bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sensor-based Pavement layer change detection using Long-Short Term Memory (LSTM)
Authors: Patel Tirth , Brian H.W. , Guo Yang Zou , Van Der Walt Jacobus Daniel , Li Yu 
Published: December 2022

Abstract
During construction, pavement projects often suffer from a lack of progress certainty, which leads to cost and time overruns. The pavement construction progress should be monitored in a timely and accurate manner to provide prompt feedback and ensure project success. However, current pavement construction progress monitoring practices (e.g., data collection, processing and analysis) are manual, time-consuming, tedious, inconsistent, subjective and error-prone. The previous research study was limited to only incremental road construction progress measurement. This preliminary study proposes a novel sensor-based method to identify pavement layer changes during construction using a time series algorithm for the approach development of automated as-built measurement of road construction. In this study, data were collected from generating various road construction scenarios in a controlled environment by simulating layer changes using a ground vehicle equipped with a laser ToF (time-of-flight) distance-ranging sensor. Subsequently, Long Short Term Memory (LSTM) was utilized on collected data for feature detection as ‘layer up’, ‘layer down’ and ‘layer not changed’ to classify road layer change. The experimental result demonstrates 84.91% as a promising overall average accuracy of road layer change classification on the control environment data, confirming the potential implementation suitability to detect pavement layers in real pavement construction projects. However, low-performance measures (low precision, recall and F1 score) of layer up and layer down suggest further improvement to enhance the robustness of the proposed model. This method can be extended to automate pavement construction progress monitoring by validating the proposed approach in a real case.
Innovation profile of services offered by Brazilian construction startups
Authors: Betiatto P , Oviedo-Haito R J J 
Published: December 2022

Abstract
Construction startups offer to optimize services in construction through innovation, by raising the levels of productivity and quality through new technologies or their application. In Brazil these companies are known as Construtechs or Proptechs, but publications provide no details on the type of innovations they offer in their services. Thus, this paper provides an innovation profile of services offered by Brazilian construction startups. To this end, a document and bibliographic review was conducted, where data was gathered initially based on a list published by a Brazilian venture capital company in 2020. This was then expanded by extracting information from the websites of 257 firms on the list. From the information available on 222 Construtechs, results shows that most of the companies offer organization and management services (36%), through digital platforms that improve the flow of information (75%) and innovate the way services are performed in the client companies, that is, the process (73%). In addition, it was identified that Brazilian Construtechs mostly offer services at the basic technological level and that only 33 companies (15%) are related to Construction 4.0. This paper provides a profile of Brazilian Construtechs and their service innovation opportunities, mainly associated with Construction 4.0.
Building our future and the future of building: Challenges from an organizational-cultural viewpoint
Authors: Tijhuis Wilco 
Published: December 2022

Abstract
Since the world was shaken because of the globally spread Covid-19 Pandemic, starting in late 2019/early 2020, many societies, including their businesses and sociological/behavioral systems, seem to be -and have been- influenced and -moreover- even been “shaken on their foundations”. All around the world such related daily experiences have lead to reconsidering for example the way we live, how we work and how we relax.
Nevertheless, when focusing more into detail on the construction industry and their outcomes (buildings, infrastructure, etc.) it is challenging to see if the way the construction industry was organized still might be the way how it will be kept organized after the COVID-19 Pandemic; especially because it seems that the influence of the pandemic on daily lives and businesses is now gradually decreasing; at least in the western developed world…
This discussion-paper aims to analyse and reflect on construction processes, derived from experiences and practices within case-studies, related to the project-development of buildings, from an organizational-cultural viewpoint. And this is especially important not only because it has been proven that the “soft factors” are maybe even more important than the “hard” factors in construction industry, but -moreover- because construction is a people’s business....and it will stay so, despite -or even thanks to- increasingly “data-driven” (i.e. behavioral) developments. Conclusions and recommendations are focusing on the future of building, being part of the construction activities that are used and/or needed to keep building our future.
Automated Regulatory Compliance towards Quality Assurance in Healthcare Building Projects
Authors: Soliman-Junior J , Tzortzopoulos P , Kagioglou M 
Published: December 2022

