Search results

Cost overruns are an inevitable issue in design and build (D&B) projects. In D&B projects, causes for cost overruns can be managed by adopting appropriate building information modelling (BIM) functions. Because there is a research gap in synergy between the use of BIM for mitigating cost overruns in D&B projects, this study aims to evaluate the adaptability of BIM to manage cost overrun issues in them.

Research objectives were attained through a quantitative research approach adopting the Delphi technique, which consists of three rounds of a questionnaire survey. Through statistical tools, the collected data were analysed.

This research revealed the ten most crucial causes for cost overruns in D&B projects, where continuous changes in designs and drawings are the top causes. Change and revision management and interoperability are the most crucial BIM functions to address the aforementioned cause. Subsequently, 16 enablers, 26 barriers and 19 strategies to implement BIM to manage the identified significant causes of cost overruns were overviewed.

This study addresses the literature gap pertaining to the cost overrun in D&B projects and the application of BIM by studying the causes for cost overrun, suggesting BIM functions to mitigate the above cause. Moreover, this study assessed the probable barriers and enablers for BIM adoption in construction projects from D&B perspective.

Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product and the as-planned design. Computer vision (CV) technology is applied to automate the CPM process. However, the synergy between the CV and CPM in literature and industry practice is lacking. This study aims to fulfil this research gap.

A Delphi qualitative approach was used in this study by conducting two interview rounds. The collected data was analysed using manual content analysis.

This study identified seven stages of CPM; data acquisition, information retrieval, verification, progress estimation and comparison, visualisation of the results and schedule updating. Factors such as higher accuracy in data, less labourious process, efficiency and near real-time access are some of the significant enablers in instigating CV for CPM. Major challenges identified were occlusions and lighting issues in the site images and lack of support from the management. The challenges can be easily overcome by implementing suitable strategies such as familiarisation of the workforce with CV technology and application of CV research for the construction industry to grow with the technology in line with other industries.

This study addresses the gap pertaining to the synergy between the CV in CPM literature and the industry practice. This research contributes by enabling the construction personnel to identify the shortcomings and the opportunities to apply automated technologies concerning each stage in the progress monitoring process.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

Despite the positive impacts of the construction sector on enhancing economic growth and ensuring societal well-being, its negative impacts on the environment from unsustainable resource consumption levels, emission of greenhouse gases (GHGs) and waste generation is monumental. Circular economy (CE) concept is identified globally as an avenue to address problems regarding adverse impacts of construction on the environment. This paper presents the principles of CE as an avenue for enhancing environmental sustainability during the pre-construction stage within Sri Lankan construction projects.

This research was approached through a qualitative research method. Data were collected through semi-structured interviews with subject matter experts. The number of experts were limited due to lack of experts with knowledge on the subject area in Sri Lanka. Data were analysed using content analysis.

Findings revealed a range of activities under each R principle of CE, that is, reduce, reuse, recycle, redesign, reclassification and renewability that could be implemented during the pre-construction stage, thereby providing a guide for construction professionals in implementing CE at the pre-construction stage. The need to expand knowledge on CE concepts within the Sri Lankan construction sector was recognized.

This study provides a qualitative in-depth perspective on how 6R principles of CE could be integrated to a construction project during the pre-construction stage. By adopting the proposed activities under CE principles, construction professionals can enhance the environmental sustainability of construction projects.

Many studies have focused on embodied energy (EE) and operational energy (OE), but a shortage of studies on decision making, which involves several decision makers whose decisions can affect the energy performance of buildings, is evident. From the stages of the project life cycle, the design stage is identified as the ideal stage for integrating energy efficiency into buildings. Therefore, the purpose of this paper is to revisit the role of professionals in designing energy-conscious buildings with low EE and OE.

This study administered a qualitative approach. Data were collected through semi-structured interviews only with 12 experts, due to the lack of expertise in the subject matter. The data were analyzed using manual content analysis.

The outcomes revealed the necessity to revisit the role of construction professionals in terms of adopting energy-efficient building design concepts from the project outset. The roles of the key professional groups (i.e. architects, structural engineers, services engineers and quantity surveyors) were identified through this research. Common issues in designing energy-efficient buildings and the means of addressing such problems were outlined.

This study contributes to the knowledge by revisiting the roles of construction professionals and proposing how they could leverage their strengths to play the important role and contribute collectively to design buildings with both low OE and EE.

