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Annie Guerriero, Sylvain Kubicki, Gilles Halin

A model-based approach to develop a dashboard tool integrating trust concepts in AEC

Abstract: In the Architecture Engineering and Construction (AEC) sector, cooperation between actors is essential for project success. During the building construction activity, the organization of actors is both hierarchical, transver-sal and adhocratic. Moreover, the quality of cooperation is fundamentally influenced by the management of interde-pendences between tasks and between actors. In this context, the development of new assistance tools has to integrate these heterogeneous parameters relative to coordination and trust. We inspired about Model-Driven Engineering ap-proach to propose a models infrastructure integrating cooperation context modelling and views modelling. We develop on the basis of this infrastructure a dashboard dedicated to the building site coordinator. This tool currently in design stage provides indicators about the trust in the good progression of activity. Moreover, it would enable context under-standing by combining these indicators in a multi-views interface. Thus, the user could navigate in the context using multiple views like meeting report, planning, performance evaluation, or 3D mock-up, and obtain more information about a particular indicator.

Keywords: building construction, cooperation, coordination, trust, process modelling, dashboard, model-driven en-gineering

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Series: w78:2007 (browse)
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Igal M. Shohet, Bhanu Tak, Massimiliano Luzi and Matty Revivi

On-Site Mobile Application for Enhanced Safety and Quality

Abstract: Two of the most important factors that site managers have to control during construction are safety and quality. We envisage that the emergence of Information and Communications Technology (ICT) systems could be of great use in effectively promoting safety and quality and exploiting the synergy between these two disciplines. The objectives of this research were to investigate the relationship between safety and quality using an on-site mobile application for communication, control and command of construction safety and quality. A system of key safety and quality performance indicators was compiled in order to evaluate the potential benefits. Safety and quality leading indicators were recorded prior and after intervention process using on-site mobile application. The results showed an improvement of 30% in Quality Indicator (Q.I), 20% in Safety Indicator 1 (S.I.1) and 42% in Safety Indicator 2 (S.I.2) and these differences were found to be statistically significant at the level of 0.99.

Keywords: Communication, Control, Mobile Technology, Quality, Safety

DOI: https://doi.org/10.24928/JC3-2017/0037

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Series: jc3:2017 (browse)
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M Batouli, Y Zhu

Using Accrual Accounting Life Cycle Assessment as an Indicator of Urban Sustainability

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Series: w78:2014 (browse)
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Mao Zhi, Goh Bee Hua, Wang Shouqing, Ofori G

Forecasting construction industry-level total factor productivity growth using neural network modeling

Abstract: Total Factor Productivity (TFP) is widely recognised as a better indicator than Labour Productivity and Multi-Factor Productivity to represent industry-level productivity performance. Productivity is the key determinant of a nation's standard of living and an industry's competitiveness. As such, the ability to predict trends in TFP growth in the construction industry is very important. The factors influencing TFP growth in the construction industry are complicatedly interrelated. This fact made the conventional regression method highly inadaptable to such complex multi-attribute nonlinear mappings. As an AI information-processing tool, the artificial neural network (ANN) system has been proven to be a powerful approach to solving complex nonlinear mappings with higher accuracy than regression methods. However, so far, there has been little application of ANNs in predicting TFP growth in the construction field. This study will for the first time, apply the concepts of ANNs to develop a model to forecast the TFP growth in the case of the construction industry of Singapore. Macro-level information processing models are useful in monitoring and predicting the performance of the construction industry as a whole. With the need to manage construction performance information at all three levels, namely, industry, firm and site, this study looks specifically at developing an 'intelligent' model for forecasting industry-level productivity. Meanwhile, using the same set of data, a model developed by the Multiple Linear Regression method will serve as a benchmark to judge the performance of the ANN model. The ANN model, compared with the traditional regression model, would be expected to have better forecasting ability for TFP growth in the construction industry, in terms of accuracy.

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Full text: content.pdf (79,002 bytes) (available to registered users only)

Series: w78:2002 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.analysis (0.037562) class.processing (0.012219) class.legal (0.002318)
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Permission to reproduce these documents have been graciously provided by the Aarhus School of Architecture, Denmark. The assistnace of the editor, Prof. Kristian Agger, is gratefully aprecciated.


Massimiliano Luzi and Igal M. Shohet

Optimal Maintenance of Below-Grade Culverts

Abstract: In Israel, below-grade culverts are critical structure as they are essential points of the water transport network across the country. A lack of maintenance may create safety hazards and economic loss. The current maintenance strategy is that of breakdown maintenance. Such strategy has the drawback of high repair and maintenance costs. This study presents a methodology to determine the optimal maintenance strategy for below-grade culverts during their design life, by evaluating life cycle performances and life cycle costs. A field survey aimed at collecting critical data to develop a suitable deterioration pattern model. An exponential model was found to be the most significant statistically. A Markov chain model was implemented to determine the culvert life cycle performances and life cycle costs. A Maintenance Effectiveness Indicator (MEI) determines the best life cycle maintenance strategy among: breakdown, performance-based, and preventive maintenance. The best maintenance strategy was found to be the preventive. It provides with the highest cost effectiveness and allows for knowing the required budget in advance. Preventive maintenance strategy, which is strongly associated with Lean Construction, permits to save 80% of the resources required under the breakdown maintenance regime. The methodology can be extended to other assets of the roadway infrastructure.

