Early Implementation of Building Information Modeling into a Cold-Formed Steel Company: Providing Novel Project Management Techniques and Solutions to Industry

The ability of building information modeling (BIM) to positively impact projects in the AEC through greater collaboration and integration is widely acknowledged. This paper aims to examine the development of BIM and how it can contribute to the cold-formed steel (CFS) building industry. This is achieved through the adoption of a qualitative methodology encompassing a literature review, exploratory interviews with industry experts, culminating in the development of e-learning material for the sector. In doing so, the research team have collaborated with one of the United Kingdom’s largest cold-formed steel designer/fabricators. By demonstrating the capabilities of BIM software and providing technical and informative videos in its creation, this project has found two key outcomes. Firstly, to provide invaluable assistance in the transition from traditional processes to a fully collaborative 3D BIM as required by the UK Government under the “Government Construction Strategy” by 2016 in all public sector projects. Secondly, to demonstrate BIM’s potential not only within CFS companies, but also within the AEC sector as a whole. As the flexibility, adaptability and interoperability of BIM software is alluded to, the results indicate that the introduction and development of BIM and the underlying ethos suggests that it is a key tool in the development of the industry as a whole.

Building information modeling based building design optimization for sustainability

Environmental problems, especially climate change, have become a serious global issue waiting for people to solve. In the construction industry, the concept of sustainable building is developing to reduce greenhouse gas emissions. In this study, a building information modeling (BIM) based building design optimization method is proposed to facilitate designers to optimize their designs and improve buildings’ sustainability. A revised particle swarm optimization (PSO) algorithm is applied to search for the trade-off between life cycle costs (LCC) and life cycle carbon emissions (LCCE) of building designs. In order tovalidate the effectiveness and efficiency of this method, a case study of an office building is conducted in Hong Kong. The result of the case study shows that this method can enlarge the searching space for optimal design solutions and shorten the processing time for optimal design results, which is really helpful for designers to deliver an economic and environmental friendly design scheme.

Building Information Modelling (BIM) Software Interoperability: A Review of the Construction Sector

Building Information Modelling (BIM) is continuing to evolve and develop as the construction industry progresses towards level 2 maturity. However, one of the core barriers in this progression is the aspect of interoperability between software packages. This research and paper stems from a Knowledge Transfer Partnership (KTP) where both industry and academia come together to address this shortcoming within the sector. One of the core objectives of this partnership and the aim of this study is investigating potential solutions to this barrier, while also developing best working practices to be applied in industry. Using one of the case studies from this partnership (a temporary steel structure), this paper demonstrates a potential solution to addressing interoperability within structural analysis and detailing packages, MasterSeries and Revit respectively. The findings of the research indicate that a process based approach rather than that of additional software coding as being the preferred solution. The results of this preliminary research will aid in the development of the topic of interoperability within the sector, while also developing the knowledge and competencies of the parties within the KTP. The findings are explored further, by providing an overview of the resolution process adopted in this case study, in overcoming the interoperability that arose as the project progressed. It is envisaged that this study will assist the construction sector and its adoption of BIM technologies, while also addressing the critical aspect of operability between software.

Building Information Modelling Energy Performance Assessment on Domestic Dwellings: A Comparative Study

Building Information Modelling (BIM) is growing in pace, not only in design and construction stages, but also in the analysis of facilities throughout their life cycle. With this continued growth and utilisation of BIM processes, comes the possibility to adopt such procedures, to accurately measure the energy efficiency of buildings, to accurately estimate their energy usage. To this end, the aim of this research is to investigate if the introduction of BIM Energy Performance Assessment in the form of software analysis, provides accurate results, when compared with actual energy consumption recorded. Through selective sampling, three domestic case studies are scrutinised, with baseline figures taken from existing energy providers, the results scrutinised and compared with calculations provided from two separate BIM energy analysis software packages. Of the numerous software packages available, criterion sampling is used to select two of the most prominent platforms available on the market today. The two packages selected for scrutiny are Integrated Environmental Solutions - Virtual Environment (IES-VE) and Green Building Studio (GBS). The results indicate that IES-VE estimated the energy use in region of ±8% in two out of three case studies while GBS estimated usage approximately ±5%. The findings indicate that the introduction of BIM energy performance assessment, using proprietary software analysis, is a viable alternative to manual calculations of building energy use, mainly due to the accuracy and speed of assessing, even the most complex models. Given the surge in accurate and detailed BIM models and the importance placed on the continued monitoring and control of buildings energy use within today’s environmentally conscious society, this provides an alternative means by which to accurately assess a buildings energy usage, in a quick and cost effective manner.

