Search results

Indoor hallways are the most common and indispensable part of people’s daily life, commercial and industrial activities. This paper aims to achieve high-precision and dense 3D reconstruction of the narrow and long indoor hallway and proposes a 3D, dense 3D reconstruction, indoor hallway, rotating LiDAR reconstruction system based on rotating LiDAR.

This paper develops an orthogonal biaxial rotating LiDAR sensing device for low texture and narrow structures in hallways, which can capture panoramic point clouds containing rich features. A discrete interval scanning method is proposed considering the characteristics of the indoor hallway environment and rotating LiDAR. Considering the error model of LiDAR, this paper proposes a confidence-based point cloud fusion method to improve reconstruction accuracy.

In two different indoor hallway environments, the 3D reconstruction system proposed in this paper can obtain high-precision and dense reconstruction models. Meanwhile, the confidence-based point cloud fusion algorithm has been proven to improve the accuracy of 3D reconstruction.

A 3D reconstruction system was designed to obtain a high-precision and dense indoor hallway environment model. A discrete interval scanning method suitable for rotating LiDAR and hallway environments was proposed. A confidence-based point cloud fusion algorithm was designed to improve the accuracy of LiDAR 3D reconstruction. The entire system showed satisfactory performance in experiments.

The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model.

The model is developed with flight velocity, ambient temperature, liquid water content, median volumetric diameter and icing time taken as inputs and icing thickness given as outputs. To enhance the icing prediction accuracy, the model involves geometrical constraints into the loss function. Then the model is trained according to icing samples of 2D NACA0012 airfoil acquired by numerical simulation.

The results show that the involvement of geometrical constraints effectively enhances the prediction accuracy of ice shape, by weakening the appearance of fluctuation features. After training, the airfoil icing prediction model can be used for quickly predicting airfoil icing.

This work involves geometrical constraints in airfoil icing prediction model. The proposed model has reasonable capability in the fast assessment of aircraft icing.

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.

This study aims to develop a modelling framework of housing supply dynamics within the context of urban microsimulation systems. Housing markets have witnessed substantial investigation over recent decades, predominantly concerning residential demand. However, comparatively limited attention has been directed towards comprehending the housing supply dynamics. Housing policy disconnects with the developers’ market behaviours, which leads to significant mismatch between the housing construction and affordable housing needs of the population. Research attention should be made in comprehending the residential construction market activities. To address this gap, this study developed an autoregressive distributed lag (ARDL) model and analyzed the temporal evolution of housing construction.

An ARDL model was developed to address the issue of temporal modelling of the housing supply. An empirical study was conducted in the Greater Toronto and Hamilton Area (GTHA) based on a longitudinal housing starts data set from 1998 to 2020. The model integrates diverse variables, including macroeconomic conditions, property development costs, dwelling prices and opportunity costs. Notably, the model captures both the path-dependent effects stemming from supply market fluctuations and the temporal lag effect of influential factors.

The findings reveal that the supply-side’s responsiveness to market condition alterations may span up to 18 months. The model has reasonable and satisfying performance in fitting the observed starts. The methodological foundations laid will facilitate future modelling of housing supply dynamics.

This study innovatively separated the modelling of housing supply within the context of urban microsimulation, into two parts, the modelling of housing starts and completion. The housing starts are determined in a complex and regressive process influenced by both the micro-economic environment and the construction cost and housing market trends. Through the temporal modelling method, this study captures how long it would take for the housing supply to respond to multiple factors and provides insight for urban planners in regulating the housing market and leveraging various policies to influence the housing supply.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

The traditional Chinese culture has always emphasized the authority of leaders and their “top-down” influence over subordinates tangled with “bottom-up” management. Paternalistic leadership can both nurture and restrict growth in mega-construction projects, due to the unique consequences (i.e. positive vs negative implications) for project teams. Hence, the present study aimed to explore the impact of paternalistic leadership (PL), team members’ voice (TMV) and team resilience (TR) on the mega-construction project success (MPS) in China's Belt and Road Initiative (BRI).

A surveyed-based sample of project professionals (N = 563) directly linked with the BRI was employed for statistical estimations with partial least squares (PLS) structural equation modeling (SEM).

