Abstract The paper presents the state-of-the-art concerning the current achievements in the field of 3D printing of buildings and building components. The 3D printing technologies, comparing to traditional techniques of constructing the buildings, could be considered as environmental friendly derivative giving almost unlimited possibilities for geometric complexity realizations. Two kinds of technologies were described in this paper with pointing to Contour Crafting as a promising technique that may be able to revolutionize construction industry in near future. Numerous advantages of this technology, such as reduction of the costs and time, minimizing the pollution of environment and decrease of injuries and fatalities on construction sites could be cited. Despite many advantages and hopes, some concerns are summarized in the conclusions, as the technology still has many limitations. A brief description of few examples of pioneering usage of 3D printing in construction industry are presented (Canal House in Amsterdam, WinSun company and printing application for building carried out by Skanska company). Creating a model that will be appropriate for 3D printers is possible in many different modelling programs. One of the most popular formats for sharing such models is STL format. In the paper sample models crated in Autodesk Inventor are shown, but also other tools suitable for preparing models for 3D printing are briefly discussed.
Recycled brick aggregates (RBAs) are produced by the process of crushing unused bricks.This approach offers an effective solution for addressing environmental degradation and limited availability of natural resources in civil engineering sector. The objective of this study is to promote the widespread adoption of recycled brick
aggregate concrete (RBAC) in the construction industry. The elastic modulus (E_RBA) of concrete produced from recycled brick aggregate is calculated by applying three alternative methodologies. This research presents a comparison of the Sand cat swarm optimization (SCSO), a hybrid optimal approach when utilized for two standard machine learning methods: Random Forests Analysis (RFA) and Gradient Boosting Regression (GBR). The determination of the elastic modulus of recycled brick aggregate in concrete involves the use of six distinct factors. The variables are determined using a computerized database that contains 123 test results from prior research.This approach was implemented to ensure a thorough evaluation of the framework. Based on the presented metrics in the training section, PI and U (95%) decreased from 0.0345 and 4.5938 related to RFA(S) to
0.0385 and 5.1174 related to GBR(S), respectively. According to the results, the outcome of metrics (PI and U_(95%)) in the previous sentence, for the testing section, it was reduced from 0.0301 and 3.4011 related to RFA(S) to 0.0338 and 3.7992 related to GBR(S). By comparing all the indicators and total of scores for metrics in two
models called GBR(S) and RFA(S), it is evident that model RFA(S) outperformed model GBR(S) (total scores for metrics in GBR(S) and RFA(S) are 16 and 32)
Engineering (General). Civil engineering (General), Chemical engineering
Muhammad Ali, Ayesha Zubair, Jahanzaib Israr
et al.
Climate change impacts the demand of the construction industry to reduce its carbon footprint while increasing the resilience of the buildings. This twofold need emphasises better understanding and cost prediction of green resilient buildings based on their ‘resilience’ and ‘sustainability’, which is very limited or based on obsolete perceptions and stereotypes. This study presents a novel index to predict the cost of converting a conventional building to a green-resilient building. In this study; twenty factors based on comprehensive market research have been analysed both quantitatively and qualitatively. An economic impact model has been developed for the cost prediction of green resilient buildings through a novel Green Resilient Building Index, based on sustainability and resilience factors, and the cost of conventional buildings. It has been observed that on-ground construction cost and predicted values fall within 10 % of the 1:1 line validating the precision and confidence level of the research data. Single-factor analysis of variance showed the acceptable precision of the results at a confidence level of 95 %. Nevertheless, this study envisions supporting the professionals and policymakers to develop a sustainable construction industry.
Mehrdad Saadatmand, Abbas Khan, Beatriz Marin
et al.
The evolving landscape of software development demands that software testers continuously adapt to new tools, practices, and acquire new skills. This study investigates software testing competency needs in industry, identifies knowledge gaps in current testing education, and highlights competencies and gaps not addressed in academic literature. This is done by conducting two focus group sessions and interviews with professionals across diverse domains, including railway industry, healthcare, and software consulting and performing a curated small-scale scoping review. The study instrument, co-designed by members of the ENACTEST project consortium, was developed collaboratively and refined through multiple iterations to ensure comprehensive coverage of industry needs and educational gaps. In particular, by performing a thematic qualitative analysis, we report our findings and observations regarding: professional training methods, challenges in offering training in industry, different ways of evaluating the quality of training, identified knowledge gaps with respect to academic education and industry needs, future needs and trends in testing education, and knowledge transfer methods within companies. Finally, the scoping review results confirm knowledge gaps in areas such as AI testing, security testing and soft skills.
