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This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

Centralized waiting lists (CWLs) for patient attachment to a primary care provider have been implemented across Canada, including Quebec. Little is known about the implementation of CWLs and the factors that influence implementation outcomes of such primary care innovations. The purpose of this paper is to explain variations in the outcomes of implementation by analyzing the characteristics of CWLs and contextual factors that influence their implementation.

A multiple qualitative case study was conducted. Four contrasting CWLs were purposefully selected: two relatively high-performing and two relatively low-performing cases with regard to process indicators. Data collected between 2015 and 2016 drew on three sources: 26 semi-structured interviews with key stakeholders, 22 documents and field notes. The Consolidated Framework for Implementation Research was used to identify, through a cross-case comparison of ratings, constructs that distinguish high from low-performing cases.

Five constructs distinguished high from low-performing cases: three related to the inner setting: network and communications; leadership engagement; available resources; one from innovation characteristics: adaptability with regard to registration, evaluation of priority and attachment to a family physician; and, one associated with process domain: engaging. Other constructs exerted influence on implementation (e.g. outer setting, individual characteristics), but did not distinguish high and low-performing cases.

This is the first in-depth analysis of CWL implementation. Results suggest important factors that might be useful in efforts to continuously improve implementation performance of CWLs and similar innovations.

The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method.

The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly.

The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable.

The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels.

The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud.

This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.

The assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.

An extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.

The models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).

All the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.

The accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.

To date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).

Secondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.

The findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.

The novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.

The wide application of metal material extrusion (MEX) has been hampered by the practicalities associated with the resulting shrinkage of the final parts when commercial three-dimensional (3D) printing equipment is used. The shrinkage behaviour of MEX metal parts is a very important aspect of the MEX metal production process, as the parts must be accurately oversized to compensate for shrinkage. This paper aims to investigate the influence of primary 3D printing parameters, namely, print speed, layer height and print angle, on the shrinkage behaviour of MEX Steel 316L parts.

Two groups of dog-bone and rectangular-shape specimens were produced with the BASF Ultrafuse Steel 316L metal filament. The length, width and thickness of the specimens were measured pre- and post-debinding and sintering to calculate the percentile shrinkage rates. Analysis of variance (ANOVA) was used to evaluate and rank the significance of each manufacturing parameter on shrinkage. Typical main print quality issues experienced in this analysis are also reported.

The shrinkage rates of the tested specimens ranged from 15.5 to 20.4% along the length and width axis and 18.5% to 23.1% along the thickness axis of the specimens. Layer height and raster angle were the most statistically significant parameters influencing shrinkage, while print speed had very little influence. Three types of defects were observed, including surface roughness, surface deformation (warping and distortion) and balling defects.

This paper bridges an existing gap in MEX Steel 316L literature, with a focus on the relationship between MEX manufacturing parameters and subsequent shrinkage behaviour. This study provides an in-depth analysis of the relationship between manufacturing parameters – layer height, raster angle and print speed and subsequent shrinkage behaviour, thereby providing further information on the relationship between the former and the latter.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

The metaverse is a virtual world where users can communicate with each other in a computer-generated environment. The use of metaverse technology has the potential to revolutionize the way businesses operate, interact with customers, and collaborate with employees. However, several obstacles must be addressed and overcome to ensure the successful implementation of metaverse technology. This study aims to examine the implementation of metaverse technology in the management of an organization's supply chain, with a focus on predicting potential barriers to provide suitable strategies.

Covariance-based structural equation modeling (CB-SEM) was used to test the model. In addition, artificial neural network modeling (ANN) was also performed.

The CB-SEM results revealed that a firm's technological limitations are among the most significant barriers to implementing metaverse technology in the supply chain management (SCM). The ANN results further highlighted that the firm's technological limitations are the most crucial input factors, followed by a lack of governance and standardization, integration challenges, poor diffusion through the network, traditional organizational culture, lack of stakeholder commitment, lack of collaboration and low perception of value by customers.

Because metaverse technology has the potential to provide organizations with a competitive advantage, increase productivity, improve customer experience and stimulate creativity, it is crucial to discuss and develop solutions to implementation challenges in the business world. Companies can position themselves for success in this fascinating and quickly changing technological landscape by conquering these challenges.

This study provides insights to metaverse technology developers and supply chain practitioners for successful implementation in SCM, as well as theoretical contributions for supply chain managers aiming to implement such environments.

The purpose of this paper was to assess the economic impact of investment in different animal welfare–enhancing flooring solutions in Swedish dairy farming.

The authors developed a bio-economic model and used stochastic partial budgeting approach to simulate the economic consequences of enhancing solid and slatted concrete floors with soft rubber covering.

The findings highlight that keeping herds on solid and slatted concrete floor surfaces with soft rubber coverings is a profitable solution, compared with keeping herds on solid and slatted concrete floors without a soft covering. The profit per cow when kept on a solid concrete floor with soft rubber covering increased by 13%–16% depending on the breed.

Promoting farm investments such as improvement in flooring solution, which have both economic and animal welfare incentives, is a potential way of promoting sustainable dairy production. Farmers may make investments in improved floors, resulting in enhanced animal welfare and economic outcomes necessary for sustaining dairy production.

This literature review indicated that the economic impact of investment in specific types of floor improvement solutions, investment costs and financial outcomes have received little attention. This study provides insights needed for a more informed decision-making process when selecting optimal flooring solutions for new and renovated barns that improve both animal welfare and ease the burden on farmers and public financial support.