Complexity for megaprojects in the energy sector

Megaprojects are characterised by their large-scale capital costs, long duration and extraordinary levels of technical and process complexity. Empirical data demonstrate that these projects experience alarming rates of failure. One of the main causes of such project failure is the high level of complexity and the absence of effective tools to assess and manage it. This study develops a new project complexity assessment method, which is specifically aimed at megaprojects in the energy sector. The assessment method contains a taxonomy of 51 complexity indicators and their consolidated weights, which are established through a novel Delphi-AHP Group Decision Making method. Numerical Scoring criteria for all indicators are defined on the basis of synthesis of existing knowledge of megaprojects to facilitate the application of the new method. It is reviewed and evaluated by experts and tested through a case study energy megaproject

Training of YOLO Neural Network for the Detection of Fire Emergency Assets

Building assets surveys are cost and time demanding and the majority of current methods still rely on manual procedures. New technologies could be used to support this task. The exploitation of Artificial Intelligence (AI) for the automatic interpretation of data is spreading throughout various application fields. However, a challenge with AI is the very large number of training images required for robustly detect and classify each object class. This paper details the procedure and parameters used for the training of a custom YOLO neural network for the recognition of fire emergency assets. The minimum number of pictures for obtaining good recognition performances and the image augmentation process have been investigated. In the end, it was found that fire extinguishers and emergency signs are reasonably detected and their position inside the pictures accurately evaluated. The use case proposed in this paper for the use of custom YOLO is the retrieval of as-is information for existing buildings. The trained neural networks are part of a system that makes use of Augmented Reality devices for capturing pictures and for visualizing the results directly on site

An Integrated Scan-to-BIM Approach for Buildings Energy Performance Evaluation and Retrofitting

Energy retrofitting is paramount to reduce the use of energy in existing buildings, with benefits to the environment and people’s economy. The increasing use of novel technologies and innovative methodologies, such as Terrestrial Laser Scanning (TLS) and Building Information Modelling (BIM), is contributing to optimise retrofit processes. In the context of energy efficiency retrofitting, complex semantic 3D BIM models are required that include specific information, such as second level space boundaries (2LSBs), material energy performance properties, and information of the Heating Ventilation and Air Conditioning (HVAC) system and their layout. All this information is necessary for energy analysis of the existing building and planning of effective retrofitting strategies. In this paper, we present an integrated (semi-)automated Scan-to-BIM approach to produce BIM models from point clouds and photographs of buildings by means of computer-vision and artificial intelligence techniques, as well as a Graphical User Interface (GUI) that enables the user to complete the models with information that cannot be retrieved by means of visual features. Information about the materials and their performance properties as well as the specification of the HVAC component is obtained from a database that integrates information from BAUBOOK, OKOBAUDAT and ASHRAE. The Scan-to-BIM tool introduced in this paper is evaluated with data from an inhabited two-storey building, delivering promising results in energy simulations

The importance of a correct alignment in contactless inspection of Additive Manufactured parts

Nowadays products having complex freeform custom-made shapes can be fabricated without any tool by means of additive manufacturing processes. Additive manufactured parts must be inspected for quality to verify that they meet dimensional and geometrical specifications among other requirements just as any other product. Contactless inspection carried out with optical 3D scanners is preferred to traditional pointwise measurements because of the higher amount of data retrieved in short times. A key step of the contactless inspection process is the definition of the part reference frame for the alignment of scan data. This paper considers different 3-2-1 alignments and analyze their influence on the inspection results, putting in evidence that an inattentive or inaccurate definition of the part reference frame can lead to incorrect evaluations of real part deviation

Engineering Support Systems for Industrial Machines and Plants

In the business of industrial machines and plants, rapid and detailed estimates for planning installation, replacement of equipment, or maintenance work are key requirements for meeting the demands for greater reliability, lower costs and for maintaining safe and secure operation. These demands have been addressed by developing technology driven by IT. When replacing equipment at complex building or plants with high equipment density, the existing state of the installation locations and transportation routes for old and new equipment need to be properly measured. We have met this need by developing parts recognition technology based on 3D measurement, and by developing high-speed calculation technology of optimal routes for installation parts. This chapter provides an overview of these development projects with some real business application results