Nonisothermal moisture transport in hygroscopic building materials: modeling for the determination of moisture transport coefficients

A mathematical model for calculating the nonisothermal moisture transfer in building materials is presented in the article. The coupled heat and moisture transfer problem was modeled. Vapor content and temperature were chosen as principal driving potentials. The coupled equations were solved by an analytical method, which consists of applying the Laplace transform technique and the Transfer Function Method. A new experimental methodology for determining the temperature gradient coefficient for building materials was also proposed. Both the moisture diffusion coefficient and the temperature gradient coefficient for building material were experimentally evaluated. Using the measured moisture transport coefficients, the temperature and vapor content distribution inside building materials were predicted by the new model. The results were compared with experimental data. A good agreement was obtained.

Automatic extraction of Reduced order models from CFD simulations for building energy modeling

Thermal comfort is defined as “that condition of mind which expresses satisfaction with the thermal environment’ [1] [2]. Field studies have been completed in order to establish the governing conditions for thermal comfort [3]. These studies showed that the internal climate of a room was the strongest factor in establishing thermal comfort. Direct manipulation of the internal climate is necessary to retain an acceptable level of thermal comfort. In order for Building Energy Management Systems (BEMS) strategies to be efficiently utilised it is necessary to have the ability to predict the effect that activating a heating/cooling source (radiators, windows and doors) will have on the room. The numerical modelling of the domain can be challenging due to necessity to capture temperature stratification and/or different heat sources (radiators, computers and human beings). Computational Fluid Dynamic (CFD) models are usually utilised for this function because they provide the level of details required. Although they provide the necessary level of accuracy these models tend to be highly computationally expensive especially when transient behaviour needs to be analysed. Consequently they cannot be integrated in BEMS. This paper presents and describes validation of a CFD-ROM method for real-time simulations of building thermal performance. The CFD-ROM method involves the automatic extraction and solution of reduced order models (ROMs) from validated CFD simulations. The test case used in this work is a room of the Environmental Research Institute (ERI) Building at the University College Cork (UCC). ROMs have shown that they are sufficiently accurate with a total error of less than 1% and successfully retain a satisfactory representation of the phenomena modelled. The number of zones in a ROM defines the size and complexity of that ROM. It has been observed that ROMs with a higher number of zones produce more accurate results. As each ROM has a time to solution of less than 20 seconds they can be integrated into the BEMS of a building which opens the potential to real time physics based building energy modelling.

Towards Automated Data-Driven Model Creation for Cloud Computing Simulation

The increasing complexity and scale of cloud computing environments due to widespread data centre heterogeneity makes measurement-based evaluations highly difficult to achieve. Therefore the use of simulation tools to support decision making in cloud computing environments to cope with this problem is an increasing trend. However the data required in order to model cloud computing environments with an appropriate degree of accuracy is typically large, very difficult to collect without some form of automation, often not available in a suitable format and a time consuming process if done manually. In this research, an automated method for cloud computing topology definition, data collection and model creation activities is presented, within the context of a suite of tools that have been developed and integrated to support these activities.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

Load Balanced Scalable Byzantine Agreement through Quorum Building, with Full Information

We address the problem of designing distributed algorithms for large scale networks that are robust to Byzantine faults. We consider a message passing, full information model: the adversary is malicious, controls a constant fraction of processors, and can view all messages in a round before sending out its own messages for that round. Furthermore, each bad processor may send an unlimited number of messages. The only constraint on the adversary is that it must choose its corrupt processors at the start, without knowledge of the processors’ private random bits.

