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Recent years have seen major changes in the approach to Computer Aided Design (CAD) in the architectural, engineering and construction (AEC) sector. CAD is increasingly becoming a standard design tool, facilitating lower development costs and a reduced design cycle. Not only does it allow a designer to model designs in two and three dimensions but also to model other dimensions, such as time and cost into designs. Computer Aided Design Guide for Architecture, Engineering and Construction provides an in-depth explanation of all the common CAD terms and tools used in the AEC sector. It describes each approach to CAD with detailed analysis and practical examples. Analysis is provided of the strength and weaknesses of each application for all members of the project team, followed by review questions and further tasks. Coverage includes: 2D CAD 3D CAD 4D CAD nD modelling Building Information Modelling parametric design, virtual reality and other areas of future expansion. With practical examples and step-by step guides, this book is essential reading for students of design and construction, from undergraduate level onwards. 1. Introduction to CAD for the AEC/FM industry 2. Project and product modelling 3. 2D CAD 4. 3D CAD 5. BIM (Building Information Modelling) 6. 4D CAD 7 . nD Modelling Song Wu is the Programme Director for BSc (Hons) in Quantity Surveying at the University of Salford. His research interests include product and process modelling, data modelling and computer simulation. In 2010, Dr Wu was awarded the UK China Fellowship for Excellence for his collaboration research with leading Chinese research institutions. Ghassan Aouad is the Pro-Vice-Chancellor for Research and Innovation at the University of Salford. He is also Co-Director of the £5m EPSRC-funded Salford Centre for Research & Innovation in the Built & Human Environment, a visiting professor at Universiti Teknologi, Malaysia (UTM), and Fellow of the CIOB. Professor Aouad has spent the last 20 years teaching and researching subjects related to information modelling and visualisation, nD simulation and process mapping. Timothy Onyenobi (BSc Hons, MSc, PhD, MNIA, ICIOB, FInstCPD) is a Chartered Architect (Nigeria) and a Research Fellow with SCRI Sobe, University of Salford. He specialises in CAD and BIM and has been involved in numerous architectural projects in Nigeria and UK. Angela Lee is the Director of Postgraduate Taught Studies and Programme Director of the BSc (Hons) Architectural Design & Technology course within the School of the Built Environment, University of Salford. Her research and teaching centres on design management, process management, performance measurement and nD modeling.

The paper presents a comparative study on how to determine computer-aided determination, using AutoCAD program, of the area and perimeter of a contour. In this paper, the authors propose a way of determining two parameters frequently used in construction practice and spatial planning through two different working procedures than those existing in the analysed program.

The paper presents a comparative study on how to determine computer-aided determination, using AutoCAD program, of the area and perimeter of a contour. In this paper, the authors propose a way of determining two parameters frequently used in construction practice and spatial planning through two different working procedures than those existing in the analysed program.

Purpose Three-dimensional printing of concrete (3DPC) has a potential for the rapid industrialization of the housing sector, with benefits of reduced construction time due to no formwork requirement, ease of construction of complex geometries, potential high construction quality and reduced waste. Required materials adaption for 3DPC is within reach, as concrete materials technology has reached the point where performance-based specification is possible by specialists. This paper aims to present an overview of the current status of 3DPC for construction, including existing printing methods and material properties required for robustness of 3DPC structures or structural elements. Design/methodology/approach This paper has presented an overview of three categories of 3DPC systems, namely, gantry, robotic and crane systems. Material compositions as well as fresh and hardened properties of mixes currently used for 3DPC have been elaborated. Findings This paper presents an overview of the state of the art of 3DPC systems and materials. Research needs, including reinforcement in the form of bars or fibres in the 3D printable cement-based materials, are also addressed. Originality/value The critical analysis of the 3D concrete printing system and materials described in this review paper is original.