Privacy is an instance of a social norm formed through legal, technical, and cultural dimensions. Institutions such as regulators, industry, and researchers act as societal agents that both influence and respond to evolving norms. Attempts to promote privacy are often ineffective unless they account for this complexity and the dynamic interactions among these actors. Privacy enhancing technologies (PETs) are technical solutions for privacy issues that enable collaborative data analysis, allowing for the development of solutions that benefit society, all while ensuring the privacy of individuals whose data is being used. However, despite increased privacy challenges and a corresponding increase in new regulations being proposed by governments across the globe, a low adoption rate of PETs persists. In this work, we investigate the factors influencing industry's decision-making processes around PETs adoption as well as the extent to which privacy regulations inspire such adoption. We conducted a qualitative survey study with 22 industry participants from across Canada to investigate how businesses in Canada make decisions to adopt novel technologies and how new privacy regulations impact their business processes. Informed by the results of our analysis, we make recommendations for industry, researchers, and policymakers on how to support what each of them seeks from the other when attempting to improve digital privacy protections. By advancing our understanding of what challenges the industry faces, we increase the effectiveness of future privacy research that aims to help overcome these issues.
Borja García de Soto, I. Agustí-Juan, S. Joss
et al.
Abstract The counterpart of Industry 4.0 in the AEC/FM industry is known as Construction 4.0. Its essence is the digitalization and automation of the AEC/FM industry. As robots and other technologies make their way into the different phases of the lifecycle of construction projects, the concern about the future of jobs and wages will increase. While the use of robotics has the potential to improve productivity and safety, it should not necessarily reduce total employment in the construction sector in the long run. It is expected that existing roles will evolve, and new roles will be created (e.g., in addition to designers there will be a need for employees with digital skills). Focusing on the construction phase of a robotically built concrete wall, the different roles were evaluated. From this study, it was found that there will be a time in which conventional construction and robotic technologies will coexist, leading to a higher job variability and new roles, both at the managerial and operations/execution levels. Although this study is not meant to be an exact representation of how the AEC/FM roles will change as a consequence of Construction 4.0, it opens the debate and research in this area.
The statistic of accidents at construction sites give us a picture that Malaysian construction industry is one of the critical sectors that need a huge and fast overhaul from the current site safety practices. Accident don’t just happen, they are caused by unsafe acts, unsafe conditions or both. Most accidents result from a combination of contributing causes and one or more unsafe acts and unsafe condition. In order to improve the overall safety performance we need to investigate the root causes of construction accidents. That knowledge could be utilised in formulating more conducive working conditions and environments at construction sites. Therefore, a study has been conducted to identify the causes of accident at construction sites. This study was started out by reviewing literature from journals, books and web pages. Then reported accidents cases kept by the Department of Occupational Safety and Health Malaysia (DOSH) were examined to investigate causes of accidents. Surveys using questionnaire forms were carried out to obtain data from respondents who are mainly contractors and the rest are developers and consultants firms all around countries that are well versed with the construction safety. The finding of this study reveals that accidents are the result of many contributing factors, causes, and sub causes. Some of the critical factors are unsafe method, human element, unsafe equipment, job site conditions, management, and unique nature of the industry. The causes of accidents in Malaysia were found to be similar to that mentioned in literature review. However, some of the causes are low in frequency of occurrence. The main cause of construction accidents found are the workers’ negligence, failure of workers to obey work procedures, work at high elevation, operating equipment without safety devices, poor site management, harsh work operation, low knowledge and skill level of workers, failure to use personal protective equipments and poor workers attitude about safety.
Abstract Construction is one of the least-digitized industries in the economy. To rein in the rising costs of building activities, digital transformation is one of the pillars that industry leaders rely on. A case in point are logistics processes which are characterized by very limited visibility and inefficient organization. To progress beyond this current state of the art, we conceptualize the idea of a lightweight digital twin for non-high-tech industries. In collaboration with a leading supplier of building materials, we explore the opportunities offered by digital silo twin capabilities. Focusing on fill level monitoring we identify diverse opportunities for generating informational, automational and transformational business value. Leveraging new information sources for the redesign of core business processes drastically increases the complexity of operational decision-making. To tap into these opportunities, we design and implement a decision support system for silo dispatch and replenishment activity.
The construction industry plays a significant role in a country's economic growth. However, construction projects frequently face challenges in meeting schedule targets. Project scheduling is crucial as it provides insights into enhancing the development process. Various scheduling methods are employed in construction projects, including the line of balance (LoB) method and precedence diagramming. Selecting the appropriate scheduling method impacts the project's timeline. This research aims to analyze the effectiveness of the LoB method and precedence diagramming in building construction projects, with a specific case study of a COVID center. The research method involves collecting project data, including project s-curves. The findings indicate that utilizing both the line of balance and precedence diagram methods can enhance project scheduling by ensuring the continuous allocation of resources.