Abstract
Healthcare building projects are severely constrained by their associated regulatory frameworks. In this context, regulatory requirements define a basic outline upon which design is developed, as well as aid designers towards compliance to minimum standards. Automation has been explored by existing research focusing mostly on compliance checking (i.e., quality control). There has been limited developments within this domain related to quality assurance. This paper aims to highlight what are key needed improvements to enable the use of automation to promote quality assurance for regulatory compliance in healthcare building projects. For this purpose, an ongoing revision of a British healthcare design guidance document (HBN 11-01) was analysed according to a requirements’ taxonomy. Key areas of improvement needs were highlighted based on a series of interviews. Our main findings relate to identifying that despite the guidance character of regulatory documents in the UK, they are rarely used for this purpose, revealing the focus on quality control. In this context, the regulatory framework could be repositioned as a catalyst towards automated design quality assurance as long as (i) the regulatory documents are developed and revised to fit automated design processes; and (ii) there are compatible software developments to streamline design processes through automation.
Enhancing productivity in construction with co-existence of different definitions
Authors: Ofori G , Ling F Y Y , Zhang Z 
Published: December 2022

Abstract
Productivity has long been considered highly important in Singapore. In 2015, a national strategy considering skills, innovation and productivity as the basis for sustaining economic growth involving all sectors, was replaced with one seeking to move the economy from a “value-adding” to a “value-creating” one. In Singapore, construction productivity is mainly measured by: value added per person employed; and m2 of floor area completed per manday. By the economic measure, construction usually has the lowest annual growth rate among sectors, but the physical measure shows consistent improvement. This discrepancy has led to debate on the appropriate measure. The construction productivity programme in Singapore has included: promoting mechanisation, standardisation and prefabrication; training; procurement arrangements; regulatory requirements; and application of information and communication technology. Part of the findings of a study commissioned by two trade associations is presented. The aim was to derive findings which would help construction companies to improve productivity, and government to devise better policies and programmes. The study is based on interviews and focus group discussions with senior practitioners in Singapore. The questions included: level of construction productivity; how it is measured; firms’ policies on productivity; obstacles to, and enablers of, productivity measurement and improvement; the productivity development programme; and how productivity could be improved. The study found that construction firms could systematically develop their productivity-enhancing capabilities. It proposed measures for government and industry to improve construction productivity performance.
Digital Twin applications using the SIMULTAN data model and Python
Authors: Bühler M , Steiner B , Bednar T 
Published: December 2022

Abstract
Python is an open, general-purpose programming language that is used in many tools, libraries and APIs for Building Performance Simulations (BPS). Advantages of Python in the context of digital twins are the simple and powerful capabilities to generate input files, automate processes, import libraries in many languages and a large number of useful modules. However, in order to use BPS tools and libraries with real time data, a comprehensive data model is required in which all necessary data such as geometry, system engineering, databases, sensors, or simulation parameters for the different BPS are defined. Python in combination with SIMULTAN as a suitable open Building Information Modelling (BIM) data model allows an effective use of these tools and libraries to perform and automate analyses. This paper presents a Python module that integrates the SIMULTAN model in Python and enables almost seamless integration with minor adaptations to existing tools or modules. The import is achieved using simple text-based templates for the data types and their mapping in the data model. The data model, the definition of the data types and the use of this module is demonstrated by calculating the trend of the CO2 concentration in a zone of a digital twin using real time data.
Deep learning-based text detection on architectural floor plan images
Authors: Schönfelder P , König M 
Published: December 2022

Abstract
Architectural drawings are an important source of information for many construction-related tasks, as they contain geometric and semantic information about building parts. However, the manual extraction of room stamps and the insertion of such gleaned information into facility management systems is quite laborious and, thus, its automation is anticipated. In this paper, a method is proposed to detect and classify obscure or illegible text elements on legacy 2D architectural drawings of possibly poor quality. In contrast to existing approaches, a deep learning model is specifically trained for the task at hand rather than making use of transfer learning approaches. The resulting text snippets can be further processed with natural language processing tools to be fed into a facility management system automatically. Other conceivable applications include the extraction of drawing header information, material type or any additional text given on the drawing, to facilitate the enrichment of digital twins of existing structures with semantic data. To provide training data, two floor plan datasets are annotated in a consistent manner. The influence of different data augmentation techniques is investigated systematically. With regard to performance and efficiency, the presented method is compared to alternative tools for the task at hand.
Data augmentation for deep learning-based detection of pump anomalous conditions
Authors: Hallaji S M , Fang Y , Winfrey B K 
Published: December 2022

