Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
402,839
result(s) for
"building construction."
Sort by:
Recent Technological Advancements in BIM and LCA Integration for Sustainable Construction: A Review
In the high-energy, high-carbon landscape of the construction industry, a detailed and precise life cycle assessment (LCA) is essential. This review examines the role of building information modeling (BIM) software in streamlining the LCA process to enhance efficiency and accuracy. Despite its potential, challenges such as software interoperability and compatibility persist, with no unified standard for choosing BIM-integrated LCA software. Besides, the review explores the capabilities and limitations of various BIM software, LCA tools, and energy consumption tools, and presents characteristics of BIM-LCA integration cases. It critically discusses BIM-LCA integration methods and data exchange techniques, including bill of quantities import, Industry Foundation Classes (IFC) import, BIM viewer usage, direct LCA calculations with BIM plugins, and LCA plugin calculations. Finally, concluding with future perspectives, the study aims to guide the development of advanced LCA tools for better integration with BIM software, addressing a vital need in sustainable construction practices.
Journal Article
Automation in construction toward resilience : robotics, smart materials & intelligent systems
\"This book presents all aspects of automation in construction pertaining to the use of information technologies in design, engineering, construction technologies, and maintenance and management of constructed facilities. The broad scope encompasses all stages of the construction life cycle from initial planning and design, through the construction of the facility, its operation, and maintenance, to the eventual dismantling and recycling of buildings and engineering structures\"-- Provided by publisher.
BOOK
Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction
Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience in contract cost estimation. Unlike conventional building construction, green buildings are designed to utilize new technologies to reduce their operations’ environmental and societal impacts. Consequently, green buildings’ construction bidding and awarding processes have become more complicated due to difficulties forecasting the initial construction costs and setting integrated selection criteria for the winning bidders. Thus, robust green building cost prediction modeling is essential to provide stakeholders with an initial construction cost benchmark to enhance decision-making. The current study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs. The proposed models are designed to consider the influence of soft and hard cost-related attributes. Evaluation metrics (i.e., MAE, MSE, MAPE, and R2) are applied to evaluate and compare the developed algorithms’ accuracy. XGBOOST provided the highest accuracy of 0.96 compared to 0.91 for the DNN, followed by RF with an accuracy of 0.87. The proposed machine learning models can be utilized as a decision support tool for construction project managers and practitioners to advance automation as a coherent field of research within the green construction industry.
Journal Article
Job site
Following directions from the job site boss, construction workers carry out important tasks at a construction site using heavy machinery, including a bulldozer, an excavator, and a loader.
Book
Sustainability in Construction Projects: A Systematic Literature Review
This paper aims to identify the major research concepts studied in the literature of sustainability in construction projects. Two bibliometric analysis tools—(a) BibExcel and (b) Gephi, were used to analyze the bibliometrics indices of papers and visualize their interrelations as a network, respectively. Therefore, a research focus parallelship network (RFPN) analysis and keyword co-occurrence network (KCON) analysis were performed to uncover the primary research themes. The RFPN analysis clustered the studies into three major categories of evaluating sustainability, project management for sustainability, and drivers of sustainable construction. The KCON analysis revealed that while each paper had a different focus, the underlying concept of all clusters was sustainability, construction, and project management. We found that while ‘sustainability’ was the leading keyword in the first cluster, i.e., evaluating sustainability, it was the second top keyword with the eigenvector centrality of over 0.94 in the other two clusters. We also found that the concept of sustainability should be included in construction projects from the early stages of design and feasibility studies and must be monitored throughout the project life. This review showed that previous researchers used a variety of statistical and mathematical techniques such as structural equation modelling and fuzzy decision-making methods to study sustainability in construction projects. Using an integrated approach to identifying the research gaps in this area, this paper provides researchers with insights on how to frame new research to study sustainability in construction projects.
Journal Article
Construction workers
Construction workers build and repair the homes, roads, and skyscrapers we see around us every day. Readers discover fun facts about this important career as they learn about the skills and tools used by construction workers on the job. Fact boxes and a detailed graphic organizer provide additional information in an eye-catching way. Full-color photographs of construction workers give readers a clear picture of what this career is all about.
Book
Advanced Timber Construction Industry: A Review of 350 Multi-Storey Timber Projects from 2000–2021
Throughout the last two decades the timber building sector has experienced a steady growth in multi-storey construction. Although there has been a growing number of research focused on trends, benefits, and disadvantages in timber construction from various technical perspectives, so far there is no extensive literature on the trajectory of emerging architectural typologies. This paper presents an examination of architectural variety and spatial possibilities in current serial and modular multi-storey timber construction. It aims to draw a parallel between architectural characteristics and their relation to structural systems in timber. The research draws from a collection of 350 contemporary multi-storey timber building projects between 2000 and 2021. It consists of 300 built projects, 12 projects currently in construction, and 38 design proposals. The survey consists of quantitative and qualitative project data, as well as classification of the structural system, material, program, massing, and spatial organization of the projects. It then compares the different structural and design aspects to achieve a comprehensive overview of possibilities in timber construction. The outcome is an identification of the range of morphologies and a better understanding of the design space in current serial and modular multi-storey mass timber construction.
Journal Article
A Hybrid Multi-Criteria Decision Support System for Selecting the Most Sustainable Structural Material for a Multistory Building Construction
In recent years, the performance of the construction industry has highlighted the increased need for better resource efficiency, improved productivity, less waste, and increased value through sustainable construction practices. The core concept of sustainable construction is to maximize value and minimize harm by achieving a balance between social, economic, technical, and environmental aspects, commonly known as the pillars of sustainability. The decision regarding which structural material to select for any construction project is traditionally made based on technical and economic considerations with little or no attention paid to social and environmental aspects. Furthermore, the majority of the available literature on the subject considered three sustainability pillars (i.e., environmental, social, and economic), ignoring the influence of technical aspects for overall sustainability assessment. Industry experts have also noted an unfulfilled need for a multi-criteria decision-making (MCDM) technique that can integrate all stakeholders’ (project owner, designer, and constructor) opinions into the selection process. Hence, this research developed a decision support system (DSS) involving MCDM techniques to aid in selecting the most sustainable structural material, considering the four pillars of sustainability in the integrated project delivery (IPD) framework. A hybrid MCDM method combining AHP, TOPSIS, and VIKOR in a fuzzy environment was used to develop the DSS. A hypothetical eight-story building was considered for a case study to validate the developed DSS. The result shows that user preferences highly govern the final ranking of the alternative options of structural materials. Timber was chosen as the most sustainable option once the stakeholders assigned balanced importance to all factors of sustainable construction practices. The developed DSS was designed to be generic, can be used by any group of industry practitioners, and is expected to enhance objectivity and consistency of the decision-making process as a step towards achieving sustainable construction.
Journal Article