Abstracts:In this paper, the importance of the predictive modelling process of broadband services adoption is described. A detailed overview of different analytical models used for prediction, i.e., fitting and forecasting processes of broadband services adoption are presented. Furthermore, a comparison of several analytical models commonly used for prediction of broadband adoption is conducted. In order to more accurately fit to the existing broadband adoption time series data, and to forecast the future broadband services adoption paths, the features of the most accurate common predictive models have been identified for different phases of broadband services adoption. Considering the given results, usage of additional models in the predictive modelling process is analyzed. The objective of these analyses is set to improve the accuracy of the existing predictive modelling process. The accuracy of the predictive modelling process using additional models is tested and compared in different phases of broadband adoption. The model which gives the most accurate results is identified. Finally, in order to enable the usage of this model within a whole broadband service life cycle, as well as to include a greater number of explanatory parameters in predictive modelling process, an enhanced predictive modelling process is proposed.

Abstracts:Occupant-generated work orders are recognized as a good potential data to support Facility Management (FM) activities, however they are unstructured and rarely contain the specific information engineers require to resolve the reported issues. Instead, this often requires multiple trips are often needed to identify the required trade, identify the problem and required parts/tools, and resolve. A key challenge is data quality: free-form (unstructured) text is collected that frequently lacks necessary detail for problem diagnosis. Machine Learning provides new opportunities within the FM domain to improve the quality of information collected through online work order reporting systems by automatically classifying WOs and prompting building occupants with appropriate FM team-developed questions in real time to gather the required specific information in structured form. This paper presents the development, comparison, and application of two sets of supervised machine learning models to perform this classification for WOs generated from occupant complaints. A set of ∼150,000 historical WOs was used for model development and textual classification using with various term and itemset frequency approaches was tested. Classifier prediction accuracies ranged from 46.6% to 81.3% for classification by detailed subcategory; this increased to between 68% (simple term frequency) to 90% (random forest) when the dataset only included the ten most common (accounting for 70% of all WOs) subcategories. Hierarchical classification decreased performance. An FM-BIM integration approach is finally presented using the resultant classifiers to provide facilities management teams with spatio-temporal visualization of the work order categories across a series of buildings to help prioritize and streamline operations and maintenance task assignments.

Abstracts:In many industrial sectors, Non Destructive Testing (NDT) methods are used for the thermomechanical analysis of parts in assemblies of engines or reactors or for the control of metal forming processes. This article suggests an automated multi-view approach for the thermal $3D$ reconstruction required in order to compute $3D$ surface temperature models. This approach is based only on infrared cameras mounted on a Cartesian robot.

Abstracts:The reliability of condition assessment of bridges using 3D imagery data, such as 3D laser scanning point clouds, relies on inspectors’ structural engineering knowledge and skills of 3D data processing. A challenge of 3D-data-based structural condition assessment lies in the difficulties of reliably comparing 3D imagery data sets collected at different times for analyzing spatial changes of the structures and finding anomalous deformations. Spatial changes of structures could occur at multiple levels of details and be of different types: (1) rigid body motions (e.g., translations and rotations) at the structure or structural element levels; (2) deformations (e.g., bending of girders) at the levels of structural elements. Unfortunately, existing 3D imagery data-based change analysis methods only produce deviations between two 3D data sets without distinguishing deviations caused by various changes at multiple levels. Significant rigid body motions of structures and structural elements often cause large deviations that “overwhelm” deviation patterns caused by smaller element-level deformations so that engineers could hardly recognize local deformations. Unreliable deformation analysis of structural elements can lead to incorrect condition assessments.

Abstracts:Increasing sustainability requirements make evaluating different design options for identifying energy-efficient design ever more important. These requirements demand simulation models that are not only accurate but also fast. Machine Learning (ML) enables effective mimicry of Building Performance Simulation (BPS) while generating results much faster than BPS. Component-Based Machine Learning (CBML) enhances the capabilities of the monolithic ML model. Extending monolithic ML approach, the paper presents deep-learning architectures, component development methods and evaluates their suitability for space exploration in building design. Results indicate that deep learning increases the performance of models over simple artificial neural network models. Methods such as transfer learning and Multi-Task Learning make the component development process more efficient. Testing the deep-learning model on 201 new design cases indicates that its cooling energy prediction (R²: 0.983) is similar to BPS, while errors for heating energy predictions (R²: 0.848) are higher than BPS. Higher heating energy prediction error can be resolved by collecting heating data using better design space sampling methods that cover the heating demand distribution effectively. Given that the accuracy of the deep-learning model for heating predictions can be increased, the major advantage of deep-learning models over BPS is their high computation speed. BPS required 1145 s to simulate 201 design cases. Using the deep-learning model, similar results can be obtained in 0.9 s. High computation speed makes deep-learning models suitable for design space exploration.