AbstractAdopting effective asset maintenance approaches is critical in enhancing the longevity and cost-effectiveness of assets in civil infrastructure. Pumps are a crucial asset in many civil infrastructures such as wastewater treatment plants. Data-driven predictive maintenance (PdM) is an emerging asset maintenance method that diagnoses asset conditions proactively. However, the current PdM of pumping assets still requires extensive expert knowledge for finding robust feature extraction methods before applying machine learning methods. This is a significant barrier to the automation and robustness of the PdM of pumps. Deep learning-based algorithms offer the potential to address these issues by capturing data features in monitoring data and performing incremental learning of features without human interventions. To train an analytical model for accurate condition assessment, these methods require a great deal of training data, which is not often available due to time and cost limitations. This research aims to address the scarcity of training data by proposing a novel data augmentation method. The proposed approach consists of a signal-to-image data conversion method and multiple image augmentation methods. The LeNet-5 architecture was employed to produce the CNN model. The performance of the model was evaluated using a public data set. It was shown that the proposed augmentation method significantly enhances the validation accuracy and model generalisability.
The Application of the Best Value Approach in Janitorial Services
Authors: Kashiwagi Jacob , Kashiwagi Isaac , Scott Valerie , Mellecker Tyler 
Published: December 2022

Abstract
A problem facing the construction industry is how to reduce cost and increase value on projects. The Performance Based Studies Research Group (PBSRG) identified, through the Industry Structure (IS) model, that the problem is being caused by the “client centric” approach where the client is the expert and uses the price-based approach. The IS model identifies that if a client uses a Request for Proposal and identifies and utilizes vendor expertise, they can increase performance and lower cost on projects. This is the Best Value Approach (BVA). A city in Colorado used the BVA to deliver a requirement for janitorial services. Their budget was lower than their current expenses. The City agreed to utilize the BVA to attempt to cut cost and increase the level of service. This paper covers their purchasing effort to identify and utilize janitorial services. The paper identifies if the BVA was able to increase the janitorial service while lowering the cost.
The Impact of Changing Project Organization Structure on Project Performance
Authors: Sarhan Sarhan , Dulaimi Mohammed 
Published: December 2022

Abstract
Instigating changes to project organization structure during the project execution could disrupt many project processes and influence project performance. The purpose of this study is to investigate a particular problem occurred in completed construction projects in UAE market. The problem appeared due to unnecessary changes and slow or inadequate response to change by senior managers in contractor’s project organization structure. These changes had negative impact on the project management roles and responsibilities, additional cost and time impact. Furthermore, there were negative consequences on the performance of the project’s team and their effort to create new ideas as well as their innovation inside the construction project that they are involved. The qualitative methodology is used in this study through investigating five case studies for construction projects that completed in UAE market along with extensive literature review that focused on the different sides of the problem as well as an interview with ten members of contractor’s project management and senior managers to examine the impact of the problem on project performance and team effectiveness. The main findings indicate that having clear and defined roles and responsibilities have positive impact on project performance and team effectiveness. Moreover, the contractor’s senior managers have a vital role in establishing, managing the project organization structure and ensuring that any changes to the structure are dealt with effectively. This would enable the project to progress according to plan, reduce project activity reworks and address any weaking communication and cohesiveness between project team members.
Regenerative-based green supply chain management model for the construction industry
Authors: Oyefusi O N , Enegbuma W I , Brown A , Zari M P 
Published: December 2022

Abstract
Green Supply Chain Management (GSCM) is considered to be the most effective management tool that aims to integrate environmental sustainability thinking into the built environment. This is in relation to its ability to substantially reduce greenhouse emissions, energy consumption, pollution, and other drivers of negative environmental change in the built environment. Despite the implementation of GSCM practices, climate change continues to occur with a steady rise in greenhouse gas emissions per year that are associated with the built environment. Hence, there is a need for a proactive regenerative approach that focuses not only on reducing negative environmental footprints but also aims to create net positive impacts on the environment. This study aims to address this issue by first identifying the dominant forms of construction GSCM practices and their performance limitations through a Systematic Literature Review (SLR). The result revealed that regenerative factors were lacking in current GSCM practices. To further explore the nature of expanding current GSCM practices, a regenerative-based GSCM model was developed that demonstrates the relationship between regenerative and GSCM practices. This research addresses the gaps in current GSCM practices which could serve as a strategic response to climate change in terms of both mitigation and adaptation responses.
Research on the plane intelligent design of homestays based on deep learning
Authors: Gao Xiaoni , Guo Xiangmin , Lo Tiantian 
Published: December 2022