Paternalistic leadership styles, including authoritarian leadership (AL), moral leadership (ML) and benevolent leadership (BL), significantly influence the mega-construction project success in BRI. The findings empirically validated that both BL and ML increase the likelihood of mega-construction project success. However, AL could impose a threat through its underlying negative influence. In addition, leaders with benevolence and morality have a positive influence on TMV and TR, while leaders with authoritarianism signal a negative impact. Furthermore, both TMV and TR significantly and positively mediate the relationships between AL-MPS (Model-1), BL-MPS (Model-2) and ML-MPS (Model-3), respectively.

The present study is a groundbreaking endeavor that fills a crucial research gap by investigating mega-construction project success in the BRI through paternalistic leadership, project team members' voice and team resilience in a multi-mediation model. These novel findings offer valuable strategic insights for managing mega-construction projects in countries with paternalistic solid cultural foundations, enabling project managers to navigate cultural nuances and optimize megaproject outcomes.

Construction organisations cannot underestimate the improvement in public–private partnership (PPP) projects’ implementation. At the same time, construction organisations cannot overlook the risk arising from engaging in PPP construction projects. Hence, this study aims to establish the influence of risk resource management (RRM) in managing PPP risk in the construction industry in Ghana.

The researchers adopted qualitative and quantitative research methods to achieve the aim of the study, in which Delphi questions and a close-ended questionnaire were developed. A total of 650 construction specialists, including procurement officers, consultants, project managers, quantity surveyors, site engineers and planning officers were chosen using random and purposive sampling techniques. Recovered data were analysed using descriptive statistics and confirmatory factor analysis (CFA). The CFA maximum likelihood estimation extractor compresses 19 variables into 3 pattern matrices.

The results of the study revealed three factors that measure RRM in Ghana’s PPP construction industry, including financial resource management which was influenced by communicating the budget to project team members and project partners understanding the budget, and material resource management which was influenced by the provision of materials transportation and provision of delivery programs and labour resource management which was impacted by a commitment to pay social security and taxes and provision of good salaries, to address RRM in PPP construction organisations.

To incessantly improve the PPP risk management (RM) in construction through RRM, there should be a strong liaison between the universities, government agencies and the construction industry, and such collaboration will assist the industry to obtain first-hand information regarding the study findings and how they can be implemented to help the development of RM in the construction industry. This study is limited to Ghana and CFA and further study should explore structural equation model to determine the structure and measurement model of the risk resource variables.

The study may be valuable to industry stakeholders looking for new approaches to improve RM in their construction activities, particularly in PPP projects. Also, to assist reduce PPP risk, construction companies should use RRM in their organisations.

Productivity increase is correlated with profitability, sustainability and competitiveness of the construction firms. Recent studies reveal that the primary causes of productivity decline are poor usage of scientific and technological advances, ineffective supervision strategies and poor apprenticeship facilities/opportunities. Accordingly, the purpose of this study was to evaluate how well construction supervisors can utilise fundamental science and technological concepts/ideas to increase the efficiency and productivity of construction activities.

A new strategic layout was designed with the use of potential training guide tools. Based on the designed layout, a new supervisory training programme was developed, and 62 construction supervisors were selected, trained and evaluated in line with six parts of competencies and the relevant learning domains. An assessment guide with different levels of descriptions and criteria was developed through literature analysis and expert interviews. The research tools were verified using comprehensive approaches.

The overall mean values of supervisors’ performance scores indicate proficient-level grades in the competency characteristics related to taking measurements, generating drawings/designs using manual techniques and computer-aided tools, involving Bill of Quantities (BOQ) preparations and preparing training plans/materials for improving the competencies of labourers on estimation, measurements and understanding drawings. Their proficiency was notably lower in the use of information and communication technology application tools in construction tasks compared to others. The findings point to a modern generalised guideline that establishes the ranges of supervisory attributes associated with science and technology-related applications.

The study outcomes produce conceptualised projections to restructure and revalue the job functions of various working categories by adding new definitions within the specified scope. This may result in constructive benefits to upgrading the current functions associated with urbanisation, sustainability and society. The implementation of the study’s findings/conclusions will have a significant impact on present and future practices in other developing nations and developing industries, even if they are directly applicable to the Sri Lankan construction industry.

Up to certain limits/stages, the study fills not only the knowledge gap in the field of creating protocols and application techniques connected to lifelong learning and skill enhancement/upgrading but also the existing gaps in work attributes and roles of construction supervisors associated with the utilisation of fundamental science and technological concepts/ideas towards reinforcing sustainable and productive site operations.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.