Zainab Riaz

BIM and sensor-based data management system for construction safety monitoring

Purpose This research aims to investigate the integration of real-time monitoring of thermal conditions within confined work environments through wireless sensor network (WSN) technology when integrated with building information modelling (BIM). A prototype system entitled confined space monitoring system (CoSMoS), which provides an opportunity to incorporate sensor data for improved visualization through new add-ins to BIM software, was then developed. Design/methodology/approach An empirical study was undertaken to compare and contrast between the performances (over a time series) of various database models to find a back-end database storage configuration that best suits the needs of CoSMoS. Findings Fusing BIM data with information streams derived from wireless sensors challenges traditional approaches to data management. These challenges encountered in the prototype system are reported upon and include issues such as hardware/software selection and optimization. Consequently, various database models are explored and tested to find a database storage that best suits the specific needs of this BIM-wireless sensor technology integration. Originality value This work represents the first tranche of research that seeks to deliver a fully integrated and advanced digital built environment solution for automating the management of health and safety issues on construction sites.

Data flow analysis of plant and equipment health and safety management

Purpose – Construction plant and equipment accident statistics suggest constant re‐evaluation of health and safety (H&S) systems is beneficial. This paper aims to process analyse plant and equipment H&S management systems on UK construction sites, with a view to applying information and communication technology (ICT) to them as an improvement mechanism.Design/methodology/approach – Five construction project case studies drawn from members of the former Major Contractors Group yield rich H&S process data. These are analysed using data flow diagram (DFD) techniques, to evaluate processes and proffer system improvements incorporating ICT.Findings – Causes of unsafe practice regarding management of construction plant and equipment are found to include: aspects of the plant itself, management processes and operator competence. A new ICT “process paradigm” is suggested, the architecture of which incorporates mobile computing, automatic identification and data collection and a management information system.Resea...

Leveraging BIM and Big Data to deliver well maintained highways

Purpose Effective management of highways requires management of diverse data sets including traffic volume data, roadway, and road edge and road-side data. Like all major infrastructure clients, highways administration authorities are under pressure to use such platforms for better management of data that, in addition to creating other opportunities, allows improved life cycle management of asset data and predictive analytics. This paper aims to review such opportunities and the value that can be generated through integrated life cycle data management by leveraging Big Data and building information modelling (BIM). Design/methodology/approach A literature review is initially performed to systematically gather information to identify and understand BIM as a collaborative platform. Data management applications in other industries are also reviewed. Interviews were conducted and two industry workshops were organised to understand BIM implementation challenges within highways development projects and the role BIM can play in bridging inefficiencies resulting from loss of information at handover phases. The overall understanding lead to drawing up user needs, gathering system requirements and eventually a system architecture design to promote efficient information management throughout the asset lifecycle. Findings It is observed that data from the design and construction phases of projects can be used to inform asset registers from an earlier stage. This information can be used to plan maintenance schedules. Moreover, it can also be integrated with data generated from numerous other sensors to develop a better picture of network operations and support key decision-making. Effective road network management involves collection and analysis of huge data from a variety of sources including sensors, mobiles, assets and Open Data. Recent growth in Big Data analytics and data integration technologies provides new opportunities to optimise operations of highways infrastructure. Research limitations/implications The system architecture designed for this research is translated into a prototype system as a proof of concept. However, it needs to be tested and validated by end users to be transformed into a useful solution for the industry. Originality/value This paper provides an enhanced understanding of new opportunities created to optimise operations of highways infrastructure using the recent growth in Big Data analytics and data integration technologies.

Open Data Landscape: A Global Perspective and a Focus on China

Governments are producing significant public data that, if made open, is expected to create enormous social and commercial value as well as improve the civil governance. Unleashing the true power of open public data requires a much better understanding of its ecosystem than is known currently. This paper surveys the global open data landscape by taking into account the Open Data Barometer (ODB) ranking system and its three sub-indexes - readiness, implementation and impact. These indexes are compared and analyzed on the basis of income levels of the ODB ranked countries. Finally, using air quality open data, data availability in developing countries like China is compared with countries of better practices such as UK and US. The comparison helps in understanding the current situation and barriers in opening data in China.

Challenges and drivers for data mining in the AEC sector

The purpose of this paper is to explore the current challenges and drivers for data mining in the AEC sector.,Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics.,The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals.,With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and Big Data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for data mining in the AEC sector with a practical focus.

Building Information Modeling (BIM) Enabled Facilities Management Using Hadoop Architecture

Protecting the safety of occupants and the environment is a core value within Facilities Management. Data acquisition systems such as wireless sensor networks can help to monitor facilities to reduce hazards such as fire, water flooding and burglaries. Building Information Modeling (BIM) is a revolutionary development that is reshaping the Architecture Engineering and Construction (AEC) industry. BIM is both a process and a technology, offers wide solutions for facilities management. In order to monitor and manage facilities data in a BIM based simulated environment, this work reports upon a development of a system that uses BIM software and a wireless sensor technology to develop a proactive safety management system. However, to link BIM data with data values of wireless sensors motes will challenge the traditional approaches to data management and massive sensor data will contribute to the emerging model of big data. To extract relevant information out from the collected data, a prototype system uses Hadoop architecture for data storage and processing. The results of processing BIM and sensor data in a Hadoop architecture have demonstrated that proposed system can effectively provide data visualizations to facility managers with the most accurate information needed to make decisions. It also ultimately attempts to reduce safety hazards during the facility management phase of a building.