Alireza Ahmadian F.F., Taha H. Rashidi, Ali Akbarnezhad and S. Travis Waller
Enhancing sustainability of the supply process of construction materials is challenging and requires accounting for a variety of environmental and social impacts on top of the…
Abstract
Purpose
Enhancing sustainability of the supply process of construction materials is challenging and requires accounting for a variety of environmental and social impacts on top of the traditional, mostly economic, impacts associated with a particular decision involved in the management of the supply chain. The economic, environmental, and social impacts associated with various components of a typical supply chain are highly sensitive to project and market specific conditions. The purpose of this paper is to provide decision makers with a methodology to account for the systematic trade-offs between economic, environmental, and social impacts of supply decisions.
Design/methodology/approach
This paper proposes a novel framework for sustainability assessment of construction material supply chain decisions by taking advantage of the information made available by customized building information models (BIM) and a number of different databases required for assessment of life cycle impacts.
Findings
The framework addresses the hierarchy of decisions in the material supply process, which consists of four levels including material type, source of supply, supply chain structure, and mode of transport. The application is illustrated using a case study.
Practical implications
The proposed framework provides users with a decision-making method to select the most sustainable material alternative available for a building component and, thus, may be of great value to different parties involved in design and construction of a building. The multi-dimensional approach in selection process based on various economic, environmental, and social indicators as well as the life cycle perspective implemented through the proposed methodology advocates the life cycle thinking and the triple bottom line approach in sustainability. The familiarity of the new generation of engineers, architects, and contractors with this approach and its applications is essential to achieve sustainability in construction.
Originality/value
A decision-making model for supply of materials is proposed by integrating the BIM-enabled life cycle assessment into supply chain and project constraints management. The integration is achieved through addition of a series of attributes to typical BIM. The framework is supplemented by a multi-attribute decision-making module based on the technique for order preference by similarity to ideal solution to account for the trade-offs between different economic and environmental impacts associated with the supply decisions.
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Zineb Ibn Majdoub Hassani, Abdellah El Barkany, Abdelouahhab Jabri, Ikram El Abbassi and Abdel Moumen Darcherif
This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their…
Abstract
Purpose
This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs.
Design/methodology/approach
The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes.
Findings
The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing.
Originality/value
This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.
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Abdul-Majeed Mahamadu, Patrick Manu, Lamine Mahdjoubi, Colin Booth, Clinton Aigbavboa and F.H. Abanda
The emergence of building information modelling (BIM) has led to the need for pre-qualification and selection of organisations capable of working within a BIM environment. Several…
Abstract
Purpose
The emergence of building information modelling (BIM) has led to the need for pre-qualification and selection of organisations capable of working within a BIM environment. Several criteria have been proposed for the assessment of an organisation’s BIM capability during the pre-qualification and selection phase of projects. However, no studies have sought to empirically establish whether organisations selected on the basis of such criteria have actually been the most successful at delivering BIM on projects. The purpose of this paper is to address the aforementioned gap through a comparison of predicted BIM capability and post-selection performance.
Design/methodology/approach
BIM capability of firms in a case study was predicted using 28 BIM pre-qualification and selection criteria, prioritised based on their perceived contribution to BIM delivery success from a survey of practitioners on BIM-enabled projects. The comparison of predicted BIM capability and post-selection performance was, on the other hand, achieved through the application of the Technique to Order Preference by Similarity to Ideal Solution and fuzzy sets theory (Fuzzy-TOPSIS).
Findings
Findings underscore the reliability of the 28 BIM pre-qualification and selection criteria as well as the priority weightings proposed for their use in predicting BIM capability and likelihood of performance. The findings have highlighted the importance of criteria related as previous BIM use experience as well as information processing maturity as critical indicators of the capability of organisations, particularly design firms.
Originality/value
Overall, the findings highlight the need for prioritisation of BIM pre-qualification and selection criteria on the basis of their actual contribution to delivery success from post-selection evaluation of performance.
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Johnny Kwok Wai Wong, Mojtaba Maghrebi, Alireza Ahmadian Fard Fini, Mohammad Amin Alizadeh Golestani, Mahdi Ahmadnia and Michael Er
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Alireza Ahmadian Fard Fini, Mojtaba Maghrebi, Perry John Forsythe and Travis Steven Waller
Measuring onsite productivity has been a substance of debate in the construction industry, mainly due to concerns about accuracy, repeatability and unbiasedness. Such…
Abstract
Purpose
Measuring onsite productivity has been a substance of debate in the construction industry, mainly due to concerns about accuracy, repeatability and unbiasedness. Such characteristics are central to demonstrate construction speed that can be achieved through adopting new prefabricated systems. Existing productivity measurement methods, however, cannot cost-effectively provide solid and replicable evidence of prefabrication benefits. This research proposes a low-cost automated method for measuring onsite installation productivity of prefabricated systems.
Design/methodology/approach
Firstly, the captured ultra-wide footages are undistorted by extracting the curvature contours and performing a developed meta-heuristic algorithm to straighten these contours. Then a preprocessing algorithm is developed that could automatically detect and remove the noises caused by vibrations and movements. Because this study aims to accurately measure the productivity the noise free images are double checked in a specific time window to make sure that even a tiny error, which have not been detected in the previous steps, will not been amplified through the process. In the next step, the existing side view provided by the camera is converted to a top view by using a spatial transformation method. Finally, the processed images are compared with the site drawings in order to detect the construction process over time and report the measured productivity.
Findings
The developed algorithms perform nearly real-time productivity computations through exact matching of actual installation process and digital design layout. The accuracy and noninterpretive use of the proposed method is demonstrated in construction of a multistorey cross-laminated timber building.