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm.
Cyber attacks leveraging or targeting the software supply chain, such as the SolarWinds and the Log4j incidents, affected thousands of businesses and their customers, drawing attention from both industry and government stakeholders. To foster open dialogue, facilitate mutual sharing, and discuss shared challenges encountered by stakeholders in securing their software supply chain, researchers from the NSF-supported Secure Software Supply Chain Center (S3C2) organize Secure Supply Chain Summits with stakeholders. This paper summarizes the Industry Secure Supply Chain Summit held on November 16, 2023, which consisted of \panels{} panel discussions with a diverse set of \participants{} practitioners from the industry. The individual panels were framed with open-ended questions and included the topics of Software Bills of Materials (SBOMs), vulnerable dependencies, malicious commits, build and deploy infrastructure, reducing entire classes of vulnerabilities at scale, and supporting a company culture conductive to securing the software supply chain. The goal of this summit was to enable open discussions, mutual sharing, and shedding light on common challenges that industry practitioners with practical experience face when securing their software supply chain.
Mohammed Hussein Manhal, Abbas Al-khalidi , Zaina Mustafa Mahmoud Hamad
The strategic network of any organization plays a significant role in the industry. Therefore, companies must study the factors hindering the construction of this network. Companies need a strategic network of alliances and partnerships to complement each other and constitute a superpower that competitors cannot overcome. This study explores the size of obstacles posed by managerial myopia in weakening the ability of organizations to build their strategic network. Current paper tests the influential relationship between managerial myopia and the ability of organizations to build their strategic network in one of the most important institutions within the oil sector. Results show a negative impact of managerial short-sightedness on an organization's ability to build a successful strategic network that enables it to coexist within an atmosphere of competition. This study recommends that organizations adopt the concept of managerial hyperopia as a valuable tool for organizational success.
The manufacturing industry sector was expected to generate new employment opportunities and take on labour. Gradually, however, it emerged as a menace to the sustenance of its workers. According to the findings of this study, 24 manufacturing subsectors with ISIC 2 digits in Indonesia exhibited regressive and abnormal patterns in the period 2012-2020. This suggests that, to a great extent, labour absorption has been limited and, in some cases, even shown a decline. Anomalous occurrences were observed in three subsectors: ISIC 12 (tobacco products), ISIC 26 (computer, electronic and optical products), and ISIC 31 (furniture). In contrast, regressive phenomena were present in the remaining 21 ISIC subsectors. Furthermore, the manufacturing industry displayed a negative correlation between employment and efficiency index, demonstrating this anomalous and regressive phenomenon. This implies that as the efficiency index of the manufacturing industry increases, the index of labour absorption decreases
This report characterizes the data center industry and its importance for AI development. Data centers are industrial facilities that efficiently provide compute at scale and thus constitute the engine rooms of today's digital economy. As large-scale AI training and inference become increasingly computationally expensive, they are dominantly executed from this designated infrastructure. Key features of data centers include large-scale compute clusters that require extensive cooling and consume large amounts of power, the need for fast connectivity both within the data center and to the internet, and an emphasis on security and reliability. The global industry is valued at approximately $250B and is expected to double over the next seven years. There are likely about 500 large (above 10 MW) data centers globally, with the US, Europe, and China constituting the most important markets. The report further covers important actors, business models, main inputs, and typical locations of data centers.
Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future APT defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry: (1) overlooking client-side overhead; (2) imbalanced alarm triage cost and interpretation cost; and (3) excessive server-side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry.
Thanks to rapid technological developments, new innovative solutions and practical applications of the Industrial Internet of Things (IIoT) are being created, upgrading the structures of many industrial enterprises. IIoT brings the physical and digital environment together with minimal human intervention and profoundly transforms the economy and modern business. Data flowing through IIoT feed artificial intelligence tools, which perform intelligent functions such as performance tuning of interconnected machines, error correction, and preventive maintenance. However, IIoT deployments are vulnerable to sophisticated security threats at various levels of the connectivity and communications infrastructure they incorporate. The complex and often heterogeneous nature of chaotic IIoT infrastructures means that availability, confidentiality and integrity are difficult to guarantee. This can lead to potential mistrust of network operations, concerns about privacy breaches or loss of vital personal data and sensitive information of network end-users. This paper examines the privacy requirements of an IIoT ecosystem in industry standards. Specifically, it describes the industry privacy dimensions of the protection of natural persons through the processing of personal data by competent authorities for the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties. In addition, it presents an overview of the state-of-the-art methodologies and solutions for industrial privacy threats. Finally, it analyses the privacy requirements and suggestions for an ideal secure and private IIoT environment.