Abstracts:Over-height vehicle strikes with low bridges and tunnels are an ongoing problem worldwide. While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we perform a full validation of the system using a constraint-based approach to minimize the number of over-height vehicle misclassifications due to windy conditions. The dataset includes a total of 102 over-height vehicles recorded at frame rates of 25 and 30fps. An analysis is performed of wind and vehicle displacements to track over-height features using optical flow paired with SURF feature detectors. Motion captured within the region of interest was treated as a standard two-class binary linear classification problem with 1 indicating over-height vehicle presence and 0 indicating noise. The algorithm performed with 100% recall, 83.3% precision, false positive rate of 0.2% and warning accuracy of 96.6%.

Abstracts:Models of distributed manufacturing systems cannot be consistent without a formal ontology. In this paper, the ontology formulation and maintenance are addressed in the scope of a collaborative modelling environment – in which concurrency, consistency, and model life cycle management should be supported. Thus, an extensible foundational ontology for manufacturing – system modelling is proposed in which the formal definitions of the modelling environment itself enable the definition of the manufacturing system’s elements. The presented approach ensures the consistency of ever-changing models. The ontology is integrated into a modelling framework through the concept of description layers that assist in the management of the model description’s complexity. The feasibility of the approaches is illustrated in an industrial case study that models of a manufacturing system for material processing.

Abstracts:Building Information Modeling (BIM) is rapidly advancing as an efficient new approach to cooperative building design and construction. However, BIM methodology is still mainly developed and applied for new building projects. The strong societal needs to improve the quality and the overall performance of the existing building stock, especially with regard to energy use, are yet insufficiently supported by BIM. In this paper we propose a structured approach towards the creation of a building information model of an existing building and its use for the purpose of retrofitting or renovation, based on the standard IFC specification (ISO 16739). It implies a process we define as BIMification. This process undergoes two major stages: (1) Anamnesis, dedicated to the survey and collection of facts about the building, and (2) Diagnosis, dedicated to the analysis and interpretation of the collected facts to obtain the necessary understanding of the building and its performance and prepare for the retrofitting design. The paper outlines the broader research aim that triggered the development of the suggested approach and presents the overall concept and methodology, the ICT platform under implementation and the current state of the work. Discussed are also the scope of the approach, envisaged perspectives and further development efforts.

Abstracts:In recent years, exponential growth has been detected in research efforts focused on automated construction progress monitoring. Despite various data acquisition methods and approaches, the success is limited. This paper proposes a new method, where changes are constantly perceived and as-built model continuously updated during the construction process, instead of periodical scanning of the whole building under construction. It turned out that low precision 3D scanning devices, which are closely observing active workplaces, are sufficient for correct identification of the built elements. Such scanning devices are small enough to fit onto workers’ protective helmets and on the applied machinery. In this way, workers capture all workplaces inside and outside of the building in real time and record partial point clouds, their locations, and time stamps. The partial point clouds are then registered and merged into a complete 4D as-built point cloud of a building under construction. Identification of as-designed BIM elements within the 4D as-built point cloud then results in the 4D as-built BIM. Finally, the comparison of the 4D as-built BIM and the 4D as-designed BIM enables identification of the differences between both models and thus the deviations from the time schedule. The differences are reported in virtual real-time, which enables more efficient project management.

Abstracts:Natural hazards result in ill-conditioned structures with unfavorable damage. To early recognize damage existence, structures can be screened by damage detection methods after a critical hazard event. These damage detection methods are often developed based on a centralized acquiring and computing system that challenges the feasibility of deployment in a large-scale structure. Decentralized damage detection methods alter a single system to multiple subsystems that allow spatially distributing in a structure and yield comparable performance with the centralized approach. In this study, a decentralized damage detection method based on modal prediction errors via multiple banks of Kalman estimators is proposed. First, a sensor network is comprised of multiple subsystems over a structure of which the subsystems have overlapped sensing nodes. These subsystems are individually identified by an input–output frequency-domain system identification method under ambient vibrations. The identified models are then converted into several banks of Kalman estimators, and the estimators generate the estimation of structural modal responses. The prediction errors are calculated from the differentiation between measured and estimated modal responses, and the accumulated standard deviations of modal prediction errors serve as the damage indices for recognizing the damage occurrence, locations, and levels. A numerical example is introduced to demonstrate the proposed method as well as to evaluate the detection effectiveness. Moreover, the proposed method is also experimentally verified by a scaled twin-tower building using shake table testing. The experimental results indicate that the proposed method is quite effective to inform damage of structures in terms of damage occurrence, locations, and levels.