There is a need to apply lean approaches in construction projects. Both BIM and IoT are increasingly being used in the construction industry. However, using BIM in conjunction with IoT for sustainability purposes has not received enough attention in construction. In particular, the capability created from the combination of both technologies has not been exploited. There is a growing consensus that the future of construction operation tends to be smart and intelligent, which would be possible by a combination of both information systems and sensors. This investigation aims to find out the recent efforts of utilizing BIM for lean purposes in the last decade by critically reviewing the published literature and identifying dominant clusters of research topics. More specifically, the investigation is further developed by identifying the gaps in the literature to utilize IoT in conjunction with BIM in construction projects to facilitate applying lean techniques in a more efficient way in construction projects. A systematic review method was designed to identify scholarly papers covering both concepts “lean” and “BIM” in construction and possibilities of using IoT. A total of 48 scholarly articles selected from 26 construction journals were carefully reviewed thorough perusal. The key findings were discussed with industry practitioners. The transcriptions were analyzed employing two coding and cluster analysis techniques. The results of the cluster analysis show two main directions, including the recent practice of lean and BIM interactions and issues of lean and BIM adoption. Findings revealed a large synergy between lean and BIM in control interactions and reduction in variations, and surprisingly there are many uncovered areas in this field. The results also show that the capability of IoT is also largely not considered in recent developments. The number of papers covering both lean and BIM is very limited, and there is a large clear gap in understanding synergetic interactions of lean concepts applying in BIM and IoT in specific fields of construction such as sustainable infrastructure projects.

Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the compressive strength of this concrete material is considered as a crucial parameter and an important concern for construction engineers regarding its application. In the present work, the 28-days compressive strength of recycled aggregate concrete is investigated through four artificial intelligence techniques based on a meta-heuristic search of sociopolitical algorithm (i.e. ICA) and XGBoost, called the ICA-XGBoost model. Based on performance indices, the optimum among these developed models proved to be ICA-XGBoost model. Namely, findings demonstrated that the proposed ICA-XGBoost model performed better than the other models (i.e. ICA-ANN, ICA-SVR, and ICA-ANFIS models) in estimating compressive strength of recycled aggregate concrete. The suggested model can be used in construction engineering in order to ensure adequate mechanical performance of the recycled aggregate concrete and allow its safe use for building purposes.

Purpose – The purpose of this paper is to investigate the adhesion of polymer materials printed directly onto fabrics using entry-level fused deposition modelling (FDM) machines. A series of functional and decorative parts were designed to explore the limitations and to identify potential applications. Design/methodology/approach – A series of shapes and structures were designed as 3D computer-aided design (CAD) solids to determine whether complex parts could be printed directly onto the surface of fabrics. The structures were fabricated using an entry-level FDM printer with acrylonitrile butadiene styrene, polylactic acid (PLA) and nylon on eight different types of synthetic and man-made woven and knit fabrics. The results were recorded according to four parameters – the warp, bond, print quality and flex – before comparing the data sets. Findings – Among the three polymers, PLA showed the best results when printed on the eight different types of fabrics, having extremely good adhesion with little warp, yet displaying a high quality of print with good flexural strength. For the fabrics, woven cotton, woven polywool and knit soy had excellent adhesion when the three polymers were deposited. Research limitations/implications – Future work should cover a wider range of polymers and textiles and incorporate more functional features for testing. Other aspects include modifying the fibre surface through mechanical or chemical means to achieve a more efficient adhesion with the fibre and examining the deposition process in terms of temperature, pressure and build density. Future work should also investigate the feasibility for large-scale production. Practical implications – This paper supports work on wearable electronics by integrating comfortable textiles with hard wearing parts without compromising on quality and fit and combining additive manufacturing processes with textiles to maintain the drape characteristics of the fabric. Polymer–textile deposition will contribute to new applications and functional products such as orthopaedic braces for medical use or for decorative features such as buttons and trimmings for garments. Originality/value – This paper has contributed to new knowledge by providing a better understanding of polymer materials being printed directly onto fabrics using entry-level FDM machines.

