Information technology support to improved construction processes : inter-disciplinarity in research and practice

Research and innovation in the built environment is increasingly taking on an inter-disciplinary nature. The built environment industry and professional practice have long adopted multi and inter-disciplinary practices. The application of IT in Construction is moving beyond the automation and replication of discrete mono and multi-disciplinary tasks to replicate and model the improved inter-disciplinary processes of modern design and construction practice. A major long-term research project underway at the University of Salford seeks to develop IT modelling capability to support the design of buildings and facilities that are buildable, maintainable, operable, sustainable, accessible, and have properties of acoustic, thermal and business support performance that are of a high standard. Such an IT modelling tool has been the dream of the research community for a long time. Recent advances in technology are beginning to make such a modelling tool feasible.----- Some of the key problems with its further research and development, and with its ultimate implementation, will be the challenges of multiple research and built environment stakeholders sharing a common vision, language and sense of trust. This paper explores these challenges as a set of research issues that underpin the development of appropriate technology to support realisable advances in construction process improvements.

Microwave International Media Art Festival

An international festival that champions a pioneer role in promoting media/digital art in Hong Kong. Apart from organising international video screenings in which the latest media art with the most recent trend and development being introduced, a series of artist-in-resident workshops, exhibitions, seminars and symposiums were also hosted with a view to enhancing culture exchange and stimulating media art creation among overseas and local artists

Evaluation of polycaprolactone scaffold degradation for 6 months in vitro and in vivo

The use of polycaprolactone (PCL) as a biomaterial, especially in the fields of drug delivery and tissue engineering, has enjoyed significant growth. Understanding how such a device or scaffold eventually degrades in vivo is paramount as the defect site regenerates and remodels. Degradation studies of three-dimensional PCL and PCL-based composite scaffolds were conducted in vitro (in phosphate buffered saline) and in vivo (rabbit model). Results up to 6 months are reported. All samples recorded virtually no molecular weight changes after 6 months, with a maximum mass loss of only about 7% from the PCL-composite scaffolds degraded in vivo, and a minimum of 1% from PCL scaffolds. Overall, crystallinity increased slightly because of the effects of polymer recrystallization. This was also a contributory factor for the observed stiffness increment in some of the samples, while only the PCL-composite scaffold registered a decrease. Histological examination of the in vivo samples revealed good biocompatibility, with no adverse host tissue reactions up to 6 months. Preliminary results of medical-grade PCL scaffolds, which were implanted for 2 years in a critical-sized rabbit calvarial defect site, are also reported here and support our scaffold design goal for gradual and late molecular weight decreases combined with excellent long-term biocompatibility and bone regeneration. (C) 2008 Wiley Periodicals, Inc. J Biomed Mater Res 90A: 906-919, 2009

Biological performance of a polycaprolactone-based scaffold used as fusion cage device in a large animal model of spinal reconstructive surgery

A bioactive and bioresorbable scaffold fabricated from medical grade poly (epsilon-caprolactone) and incorporating 20% beta-tricalcium phosphate (mPCL–TCP) was recently developed for bone regeneration at load bearing sites. In the present study, we aimed to evaluate bone ingrowth into mPCL–TCP in a large animal model of lumbar interbody fusion. Six pigs underwent a 2-level (L3/4; L5/6) anterior lumbar interbody fusion (ALIF) implanted with mPCL–TCP þ 0.6 mg rhBMP-2 as treatment group while four other pigs implanted with autogenous bone graft served as control. Computed tomographic scanning and histology revealed complete defect bridging in all (100%) specimen from the treatment group as early as 3 months. Histological evidence of continuing bone remodeling and maturation was observed at 6 months. In the control group, only partial bridging was observed at 3 months and only 50% of segments in this group showed complete defect bridging at 6 months. Furthermore, 25% of segments in the control group showed evidence of graft fracture, resorption and pseudoarthrosis. In contrast, no evidence of graft fractures, pseudoarthrosis or foreign body reaction was observed in the treatment group. These results reveal that mPCL–TCP scaffolds could act as bone graft substitutes by providing a suitable environment for bone regeneration in a dynamic load bearing setting such as in a porcine model of interbody spine fusion.

The stimulation of healing within a rat calvarial defect by mPCL–TCP/collagen scaffolds loaded with rhBMP-2

Bone morphogenetic proteins (BMPs) have been widely investigated for their clinical use in bone repair and it is known that a suitable carrier matrix to deliver them is essential for optimal bone regeneration within a specific defect site. Fused deposited modeling (FDM) allows for the fabrication of medical grade poly 3-caprolactone/tricalcium phosphate (mPCL–TCP) scaffolds with high reproducibility and tailor designed dimensions. Here we loaded FDM fabricated mPCL–TCP/collagen scaffolds with 5 mg recombinant human (rh)BMP-2 and evaluated bone healing within a rat calvarial critical-sized defect. Using a comprehensive approach, this study assessed the newly regenerated bone employing microcomputed tomography (mCT), histology/histomorphometry, and mechanical assessments. By 15 weeks, mPCL–TCP/collagen/rhBMP-2 defects exhibited complete healing of the calvarium whereas the non- BMP-2-loaded scaffolds showed significant less bone ingrowth, as confirmed by mCT. Histomorphometry revealed significantly increased bone healing amongst the rhBMP-2 groups compared to non-treated scaffolds at 4 and 15 weeks, although the % BV/TV did not indicate complete mineralisation of the entire defect site. Hence, our study confirms that it is important to combine microCt and histomorphometry to be able to study bone regeneration comprehensively in 3D. A significant up-regulation of the osteogenic proteins, type I collagen and osteocalcin, was evident at both time points in rhBMP-2 groups. Although mineral apposition rates at 15 weeks were statistically equivalent amongst treatment groups, microcompression and push-out strengths indicated superior bone quality at 15 weeks for defects treated with mPCL–TCP/collagen/rhBMP-2. Consistently over all modalities, the progression of healing was from empty defect < mPCL–TCP/collagen < mPCL–TCP/collagen/rhBMP-2, providing substantiating data to support the hypothesis that the release of rhBMP-2 from FDM-created mPCL–TCP/collagen scaffolds is a clinically relevant approach to repair and regenerate critically-sized craniofacial bone defects. Crown Copyright 2008 Published by Elsevier Ltd. All rights reserved.

A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.