Shu-Ling Lu

The challenges of creating actionable knowledge: an action research perspective

Academia has a critical role in developing new knowledge which construction industry practitioners need to envision, undertake and sustain successful innovation. The new knowledge produced by academia, however, often does not satisfy the needs of practitioners. This unsatisfactory state of affairs is frequently taken to be the consequence of the cultural, motivational and operational differences between the two communities. Actionable knowledge is presented as a useful concept which can fuse the expectations, contributions and outputs of academia and practitioners. Within this context, action research is argued to be an appropriate methodology to develop successful actionable knowledge. Results from an action research project are given which provide researchers and practitioners greater understanding of the key factors that shape the degree to which action research produces actionable knowledge: change focus, collaboration capabilities and systematic process. The criteria intrinsic to Mode 2 research (Gibbons et al., 1994) are demonstrated to have utility in evidencing actionable knowledge. The implication for policy is that there is a need to develop and use appropriate actionable knowledge frameworks and measures to design funding calls, and to evaluate research proposals and outputs.

Career journeys and turning points of senior female managers in small construction firms

There is under‐representation of senior female managers within small construction firms in the United Kingdom. The position is denying the sector a valuable pool of labour to address acute knowledge and skill shortages. Grounded theory on the career progression of senior female managers in these firms is developed from biographical interviews. First, a turning point model which distinguishes the interplay between human agency and work/home structure is given. Second, four career development phases are identified. The career journeys are characterized by ad hoc decisions and opportunities which were not influenced by external policies aimed at improving the representation of women in construction. Third, the ‘hidden’, but potentially significant, contribution of women‐owned small construction firms is noted. The key challenge for policy and practice is to balance these external approaches with recognition of the ‘inside out’ reality of the ‘lived experiences’ of female managers. To progress this agenda there is a need for: appropriate longitudinal statistical data to quantify the scale of senior female managers and owners of small construction firms over time; and, social construction and gendered organizational analysis research to develop a general discourse on gender difference with these firms.

Managing information complexity using system dynamics on construction projects

The increase in e-commerce, the digitisation of design data and the interchange and sharing of information have made the construction sector more reliant upon IT infrastructure and systems. The design and production process is complex, dynamic, interconnected and dependent upon greater information mobility, requiring seamless exchange of data and information in real time. Small- and medium-sized enterprises (SMEs), in particular, specialty contractors, can utilise cost-effective collaboration technologies, such as cloud computing using software as a service, to help in the effective transfer of information and data. The system dynamics (SD) approach gives a better understanding of the dynamics of complex systems. SD methodology is used as a modelling and analysis tool to understand and identify the key drivers in the absorption of cloud computing for SMEs. The aim is to determine how the use of SD can improve the management of complexity of information flow, leading to improved performance for SMEs. SD is shown to be a viable tool to manage information complexity using cloud computing for performance improvement in construction.

Detecting defects in the UK new-build housing sector: a learning perspective

Rapid growth in the production of new homes in the UK is putting build quality under pressure as evidenced by an increase in the number of defects. Housing associations (HAs) contribute approximately 20% of the UK’s new housing supply. HAs are currently experiencing central government funding cuts and rental revenue reductions. As part of HAs’ quest to ramp up supply despite tight budget conditions, they are reviewing how they learn from defects. Learning from defects is argued as a means of reducing the persistent defect problem within the UK housebuilding industry, yet how HAs learn from defects is under-researched. The aim of this research is to better understand how HAs, in practice, learn from past defects to reduce the prevalence of defects in future new homes. The theoretical lens for this research is organizational learning. The results drawn from 12 HA case studies indicate that effective organizational learning has the potential to reduce defects within the housing sector. The results further identify that HAs are restricting their learning to focus primarily on reducing defects through product and system adaptations. Focusing on product and system adaptations alone suppresses HAs’ abilities to reduce defects in the future.