This paper aims to propose an 18th-order nonlinear model for doubly fed induction generator (DFIG) wind turbines. Based on the proposed model, which is more complete than the models previously developed, an extended Kalman filter (EKF) is used to estimate the DFIG state variables.

State estimation is a popular approach in power system control and monitoring because of minimizing measurement noise level and obtaining non-measured state variables. To estimate all state variables of DFIG wind turbine, it is necessary to develop a model that considers all state variables. So, an 18th-order nonlinear model is proposed for DFIG wind turbines. EKF is used to estimate the DFIG state variables based on the proposed model.

An 18th-order nonlinear model is proposed for DFIG wind turbines. Furthermore, based on the proposed model, its state variables are estimated. Simulation studies are done in four cases to verify the ability of the proposed model in the estimation of state variables under noisy, wind speed variation and fault condition. The results demonstrate priority of the proposed model in the estimation of DFIG state variables.

Evaluating DFIG model to estimate its state variables precisely.

The chapter introduced stealth construction as a transformative concept emerging in the construction industry, emphasising resilience and efficiency through innovative technologies, cutting-edge materials, and advanced methodologies. Drawing parallels to stealth aircraft, stealth construction aims to create buildings and infrastructure that blend seamlessly into their environments, minimising environmental impact and enhancing sustainability. This approach integrates smart materials, strategic planning, and covert practices throughout all construction stages, ensuring robust, adaptive, and discreet structures. Stealth construction represents a significant paradigm shift by prioritising energy efficiency, environmental responsibility, and resilience against natural disasters and cyber threats. It combines modern aerospace engineering principles with diverse construction practices to achieve construction invincibility, setting new standards for the industry regarding sustainability, security, and aesthetics.

The purpose of this study is to investigate the current construction progress monitoring (CPM) process in relation to the contractual obligations, how project management teams carry out this activity in the field and why teams continue to adopt the current method. The study aims to provide a comprehensive understanding of the current monitoring process and its effectiveness, identify any shortcomings and propose recommendations for improvements that can lead to better project outcomes.

The study conducted semi-structured interviews with 28 construction management practitioners to explore their views on contractual requirements, traditional progress monitoring practices and advanced monitoring methods. Thematic analysis was used to identify existing processes, practices and incentives for advanced monitoring.

Standard construction contracts mandate current progress monitoring practices, which often rely on manual, document-centric and labor-intensive methods, leading to slow and erroneous progress reporting and project delays. Key barriers to adopting advanced tools include rigid contractual clauses, lack of incentives and the absence of reliable automated tools. A holistic automated approach that covers the entire CPM process, from planning to claim management, is needed as a viable alternative to traditional practices.

The study's findings can inform researchers, stakeholders and decision-makers about the existing monitoring practices and contribute to enhancing project management practices.

The study identified contractually mandated progress monitoring processes, traditional methods of collecting, transferring, analyzing and dispensing progress-related information and potential incentives and points of departure towards technologically advanced methods.

A comprehensive literature review was conducted to identify the lean principles and the challenges of building refurbishment. To have an in-depth investigation of the application of lean principles to address the challenges of refurbishment projects, ten expert interviews following a qualitative research approach were utilised in this research. Data were analysed using manual content analysis to derive the framework.

The refurbishment of buildings has attracted the attention of the present construction industry. However, uncertain project characteristics, information deficiency, limited space for construction activities and less stakeholder involvement make it complex. Since the lean concept effectively deals with complex and uncertain projects, this study focusses to investigate the application of lean principles to overcome the challenges of refurbishment projects in Sri Lanka by developing a framework.

It was found that the five main lean principles of customer value, value stream, value flow, pull and perfection are appropriate for building refurbishment projects in Sri Lanka. Precise identification of clients and end-users, value adding and non-value adding activities, interruptions and stakeholder communication chains, setting scope, examining the possible technologies and taking measures to deliver the exact product to ensure the successful application of lean principles for refurbishment projects. Further, 27 benefits of five lean principles were identified which can be used to address the 13 identified challenges of building refurbishment of projects. Finally, a framework has developed portraying the application of lean principles in building refurbishment.

The framework developed is beneficial for the building refurbishment project team to address the barriers of refurbishment projects by applying lean principles.

This framework can be used as a guideline for the implementation of building refurbishment projects by addressing their challenges with lean principles.