Originality/value
This study uses footages of an already installed surveillance camera where the camera's features are unknown and then image processing algorithms are deployed to retrieve accurate installation quantities and cycle times. The algorithms are almost generalized and versatile to be adjusted to measure installation productivity of other prefabricated building systems.
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Perry Forsythe and Alireza Ahmadian Fard Fini
The short life cycle replacement of fitout in modern high-rise office buildings represents an under-researched waste problem. This paper aims to quantify the amount of demolition…
Abstract
Purpose
The short life cycle replacement of fitout in modern high-rise office buildings represents an under-researched waste problem. This paper aims to quantify the amount of demolition waste from office strip-out including attention to waste streams going to landfill, reuse and recycling.
Design/methodology/approach
Quantitative waste data (by weight) were measured from 23 office fitout projects situated in “A” grade office building stock from the Sydney CBD. Waste streams were measured separately for landfill, reuse and recycled materials. Descriptive and clustering statistics are presented and analysed.
Findings
From a total of 9,167 tonnes office fitouts demolished, 5,042 tonnes are going to landfill. The main contributor to landfill stream is the mixed waste generated in a fast-track demolition process. This approach partly resulted from the office interiors lacking regularity and easy disassembly. Moreover, considerable variability is observed in the waste per area, the waste streams and the waste compositions. Also, it is noteworthy that the recycled waste stream considerably increases when there exist economically viable conversion facilities, as for metals, hard fills and plasterboards.
Research limitations/implications
The research is focused upon work practices that take place in Australia; therefore, generalisability is limited to situations that have similar characteristics. Future studies are needed to verify and extend the findings of this research.
Practical implications
A key area arising from the research findings is the need to design fitout with recycling and reuse in mind to divert more from landfill. This must explore and incorporate onsite demolition processes to ensure the design is well suited to commercially dominant processes in the overall demolition process, as well as attention to developing economies of scale and viability in re-sale markets for reused items.
Originality/value
Little empirical or quantitative research exists in the area of office fitout waste. This research provides entry to this area via quantifiable data that enables comparison, benchmarking and diagnostic ability that can be used to underpin strategic solutions and measurement of improvements.
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Elham Yousefi, Alireza Ahmadian Fard Fini and Santhosh Loganathan
This study aims to develop a production-oriented approach for optimal mass-customisation of floor panel layouts in cross-laminated timber (CLT) buildings. The study enables…
Abstract
Purpose
This study aims to develop a production-oriented approach for optimal mass-customisation of floor panel layouts in cross-laminated timber (CLT) buildings. The study enables meeting building clients’ unique floor plan requirements at an optimal cost and simultaneously enhances manufacturers’ profit by minimising material and manufacturing process waste.
Design/methodology/approach
The present research uses a hybrid approach consisting of field data collection, mathematical modelling, development of a Genetic Algorithm (GA) and scenario analysis. Field data includes engineered timber production information, design data and building code requirements. The study adopts the Flexible Demand Assignment (FDA) technique to formulate a mathematical model for optimising the design of mass timber buildings and employs GA to identify optimal production solutions. Scenario analysis is performed to validate model outputs.
Findings
The proposed model successfully determines the load-bearing wall placement and building spans and specifications of floor panels that result in optimal production efficiency and the desired architectural layout. The results indicate that buildings made of a single category of thickness of panels but customised in various lengths to suit building layout are the most profitable scenario for CLT manufacturers and are a cost-effective option for clients.
Originality/value
The originality of the present study lies in its mathematical and model-driven approach towards implementing mass customisation in multi-storey buildings. The proposed model has been developed and validated based on a comprehensive set of real-world data and constraints.
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Johnny Kwok Wai Wong, Fateme Bameri, Alireza Ahmadian Fard Fini and Mojtaba Maghrebi
Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically…
Abstract
Purpose
Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.
Design/methodology/approach
A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.
Findings
The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.
Originality/value
The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.
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Muneeb Afzal, Johnny Kwok Wai Wong and Alireza Ahmadian Fard Fini
Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they…
Abstract
Purpose
Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they harbour risks like schedule delays and increased project costs, underlining the importance of strategic RFI management in construction projects. Despite this, a lack of literature dissecting RFI processes impedes a full understanding of their intricacies and impacts. This study aims to bridge the gap through a comprehensive literature review, delving into RFI intricacies and implications, while emphasising the necessity for strategic RFI management to prevent project risks.
Design/methodology/approach
This research study systematically reviews RFI-related papers published between 2000 and 2023. Accordingly, the review discusses key themes related to RFI management, yielding best practices for industry stakeholders and highlighting research directions and gaps in the body of knowledge.
Findings
Present RFI management platforms exhibit deficiencies and lack analytics essential for streamlined RFI processing. Complications arise in building information modelling (BIM)-enabled projects due to software disparities and interoperability hurdles. The existing body of knowledge heavily relies on manual content analysis, an impractical approach for the construction industry. The proposed research direction involves automated comprehension of unstructured RFI content using advanced text mining and natural language processing techniques, with the potential to greatly elevate the efficiency of RFI processing.
Originality/value
The study extends the RFI literature by providing novel insights into the problemetisation with the RFI process, offering a holistic understanding and best practices to minimise adverse effects. Additionally, the paper synthesises RFI processes in traditional and BIM-enabled project settings, maps a causal-loop diagram to identify associated issues and summarises approaches for extracting knowledge from the unstructured content of RFIs. The outcomes of this review stand to offer invaluable insights to both industry practitioners and researchers, enabling and promoting the refinement of RFI processes within the construction domain.
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Biyanka Ekanayake, Alireza Ahmadian Fard Fini, Johnny Kwok Wai Wong and Peter Smith
Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.