Search results

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.

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.

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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method.

Unlike similar approaches, this paper considers not only construction site parameters, but also supply chain parameters. Machine learner fusion-regression (MLF-R) is used to predict the production rate of concrete pouring tasks.

MLF-R is used on a field database including 2,600 deliveries to 507 different locations. The proposed data set and the results are compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian). The results show better performance of MLF-R obtaining the least root mean square error (RMSE) compared with other methods. Moreover, the RMSEs derived from the predictions by MLF-R in some trials had the least standard deviation, indicating the stability of this approach among similar used approaches.

The size of the database used in this study is much larger than the size of databases used in previous studies. It helps authors draw their conclusions more confidently and introduce more generalised models that can be used in the ready-mixed concrete industry.

Introducing a more stable learning method for predicting the concrete pouring production rate helps not only construction parameters, but also traffic and supply chain parameters.

The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs.

Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert.

The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases.

This approach can be applied in practice to match experts’ decisions.

In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.

Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.

The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.

The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.

The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.

Assessing front end engineering design (FEED) accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions. A framework to measure FEED accuracy does not exist in the literature or in practice, not does systematic data directly linking FEED accuracy to project performance. This paper aims to focus first on gauging and quantifying FEED accuracy, and second on measuring its impact on project performance in terms of cost change, schedule change, change performance, financial performance and customer satisfaction.

A novel measurement scheme was developed for FEED accuracy as a comprehensive assessment of factors related to the project leadership and execution teams, management processes and resources; to assess the environment surrounding FEED. The development of this framework built on a literature review and focus groups, and used the research charrettes methodology, guided by a research team of 20 industry professionals and input from 48 practitioners representing 31 organizations. Data were collected from 33 large industrial projects representing over $8.8 billion of installed cost, allowing for a statistical analysis of the framework's impact on performance.

This paper describes: (1) twenty-seven critical FEED accuracy factors; (2) an objective and scalable method to measure FEED accuracy; and (3) data showing that projects with high FEED accuracy outperformed projects with low FEED accuracy by 20 percent in terms of cost growth in relation to their approved budgets.

FEED accuracy is defined as the degree of confidence in the measured level of maturity of the FEED deliverables to serve as a basis of decision at the end of detailed scope, prior to detailed design. Assessing FEED accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions.

FEED accuracy has not been assessed before, and it turned out to have considerable project performance implications. The new framework presented in this paper is the first of its kind, it has been tested rigorously, and it contributes to both the literature body of knowledge as well as to practice. As one industry leader recently stated, “it not only helped to assess the quality and adequacy of the technical documentation required, but also provided an opportunity to check the organization's readiness before making a capital investment decision.”

This study aims to identify which elements of the vending marketing mix are the main sources of competitive advantage for the industry, how they impact vending profitability, and what are their related synergistic effects.

A full factorial experiment was developed to determine the effect of eight marketing mix scenarios on the profitability of a new vending channel in a French university library and assess the synergistic effects among three elements of a marketing mix (i.e. product quality, payment system, internal location) identified in a focus group as new sources of industry competitive advantage.

Although the main effects of product quality and payment system were weak-to-modest and insignificant, their interaction effect significantly impacted the daily net profit of the vending channel and generated the highest net synergy. The results partially challenge the marketing synergy axiom as internal location separately had a stronger impact on profitability than product quality and higher-order interaction effects do not necessarily translate into higher synergistic effects.

This research was conducted in a real-life setting and has its limitations, which future researchers can overcome by extending the temporal, geographic and product scope of the study.

The distinction that we introduced between gross and net synergy allowed us to partially challenge the prevailing marketing mix assumption that synergy is always positive (i.e. that a vending retailer can achieve synergy by selecting a combination of marketing mix elements instead of relying on them separately). Moreover, by demonstrating that marketing synergy is not a uni- but a bi-dimensional concept, we provide vending retailers with a better methodological understanding of why they may have already fallen into the synergy trap and how to avoid it in the future.

Due to considerable contributions of the construction industry to the global carbon emissions, a great deal of attention is placed on possible incorporation of carbon footprint minimization as an important objective in the planning of construction operations. The purpose of this paper is to present a framework to estimate and minimize the carbon emissions of the concrete placing operation through identifying the optimal number of pumps and the inter-arrival time of truck mixers.

The proposed framework integrates discrete event simulation and multi-objective optimization to estimate and minimize the carbon emission, costs and production rate of the concrete placing operation. An actual construction project is used to demonstrate the application of the proposed framework. Furthermore, a sensitivity analysis is performed to investigate the sensitivity of the results to variations in modeling parameters including the ratio of idle to non-idle emission rates of equipment and the activity duration distributions.

The results of the case study highlight that variations in the number of pumps and inter-arrival time of truck mixers significantly affect the carbon emissions, cost and production rate of the concrete placing operation. Furthermore, the results of the sensitivity analysis show that variations in the ratio of idle to non-idle emission rates for pumps and truck mixers have little effects on the selected setting for the project. This is contrary to the effect of uncertainty in the activity duration distributions, which was found to be significant.

Results of this study provide an insight into the trade-off between carbon emissions, cost and production rate of the concrete placing operation.

Teleworking, working from home and flexible work have gained popularity over the last few years. A shift in policies and practices in the workplace is required owing to the COVID-19 pandemic accelerating current trends in work-from-everywhere (WFE) research. This article presents a systematic literature review of WFE research from 1990 to early 2023 to understand the transformation of the field.

The Web of Science database was used to conduct this review based on rigorous bibliometric and network analysis techniques. The prominence of the research studied using SPAR-4-SLR and a collection of bibliometric techniques on selected journal articles, reviews and early access articles. Performance and keyword co-occurrence analysis form the premise of cluster analysis. The content analysis of recently published papers revealed the driving and restraining forces that help define and operationalize the concept of WFE.

The major findings indicate that the five established and accelerated trends from cluster analysis are COVID-19 and the pandemic, telework(ing), remote working, work from home and well-being and productivity. Driving and restraining forces identified through content analysis include technological breakthroughs, work–life integration challenges, inequality in the distribution of jobs, gender, shifts in industry and sector preferences, upskilling and reskilling and many more have been published post-COVID in the restraining forces category of WFE.

A key contribution of this pioneering study of “work from everywhere” is the linking of the bibliometric trends of the past three decades to the influencing and restraining factors during the pandemic. This study illustrates how WFE could be perceived differently post-COVID, which is of great concern to practitioners and future researchers.

A wide range of publications on WFE and multiple synonyms can create confusion if a systematic and effective system does not classify and associate them. This study uses both bibliometric and scientometric analyses in the context of WFE using systematic literature review (SLR) methods.

While the topic of team adaptation is gaining in prominence within the broader team effectiveness literature, there remain numerous unanswered questions about the way it affects, and is affected by, team dynamics over time. In particular, within this chapter, we seek to more fully examine the relationship between team adaptation and team cohesion to set the stage for additional investigations of team adaptation – team emergent state relationships. However, beyond merely suggesting that a linear relationship exists between team adaptation and cohesion, we envision the relationship as likely being curvilinear as well as reciprocal in nature. Additionally, we consider how temporal factors may shape this relationship by considering how the team’s performance on prior disruptions may influence the link between team cohesion and different adaptive outcomes (i.e., meritorious, maintenance, or maladaptation) as well as flowing along a feedback loop to affect team adaptation processes and team adaptability. By theorizing about these underexamined relationships, our intent is to introduce a framework that can be utilized as a foundation upon which future team adaptation research can build. Finally, we discuss how practitioners can leverage our thoughts in order to more effectively manage adaptation and cohesion within their teams.