A good quorum is a set of O(logn) processors, which contains a majority of good processors. In this paper, we give a synchronous algorithm which uses polylogarithmic time and Õ(vn) bits of communication per processor to bring all processors to agreement on a collection of n good quorums, solving Byzantine agreement as well. The collection is balanced in that no processor is in more than O(logn) quorums. This yields the first solution to Byzantine agreement which is both scalable and load-balanced in the full information model.

The technique which involves going from situation where slightly more than 1/2 fraction of processors are good and and agree on a short string with a constant fraction of random bits to a situation where all good processors agree on n good quorums can be done in a fully asynchronous model as well, providing an approach for extending the Byzantine agreement result to this model.

Early Implementation of BIM into a Cold-Formed Steel Design/ Fabricator and an Architectural/Planning Consultancy

There is a broad consensus surrounding the ability of building information modelling (BIM) to positively impact a project by enabling greater collaboration. This paper aims to examine the development of BIM and how it can contribute to the evermore present and growing cold-formed steel (CFS) industry. This is achieved thorough a comprehensive literature review and four exploratory interviews with industry experts. Work has been carried out, for the first time, alongside one of the UK’s largest CFS Designer/Fabricators in conjunction with Northern Ireland’s leading Architectural and Town Planning Consultants in the identification and dissemination of information. The capabilities of BIM have been investigated through modeling of simple CFS structures n consultation with the project partners. By scrutinising the literature and associated interviews, the primary opportunities, as well as barriers, of BIM implementation have been investigated in the context of these companies. It is essential to develop greater understanding of the flexibility, adaptability and interoperability of BIM software as the UK construction industry faces a daunting challenge; fully collaborative 3D BIM as required by the UK Government under the “Government Construction Strategy” by 2016 in all public sector projects. This paper, and the wider study that it stems from, approaches the problem from a new angle, from sections of the construction industry that have not yet fully embedded BIM.

Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

A constraints-based resource discovery model for multi-provider cloud environments

Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user's point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services, such as network configuration or data replication, and operating costs, such as hosting cost and data throughput. Providers' cost models often change and new commodity cost models, such as spot pricing, have been introduced to offer significant savings. In this paper, a software abstraction layer is used to discover infrastructure resources for a particular application, across multiple providers, by using a two-phase constraints-based approach. In the first phase, a set of possible infrastructure resources are identified for a given application. In the second phase, a heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic is most appropriate; for others a performance-based heuristic may be used. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental result shows the proposed model could dynamically select an appropriate set of resouces that match the application's requirements.

High-Speed Fully Homomorphic Encryption Over the Integers

A fully homomorphic encryption (FHE) scheme is envisioned as a key cryptographic tool in building a secure and reliable cloud computing environment, as it allows arbitrary evaluation of a ciphertext without revealing the plaintext. However, existing FHE implementations remain impractical due to very high time and resource costs. To the authors’ knowledge, this paper presents the first hardware implementation of a full encryption primitive for FHE over the integers using FPGA technology. A large-integer multiplier architecture utilising Integer-FFT multiplication is proposed, and a large-integer Barrett modular reduction module is designed incorporating the proposed multiplier. The encryption primitive used in the integer-based FHE scheme is designed employing the proposed multiplier and modular reduction modules. The designs are verified using the Xilinx Virtex-7 FPGA platform. Experimental results show that a speed improvement factor of up to 44 is achievable for the hardware implementation of the FHE encryption scheme when compared to its corresponding software implementation. Moreover, performance analysis shows further speed improvements of the integer-based FHE encryption primitives may still be possible, for example through further optimisations or by targeting an ASIC platform.