimation, and dislocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is focused on dislocation detection because it is closely coupled with location estimation, and because it can be used to detect multi-handling of materials. Multi-handling is a key indicator of inefficiency. This study has successfully addressed the challenges of fusing data from different simple sources of information within a very noisy and dynamic environment. The results indicate a potential for the proposed model to improve location estimation and movement detection as well as to automate multi-handling counts.     

Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation

The airflow network (AFN) modeling approach provides an attractive balance between the accuracy and computational demand for naturally ventilated buildings. Its accuracy depends on input parameters such as wind pressure and opening discharge coefficients. In most cases, these parameters are obtained from secondary sources which are solely representative for very simplified buildings, i.e. for buildings without facade details. Although studies comparing wind pressure coefficients or discharge coefficients from different sources exist, the knowledge regarding the effect of input data on AFN is still poor. In this paper, the influence of wind pressure data on the accuracy of a coupled AFN-BES model for a real building with natural wind- and stack-driven ventilation was analyzed. The results of 8 computation cases with different wind pressure data from secondary sources were compared with the measured data. Both the indoor temperatures and the airflow were taken into account. The outcomes indicated that the source of wind pressure data had a significant influence on the model performance.     

燃气信息化安全服务管理系统的应用

阐述了组成燃气信息化安全服务管理系统的用户资料管理子系统、微机监控及数据采集调度系统、管网管理系统、客户管理系统、综合信息决策管理系统的功能,分析了信息化管理的优势。     

Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets

Three classification techniques (loading and score projections based on principal components analysis (PCA), cluster analysis (CA) and self-organizing maps (SOM)) were applied to a large environmental data set of chemical indicators of river water quality. The study was carried out by using long-term water quality monitoring data. The advantages of SOM algorithm and its classification and visualization ability for large environmental data sets are stressed. The results obtained allowed detecting natural clusters of monitoring locations with similar water quality type and identifying important discriminant variables responsible for the clustering. SOM clustering allows simultaneous observation of both spatial and temporal changes in water quality. The chemometric approach revealed different patterns of monitoring sites conditionally named "tributary", "urban", "rural" or "background". This objective separation could lead to an optimization of river monitoring nets and to a better tracing natural and anthropogenic changes along the river stream.     

Spot‐Monitoring zur Ermittlung der thermischen Behaglichkeit in Bürogebäuden unter Berücksichtigung der Lüftungs‐ und Klimatisierungsvarianten

Spot monitoring for determining thermal comfort in office buildings taking into account different ventilation and air‐conditioning options. Compliance with thermal comfort criteria for workspaces is becoming an increasingly important issue in the context of efforts to minimise energy consumption in new and existing office buildings. Current standards fail to take into account user adaptation, although empirical data suggest that this is a key factor for assessing thermal comfort, particularly in non‐air‐conditioned rooms. Spot monitoring is as short‐term measurement technique for analysing thermal comfort. The technique for determining the neutral temperature was evaluated based on field tests in 30 office buildings with different levels of air‐conditioning. The approach consists of short‐term measurements during different seasons and in office spaces facing in different directions. In parallel with logging of the key parameters for determining the quality of the room climate (operative temperature, air velocity, etc.) user surveys were carried out with the aim of obtaining subjective assessments of the state of the room climate. The neutral temperature determined taking into account user sensation is proportional to the change in operational temperature and therefore not constant. The results indicate a significant effect of user adaptation on the assessment of thermal comfort in offices and other spaces.     

Deep learning for data anomaly detection and data compression of a long‐span suspension bridge

As intelligent sensing and sensor network systems have made progress and low‐cost online structural health monitoring has become possible and widely implemented, large quantities of highly heterogeneous data can be acquired during the monitoring. This has resulted in exceeding the capacity of traditional data analytics techniques, especially in monitoring large‐scale or critical civil structures. In particular, data storage has become a big challenge, hence, resulting in the emergence of data compression and reconstruction as a new area in structural health monitoring (SHM) of large infrastructure systems. SHM data generally include anomalies that can disturb structural analysis and assessment. The fundamental reasons for the abnormality of data are extremely complex. Therefore, reconstruction of the abnormal data is generally difficult and poses serious challenges to achieve high‐accuracy after data has been compressed. Considering these significant challenges, in this paper, a novel deep‐learning‐enabled data compression and reconstruction framework is proposed that can be divided into two phases: (a) a one‐dimensional Convolutional Neural Network (CNN) that extracts features directly from the input signals is designed to detect abnormal data with validated high accuracy; (b) a new SHM data compression and reconstruction method based on Autoencoder structure is further developed, which can recover the data with high‐accuracy under such a low compression ratio. To validate the proposed approach, acceleration data from the SHM system of a long‐span bridge in China are employed. In the abnormal data detection phase, the results show that the proposed method can detect anomaly with high accuracy. Subsequently, smaller reconstruction errors can be achieved even by using only 10% compression ratio for the normal data.     

Analysis of an energy efficient building design through data mining approach

Incorporating energy efficiency and sustainable green design features into new/existing buildings has become a top priority in recent years for building owners, designers, contractors, and facility managers. This paper intends to address why delivery of an energy efficient building is not just the result of applying one or more isolated technologies. Rather, it can best be obtained using an integrated whole building process throughout the entire project development process, which leads building designers to generate a large amount of data during energy simulations. The authors observed that even a simple energy modeling run generated pages of data with many different variables. The volumes of energy modeling data clearly overwhelm traditional data analysis methods such as spreadsheets and ad-hoc queries with so many factors to be considered. An integrated or whole building design process involves studies of the energy-related impacts and interactions of all building components, including the building location, envelope (walls, windows, doors, and roof), heating, ventilation and air conditioning (HVAC) system, lighting, controls, and equipment, which shows why it is so difficult to find the correlation between different systems. The objective of this research is to develop an energy efficient building design process using data mining technology which can help project teams discover important patterns to improve the building design. This paper utilizes the data mining technology to extract interrelationships and patterns of interest from a large dataset. Case study revealed that data mining based energy modeling help project teams discover useful patterns to improve the energy efficiency of building design during the design phase. The method developed during this research could be used to guide designers and engineers through the process of completing an early design energy analysis based on energy simulation models.     

An interactive data management system for river water quality data

Water quality data banks are needed to document the status and trends of water pollution in a country. Examples of such systems are the STORET system in the U.S., the NAQUADAT system in Canada and the EIS system in Scandinavia. All of these systems require trained personnel to help in the formulation of the inquiry and the actual querying of the system.By contrast, what is described in this paper is an on-line, interactive data management and analysis system which allows the user the direct search, update, retrieval and analysis of the data from a computer terminal. The user addresses the system in a high level language closely resembling English and has complete control over building, updating and querying the individual data banks. Almost all statistical operations can be performed on the data starting from histograms, distributions, correlations to regression, discriminant, component and spectral analysis. Commands for producing camera-ready graphs on graphic terminals are available.The system is implemented on The University of Michigan Computer System and can be accessed through local telephone numbers in more than 100 cities in the U.S. and Canada and from the major European capitals via the TELENET system. The operation of the system is illustrated on a small sample data base on the Ohio river provided by the Ohio River Sanitation Commission (ORSANCO).