Full-face tunnel boring machine (TBM) is a modern and efficient tunnel construction equipment. A reliable and accurate TBM performance (like penetration rate, PR) prediction can reduce the cost and help to select the appropriate construction method. Therefore, this study introduces a new hybrid intelligence technique, i.e., grey wolf optimizer-feature weighted-multiple kernel-support vector regression (GWO-FW-MKL-SVR) to predict TBM PR. For this purpose, a tunnel in China was selected as a case study and the most important parameters on TBM performance, i.e., chamber earth pressure, total thrust, cutterhead torque, cutterhead speed, cohesion, internal friction angle, compression modulus, the ratio of boulder, uniaxial compressive strength and rock quality designation, were measured and considered as model inputs. To show the capability of the GWO-FW-MKL-SVR model, three models including biogeography-based optimization (BBO)-FW-MKL-SVR, MKL-SVR, and SVR were also proposed to predict the TBM PR. To select the best predictive models, some performance indices, i.e., coefficient of determination (R2), root mean square error (RMSE) and variance accounted for (VAF) were considered and calculated. The obtained results showed that the GWO-FW-MKL-SVR model receives the highest accuracy in predicting the TBM PR for both train and test stages. R2 values of 0.946 and 0.894, for train and test stages of the GWO-FW-MKL-SVR model, respectively, confirmed that this new hybrid model is considered as a powerful, applicable and simple technique in predicting the TBM PR. By performing feature weight analysis, it was found that the effects of the uniaxial compressive strength, rock quality designation and cutterhead speed features were higher than the other input parameters on the TBM PR.

3D printing, is a newly adopted technique in the construction sector with the aim to improve the economics and alleviate environmental impacts. This study assesses the eco-efficiency of 3D printing compared to conventional construction methods in large-scale structural fabrication. A single-storey 3D-printed house was selected in the United Arab Emirates to conduct the comparative assessment against traditional concrete construction. The life cycle assessment (LCA) framework is utilized to quantify the environmental loads of raw materials extraction and manufacturing, as well as energy consumption during construction and operation phases. The economics of the selected structural systems were investigated through life cycle costing analysis (LCCA), that included mainly the construction costs and energy savings. An eco-efficiency analysis was employed to aggregate the results of the LCA and LCCA into a single framework to aid in decision making by selecting the optimum and most eco-efficient alternative. The findings revealed that houses built using additive manufacturing and 3D printed materials were more environmentally favourable. The conventional construction method had higher impacts when compared to the 3D printing method with global warming potential of 1154.20 and 608.55 kg CO2 eq, non-carcinogenic toxicity 675.10 and 11.9 kg 1,4-DCB, and water consumption 233.35 and 183.95 m3, respectively. The 3D printed house was also found to be an economically viable option, with 78% reduction in the overall capital costs when compared to conventional construction methods. The combined environmental and economic results revealed that the overall process of the 3D-printed house had higher eco efficiency compared to concrete-based construction. The main results of the sensitivity analysis revealed that up to 90% of the environmental impacts in 3D printing mortars can be mitigated with decreasing cement ratios.

The discovery of chemical reactions is an inherently unpredictable and time-consuming process 1 . An attractive alternative is to predict reactivity, although relevant approaches, such as computer-aided reaction design, are still in their infancy 2 . Reaction prediction based on high-level quantum chemical methods is complex 3 , even for simple molecules. Although machine learning is powerful for data analysis 4 , 5 , its applications in chemistry are still being developed 6 . Inspired by strategies based on chemists’ intuition 7 , we propose that a reaction system controlled by a machine learning algorithm may be able to explore the space of chemical reactions quickly, especially if trained by an expert 8 . Here we present an organic synthesis robot that can perform chemical reactions and analysis faster than they can be performed manually, as well as predict the reactivity of possible reagent combinations after conducting a small number of experiments, thus effectively navigating chemical reaction space. By using machine learning for decision making, enabled by binary encoding of the chemical inputs, the reactions can be assessed in real time using nuclear magnetic resonance and infrared spectroscopy. The machine learning system was able to predict the reactivity of about 1,000 reaction combinations with accuracy greater than 80 per cent after considering the outcomes of slightly over 10 per cent of the dataset. This approach was also used to calculate the reactivity of published datasets. Further, by using real-time data from our robot, these predictions were followed up manually by a chemist, leading to the discovery of four reactions. A robot instructed by a machine learning algorithm and coupled with real-time spectroscopic systems provides fast and accurate reaction outcome predictions and reactivity assessments, leading to the discovery of new reactions.