Abstract
The design of the homestay exposed the problems of single-function layout and poor living experience. Therefore, the purpose of this article is to efficiently grasp the design requirements of the country house and to construct an algorithm for automatically generating the floor plan of the house to improve the design output efficiency. This paper takes the homestays surrounding the Paifang Street scenic spot in Chaozhou, Guangdong Province as an example, crawls the comment data of tourism websites through Python, classifies and constructs a portrait of the living needs of the country homes, analyzes the current pain points and opportunities of the country homes, and generates the subsequent plane personalized Plan the data research work; then combine the preliminary research and field research to put forward the principles of the graphic design of the homestay. The paper proposes a method of the automatic layout of architectural functions based on a generative confrontation network, which can automatically layout the architectural functions of rooms according to the design requirements of different homestays, forming a more complete floor plan.
Exploring the thermal conditions in construction sites through IoT based methodologies
Authors: Edirisinghe R , Andamon M M , Ghanem A 
Published: December 2022

Abstract
Despite improvements in reducing the rate of injuries to construction workers, the construction industry remains a high-risk industry. Working conditions in construction sites frequently pose heat hazards and presents uncontrolled heat risks. Technological solutions that generate early warnings have the potential to improve health and safety. However, little research has been done in the construction industry. The Smart Safety Vest system is proposed as an ‘internet of things’ (IoT) solution in construction sites. The system development has followed a series of technology readiness levels and construction site testing. This paper reports on the early preliminary data analysis on the validity and usability of the system in actual construction sites. Real-time monitoring of constructions sites via portable weather stations were conducted. Specifically, this paper presents the evaluation of the thermal conditions of four residential construction project sites. The environmental data collected in four residential construction project sites are quantitatively analysed which set the context for the further reporting of analysis of the smart vest data from construction workers. The initial results obtained during extremely hot summer conditions are used to derive recommendations. It presents the viability of an integral part of the real-time monitored future pervasive construction sites.
Teaching of emerging technology in construction education
Authors: Debs L , Hubbard B , Zimpfer M 
Published: December 2022

Abstract
As technology becomes ubiquitous in the architecture, engineering and construction (AEC) industry, there is a need to evaluate how construction education is incorporating technology in the undergraduate curriculum. Given recent trends, we are especially interested in emerging technologies that facilitate AEC integration. In the first phase of the study, a scoping review of literature was used to identify publications related to eight emerging construction-related technology types. Following, the second phase focused on how these emerging technologies were taught in construction education. Of the 58 papers identified in the second phase, the overwhelming majoring discussed technology as pedagogical aid to improve visualization, while only 10 focused on instruction about the technology itself. Some technology types did not have any education specific publications identified by the researchers. Emerging themes from the data also suggest alignments between drones and surveying, and robotics and programming and computational thinking. A majority of the papers mention several technology types and Building Information Modelling (BIM), suggesting an integrated view of emerging construction technology. We conclude the review by suggesting the need for more publications related to teaching of new technology, so that instructors can share best practices in integrating them into the construction curriculum.
IFC-based linking of the risk management process using a building data model
Authors: Bartels N , Eilers M , Pütz C , Meins-Becker A 
Published: December 2022

Abstract
A vital element in working with BIM are standardised exchange formats that enable the exchange of information from digital building models between different software solutions and project participants. In this context, the Industry Foundation Classes (IFC) defined in DIN EN ISO 16739 represent a central standard for implementing the open exchange of information. Although approaches for integrating risk management are already available in IFC, they do not sufficiently reflect the needs of the construction industry. In order to increase project quality through risk management and the universal application of the Building Information Modelling (BIM) method, it is essential to map the generally valid information on the risk management process in IFC. The following article thus presents starting points for the further integration of risk management in IFC. The aim is to link all relevant risk information in a digital building model through an analysis and the development of an approach.