Engineering, Construction and Architectural Management最新文献

Purpose

This research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.

Design/methodology/approach

The architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.

Findings

The experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.

Practical implications

The architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bi

Purpose

Cost and schedule overruns are rife in dam projects. Normative evidence espouses overruns as though they are inimical to development and prosperity aspirations of stakeholders. This study examines the causal relationship between project financing and overruns.

Design/methodology/approach

Causative data were extracted from completion reports of 28 major dam projects in Africa. Each of the projects was financed jointly by up to 10 international development lenders. Relationships between causes of overruns and project outcomes were analysed.

Findings

Analyses elicit indicators of remarkable correlations between finance procedures and project outcomes. Lenders’ disposition to risk attenuation was the main debacles to project success. Interests had mounted, whilst release of fund was erratic and ill-timed. Finance objectives and mechanisms were grossly inadequate for projects’ intense bifurcations. Projects had slowed or stalled because lenders’ risks attenuation processes were purposed to favour lenders’ objectives, and not projects’ interests. In addition, findings also show project owners’ own funds and the number of lenders to a single project correlate with overruns.

Practical implications

Findings imply commercial complexities around major projects. They also show transactions are shaped by subtle (mis)trust behaviours in project finance procedures. Thus, scholarly solutions to project performance issues should consider behavioural issues of stakeholding parties more broadly, beyond contractors and project owners. Project finance ecosystems are vulnerable to major actors’ self-interests, opportunism and predatory conducts. Borrowers would manage this by developing and improving their capacity to build resilience and trust. Evidence shows intense borrower nations in Africa have limited capacity and acuity for these.

Originality/value

This study contextualises megaprojects in complexity rather than cost. Its additionality is in how finance steers absolute control of project environment away from project owners and how finance administration triggers risks and overrun.

Purpose

Manual monitoring is a conventional method for monitoring and managing construction safety risks. However, construction sites involve risk coupling - a phenomenon in which multiple safety risk factors occur at the same time and amplify the probability of construction accidents. It is challenging to manually monitor safety risks that occur simultaneously at different times and locations, especially considering the limitations of risk manager’s expertise and human capacity.

Design/methodology/approach

To address this challenge, an automatic approach that integrates point cloud, computer vision technologies, and Bayesian networks for simultaneous monitoring and evaluation of multiple on-site construction risks is proposed. This approach supports the identification of risk couplings and decision-making process through a system that combines real-time monitoring of multiple safety risks with expert knowledge. The proposed approach was applied to a foundation project, from laboratory experiments to a real-world case application.

Findings

In the laboratory experiment, the proposed approach effectively monitored and assessed the interdependent risks coupling in foundation pit construction. In the real-world case, the proposed approach shows good adaptability to the actual construction application.

Originality/value

The core contribution of this study lies in the combination of an automatic monitoring method with an expert knowledge system to quantitatively assess the impact of risk coupling. This approach offers a valuable tool for risk managers in foundation pit construction, promoting a proactive and informed risk coupling management strategy.

Purpose

LinkedIn, as a professional networking platform, plays a crucial role in connecting professionals globally and facilitating their professional growth. This study aims to analyse the perceptions of architects and designers regarding the utilisation of LinkedIn for career development.

Design/methodology/approach

The research focuses on gathering professional perceptions and opinions through a qualitative analysis of primary data. A semi-structured interview approach was used for data collection. The study selected 12 actively engaged professionals from the architecture and design industry in Perth, Western Australia, who actively use LinkedIn.

Findings

The study's findings reveal diverse opinions and experiences among professionals in the architecture and design industry regarding LinkedIn. It identifies several positive impacts of LinkedIn on the Australian architecture and design industry. Architects and designers recognise the potential benefits of LinkedIn in expanding their professional networks, showcasing their work, accessing learning opportunities and contributing to industry outcomes. They also appreciate the platform's ability to connect them with peers, clients, suppliers and industry, fostering knowledge sharing and collaboration in the evolving work environment.

Originality/value

This study contributes to the existing literature by extending the understanding of the importance of self-presentation on LinkedIn, identifying factors influencing career goal achievement and highlighting the role of professional connections on social media. It establishes connections between 21st-century online innovations and their practical applications within the relevant context.

Purpose

We explore the design risk factors and associated managerial practices driving collaborative risk management for design efficacy in green building projects. By illuminating project design risk as an important project risk category in its own right, the study contributes to our understanding of optimising design efficacies for collaborative project risk management.

Design/methodology/approach

The study comprises exploratory interviews conducted with 27 industry project practitioners involved in the design and delivery/implementation of Green Star-certified building projects in South Africa.

Findings

The findings discursively highlight seven sources of design risk. We also identify seven specific collaborative risk management practices for design efficacy emerging from a consideration of how risk environments vary in the Green Star-certified projects, each with its own project design risk implications.

Originality/value

The study advances our understanding of how collaborations emerging from particular relational yet context-specific practices can be optimised to strengthen project risk management.

Purpose

Construction fall-from-height accidents are not only caused by a single factor but also by the risk coupling between two or more factors. The purpose of this paper is to quantitatively analyze the risk coupling relationships between multiple factors and identify critical factors in construction fall-from-height accidents.

Design/methodology/approach

A cause analysis framework was established from the perspective of human, machine, material, management and environmental factors. The definition, the classification and the process of risk coupling were proposed. The data from 824 historical accident reports from 2011 to 2021 were collected on government websites. A risk coupling analysis model was constructed to quantitatively analyze the risk coupling relationships of multiple factors based on the N-K model. The results were classified using K-means clustering analysis.

Findings

The results indicated that the greater the number of causal factors involved in risk coupling, the higher the risk coupling value and the higher the risk of accidents. However, specific risk coupling combinations occurred when the number of their coupling factors was not large. Human, machine and material factors were determined to be the critical factors when risk coupling between them tended to pose a greater risk of accidents.

Originality/value

This study established a cause analysis framework from five aspects and constructed a theoretical model to quantitatively analyze multi-factor coupling. Several suggestions were proposed for construction units to manage accident risks more effectively by controlling the number of factors and paying more attention to critical factors coupling and management and environmental factors.

Purpose

In recent years, climate for innovation has attracted wide attention from industry and academia. It is perceived as a critical component of innovation performance in the built environment sector, especially in architectural design firms (ADFs). This study attempts to assess the degree of climate for innovation in the Vietnamese ADFs under the organizational climate theory.

Design/methodology/approach

A list of 13 innovation climate variables was found by reviewing previous studies and discussions with industry practitioners. These variables were then categorized into three principal factors (personal commitment, tolerance of difference, and support for creativity), forming the inputs of the fuzzy synthetic evaluation (FSE) analysis.

Findings

The results showed that the overall level of innovation in the climate is moderate, implying that it is still necessary for more improvements to the Vietnamese ADFs. The fuzzy analysis revealed that support for creativity was the most critical factor, followed by tolerance of difference and personal commitment.

Originality/value

The proposed climate for innovation model is practical and reliable for architectural professionals and can be applied to assess other research areas. Few studies have emphasized the innovation climate in the construction sector, so this research may broaden the knowledge and literature on the industry, especially for the ADFs.