Key stakeholders’ perspectives towards UK new-build housing defects

Purpose Research on housing defects has limited its enquiry to the classifications of defects, potential impact of defects, and their detection and remediation during construction and the builder’s liability period, without considering the warranty period. The purpose of this paper is to better understand which impacts of defects are perceived as important by the key stakeholders involved in their detection and remediation over the construction, builder’s liability and insurer’s warranty periods. Design/methodology/approach The questionnaire survey approach was used. The questionnaire distribution list was drawn from the UK’s largest warranty provider (WP) and approved inspector’s records. The questionnaire was distributed to 2003 people, receiving 292 responses, a response rate of 15 per cent. Findings This research challenges the assertion that the house building industry (i.e. house builders (HBs), building inspectors and WPs) is predominantly cost focussed and finds that the potential impact of defects on home occupants (HOs) are their primary concern. In contrast, the HOs’ appear solely focussed on the disruption defects caused on their daily lives and perceive a lack customer focus in the house building industry. Originality/value This study provides empirical evidence of the contrasting view of the house building industry and HOs with respect to the prioritisation of the impacts of defects. Further, this research offers HBs an alternative approach to determine which defects should be targeted for reduction purposes which may lead to improved levels of customer satisfaction.

Reducing Defects through Organizational Learning within a Housing Association Environment

Housing Associations (HAs) contribute circa 20% of the UK’s housing supply. HAs are however under increasing pressure as a result of funding cuts and rent reductions. Due to the increased pressure, a number of processes are currently being reviewed by HAs, especially how they manage and learn from defects. Learning from defects is considered a useful approach to achieving defect reduction within the UK housebuilding industry. This paper contributes to our understanding of how HAs learn from defects by undertaking an initial round table discussion with key HA stakeholders as part of an ongoing collaborative research project with the National House Building Council (NHBC) to better understand how house builders and HAs learn from defects to reduce their prevalence. The initial discussion shows that defect information runs through a number of groups, both internal and external of a HA during both the defects management process and organizational learning (OL) process. Furthermore, HAs are reliant on capturing and recording defect data as the foundation for the OL process. During the OL process defect data analysis is the primary enabler to recognizing a need for a change to organizational routines. When a need for change has been recognized, new options are typically pursued to design out defects via updates to a HAs Employer’s Requirements. Proposed solutions are selected by a review board and committed to organizational routine. After implementing a change, both structured and unstructured feedback is sought to establish the change’s success. The findings from the HA discussion demonstrates that OL can achieve defect reduction within the house building sector in the UK. The paper concludes by outlining a potential ‘learning from defects model’ for the housebuilding industry as well as describing future work.

Investigating the impact of defects on key stakeholders in the UK new housing sector

The UK house building sector is facing dual pressures to expand supply, along with delivering against tougher Building Regulations’ requirements, predominantly in the areas of sustainability. A review of current literature has highlighted that the pressures the UK house building industry is currently under may be having a negative impact on build quality, causing an increase in defects. A review and synthesis of the current defect literature with respect to new-build housing and the wider construction sector has found that the prevailing emphasis is limited to the classification, causes, pathology and statistical analysis of defects. There is thus a need to better understand the overall impact of individual defects on key stakeholders within the new-build housing defect detection and remediation process. As part of ongoing research to develop and verify a defect impact assessment rating system, this paper seeks to contribute to our understanding of the impact of individual defects from a key stakeholder perspective by undertaking the literature review and synthesis phase. The literature review identifies the three distinct, but interrelated, dominant impact factors: cost, disruption, and health and safety. By pulling the strands of defect literature together the theoretical lens and key stakeholder sampling strategy is formed as the basis for the subsequent impact weighting development phase.

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

The Study of Using Self-Learning Neural Network in Automatic Construction-Site layout

A good construction-site layout plan could directly or indirectly save construction cost and improve on construction efficiency; the larger of construction project or higher of construction overlapping is, the more important of construction-site layout is plan. Moreover, today’s constructive automatic technique reaches a specific degree; the application of automatic technique in construction-site layout plan has been accentuated. In general, the requirements of construction-site facilities depend on the size and type of construction project. However, a traditional view for arranging construction-site facilities by heuristic mostly causes a disappointing layout plan and makes uncompleted judgement. In this paper, it would formulate construction-site layout problem to combinatorial optimization one. Because it’s problematic model of mathematics is NP-complete problem, it would try to adopt self-learning neural network to solve construction-site layout problem. In order to test its reliability; it will compare quality-efficiency with Random-searching and Annealing Neural Network method.