Keywords: Asset Management, Culverts, Markov Chains, Preventive Maintenance

DOI: https://doi.org/10.24928/JC3-2017/0208

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Olga Golovina and Jochen Teizer

BIM4LIFE: GNSS and BIM Data Fusion for Mapping Human-Machine Interaction

Abstract: This paper presents an effective approach towards integrating Real-time Location Sensing (RTLS) and Building Information Modeling (BIM) data for mapping near hit events related to human-machine interaction on construction sites. The study under the concept called BIM for lean and injury free environments (BIM4LIFE) focuses on key managerial and technological issues in planning safe and productive work environments: (a) the reliance of current performance measurement practices on lagging instead of adapting to leading indicator data and (b) the common unstructured nature and dynamic progress of construction work environments making it difficult to collect data that leads to quality information. Both call for reliable information and communication technology (ICT) in infrastructure and information management processes to advance safety in construction. The data employed are trajectories from Global Navigation Satellite System (GNSS) data loggers, while an as-is building information model and a-priori collected true geometric equipment information are the other main data sources. The initial result of the data fusion process is a heat map that precisely analyses pedestrian worker and equipment interactions. The novelty of this work lies in solving the interface issues from RTLS data to BIM and to automated protective safety equipment modelling. The methods were tested in realistic work settings. The paper concludes with a critical review on the reliability of the methods employed as well as an outlook on possible changes to current work practices.

Keywords: BIM4LIFE, Construction Safety, Remote Sensing, Equipment Operator Visibility, Human-Machine Interaction, Building Information Modelling (BIM)

DOI: https://doi.org/10.24928/JC3-2017/0185

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Patrick C. Suermann & Raja R.A. Issa

CASE STUDIES: EVALUATING BUILDING INFORMATION MODELING IMPACT ON UNITED STATES ARMY CORPS OF ENGINEERS CONSTRUCTION

Abstract: Highlighted as test bed Districts for BIM implementation in the U.S. Army Corps of Engineers (USACE) Building Information Modeling (BIM) Road Map, Seattle and Louisville accomplished in-house BIM designs in 2005, three years before any other of the 43 Districts across the United States transitioned to a BIM-centered approach in 2008. However, while perceived benefits of BIM used on these test bed jobs are driving Corps-wide changes on billions of dollars of construction, these BIM-based projects were never critically evaluated for their benefits in the construction phase, creating a need for further investigation. Therefore, this research addressed the need and assessed BIM impact on construction projects according to the U.S. Army Corps of Engineers (USACE) internal metrics, the Consolidated Command Guidance (CCGs) metrics for military construction. Information garnered in the survey phase prior to onsite research at the Districts was used to guide research methodology assessing BIM effects on construction projects in the USACE Seattle and Louisville Districts. This research documented both quantitative and qualitative evidence demonstrated in BIM-based projects in Seattle and Louisville compared to similar projects (by facility use category code) to determine a correlation between BIM-based design and construction.

Keywords: Building Information Modeling (BIM), Key Performance Indicator (KPI), United States Army Corps of Engineers (USACE), construction, metrics

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Series: w78:2008 (browse)
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Tucker S N, Ambrose M D, Johnston D R, Newton P W, Seo S, Jones D G

LCAdesign: an integrated approach to automatic eco-efficiency assessment of commercial buildings

Abstract: Buildings consume resources and energy, contribute to pollution of our air, water and soil, impact the health and well-being of populations and constitute an important part of the built environment in which we live. The ability to assess their design with a view to reducing that impact automatically from their 3D CAD representations enables building design professionals to make informed decisions on the environmental impact of building structures. Contemporary 3D object-oriented CAD files contain a wealth of building information. LCADesign has been designed as a fully integrated approach for automated eco-efficiency assessment of commercial buildings direct from 3D CAD. LCADesign accesses the 3D CAD detail through Industry Foundation Classes (IFCs) - the international standard file format for defining architectural and constructional CAD graphic data as 3D real-world objects - to permit construction professionals to interrogate these intelligent drawing objects for analysis of the performance of a design. The automated take-off provides quantities of all building components whose specific production processes, logistics and raw material inputs, where necessary, are identified to calculate a complete list of quantities for all products such as concrete, steel, timber, plastic etc and combines this information with the life cycle inventory database, to estimate key internationally recognised environmental indicators such as CML, EPS and Eco-indicator 99. This paper outlines the key modules of LCADesign and their role in delivering an automated eco-efficiency assessment for commercial buildings.

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Full text: content.pdf (647,472 bytes) (available to registered users only)

Series: w78:2003 (browse)
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Permission to reproduce these papers has been graciously provided by the University of Auckland. The assistance of the editor who provided the full texts and the structured metadata, Dr. Robert Amor, is gratefully appreciated.


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