Social sensing in the field: Challenges in detecting social interactions in construction sites

Construction industry is a sector that is renowned for the slow uptake of new technologies. This is usually due to the conservative nature of this sector that relies heavily on tried and tested and successful old business practices. However, there is an eagerness in this industry to adopt Building Information Modelling (BIM) technologies to capture and record accurate information about a building project. But vast amounts of information and knowledge about the construction process is typically hidden within informal social interactions that take place in the work environment. In this paper we present a vision where smartphones and tablet devices carried by construction workers are used to capture the interaction and communication between workers in the field. Informal chats about decisions taken in the field, impromptu formation of teams, identification of key persons for certain tasks, and tracking the flow of information across the project community, are some pieces of information that could be captured by employing social sensing in the field. This information can not only be used during the construction to improve the site processes but it can also be exploited by the end user during maintenance of the building. We highlight the challenges that need to be overcome for this mobile and social sensing system to become a reality. © 2012 ACM.

Toward automated generation of parametric BIMs based on hybrid video and laser scanning data

Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing.

Modelling information requirements in complex engineering services

Services based around complex engineering equipment and systems provide substantial challenges in both the long-term management of the equipment and the need for guaranteed delivery of the related service. One of the challenges for an organisation providing these services is the management of the information that is required to design, deliver and subsequently assess the success of the service. To assist in this process this paper develops a model for capturing, organising and assessing information requirements for these Complex Engineering Services in which information required to support key decisions in the life cycle of the service is identified. The model – referred to as The 12-Box Model for Service Information Requirements – is embedded in a three-phase procedure for providing an assessment of information requirements of a service contract which also provides insight into the capabilities of available information systems in supporting the contract. An illustrative example examining service information in an aircraft availability contract is used to demonstrate the use of the 12-Box Model and associated assessment procedure.

Modelling and control of nonlinear systems using Gaussian processes with partial model information

Gaussian processes are gaining increasing popularity among the control community, in particular for the modelling of discrete time state space systems. However, it has not been clear how to incorporate model information, in the form of known state relationships, when using a Gaussian process as a predictive model. An obvious example of known prior information is position and velocity related states. Incorporation of such information would be beneficial both computationally and for faster dynamics learning. This paper introduces a method of achieving this, yielding faster dynamics learning and a reduction in computational effort from O(Dn2) to O((D - F)n2) in the prediction stage for a system with D states, F known state relationships and n observations. The effectiveness of the method is demonstrated through its inclusion in the PILCO learning algorithm with application to the swing-up and balance of a torque-limited pendulum and the balancing of a robotic unicycle in simulation. © 2012 IEEE.

Modelling soot formation in a DISI engine

In this work, the formation of soot in a Direct Injection Spark Ignition (DISI) engine is simulated using the Stochastic Reactor Model (SRM) engine code. Volume change, convective heat transfer, turbulent mixing, direct injection and flame propagation are accounted for. In order to simulate flame propagation, the cylinder is divided into an unburned, entrained and burned zone, with the rate of entrainment being governed by empirical equations but combustion modelled with chemical kinetics. The model contains a detailed chemical mechanism as well as a highly detailed soot formation model, however computation times are relatively short. The soot model provides information on the morphology and chemical composition of soot aggregates along with bulk quantities, including soot mass, number density, volume fraction and surface area. The model is first calibrated by simulating experimental data from a Gasoline Direct Injection (GDI) Spark Ignition (SI) engine. The model is then used to simulate experimental data from the literature, where the numbers, sizes and derived mass particulate emissions from a 1.83 L, 4-cylinder, 4 valve production DISI engine were examined. Experimental results from different injection and spark timings are compared with the model and the qualitative trends in aggregate size distribution and emissions match the exhaust gas measurements well. © 2010 The Combustion Institute. Published by Elsevier Inc. All rights reserved.