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In construction projects, underpayments can be recognised as one of the significant drawbacks that impact the success of a project. Research into underpayments is considered ambiguous and provides a limited reflection of the issue, which makes it complicated to trace how it originates in the first place. This study aims to examine the causes that lead to underpayments and develop a holistic synthesis of underpayments for subcontractors in the lifecycle of a construction project.

An open-ended and closed-ended questionnaire was used to collect the data using purposeful sampling with 28 construction stakeholders who ranged from main contractors, subcontractors and others (Small medium enterprises SMEs, Consultancies, Clients etc.). Data collected was analysed to trace drivers and the impact of underpayment and suggested mitigation strategies to be identified whilst viewing the perspectives of a main contractor and subcontractor.

The findings show that the most prominent driver for underpayments is variation disputes followed by cash flow. The research also suggests mitigation strategies such as collaborative working, more robust budget control and early identification of risks as potential remedies to overcome the underpayment issue. The research concludes with a framework that elicits the complexity underlying underpayments for subcontractors in construction projects.

The research evolves the understanding that underpayment is a complex phenomenon, relying heavily on the data/information exchange mechanism between the main contractor and subcontractors. This research provokes the need to understand underpayment further so it can be mitigated.

Previous studies have established to a great extent that regulatory frameworks and, in particular, procurement approaches – that are common in a particular context – have a major impact on the success of building information modelling (BIM) implementation in construction projects. Despite the close links between these two concepts, research on the effect of procurement approaches on BIM implementation is scarce. To address this gap, this paper aims to investigate the barriers that affect BIM implementation through the lens of procurement approaches.

A mixed-method approach was adopted using a questionnaire survey (n = 116) and interviews with key stakeholders (n = 12) in Jordan. The outcomes of the quantitative parts were augmented with findings from interviews.

It was revealed that the deployment of unfavourable construction procurement approaches represents a major hurdle towards BIM implementation. Though essential for enhancing BIM implementation, it is revealed that a fundamental change from the common design-bid-build (DBB) to more collaborative procurement approaches remains infeasible in view of the realities that govern the construction industry.

It was revealed the deployment of unfavourable construction procurement approaches represents a major hurdle towards BIM implementation. Though essential for enhancing BIM implementation, it is revealed that a fundamental change from the common DBB to more collaborative procurement approaches remains infeasible given the realities that govern the construction industry.

As the first of its kind, a set of recommendations for establishing supportive, workable procurement that does not deviate significantly from common procedures and practices is presented. Rather than advocating a shift to procurement approaches that are aligned with BIM, the findings offer novel insight into the necessity of developing a framework within the boundaries of the current and widely adopted procurement approaches to address the identified construction procurement issues and facilitate BIM implementation.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

While the COVID-19 pandemic has impacted the construction industry, it is still unclear from prior studies about adequately positioning the quality assurance (QA) for the post-pandemic era and future pandemics, especially cross-border construction logistics and supply chain (Cb-CLSC). Thus, this study aims to develop a managerial framework to position the QA of Cb-CLSC during pandemics and post-pandemics by taking lessons from how COVID-19 has impacted the existing QA systems and has been managed successfully.

This is achieved pragmatically through an embedded mixed-method design involving a literature review, survey and interview from experts within the Hong Kong SAR–Mainland China links, typically known as the world’s factory. The design is further integrated with the partial least squares structural equation modelling (PLS-SEM) approach.

The study revealed 10 critical managerial practices (MPs) to position the QA to be adequate for the post-pandemic and during future pandemics, with the top three including “strict observance of government regulations (MP1)”, “planning ahead the period of quality assurance with the quarantine days in host countries (MP6)” and “modification of contract to cater for uncertainties (MP4)”. This attained a relatively good percentage agreement of 53% between the industry and academia. However, the top four MPs regarded as very effective include “implementing digital collaborative inspections with subcontractors and trades (MP8)”, “implementing a digital centralized document and issue management system (MP7)”, “strict observance to government regulations, including vaccination of workers, social distancing, use of prescribed nose masks, etc. (MP1)” and “planning ahead the period of quality assurance with the quarantine days in host countries (MP6)”. Two underlying components of the MPs were revealed as policy-process (PP)-related practices and people-technology-process (PTP)-related practices, and these can be modelled into a managerial framework capable of effectively positioning the QA to be adequate during pandemics through to the post-pandemic era.

The findings of this study depicted significant theoretical and practical contributions to the proactive management of QA activities during pandemics through to the post-pandemic era. It could empower organisations to pay attention to smartly and innovatively balancing people, processes, pandemic policy and technology to inform decisions to effectively position the QA for the post-pandemic era and survive the risks of future pandemics.

The study contributes to the body of knowledge in that it develops a managerial framework to position the QA of Cb-CLSC during pandemics and post-pandemics by taking lessons from how COVID-19 has impacted the existing QA systems and has been managed successfully. It is original research with invaluable primary data in the form of surveys and interviews from experts within the Hong Kong SAR–Mainland China links, typically known as the world’s factory.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

The oil and gas industry form the main resource of economy in Iraq and constructing any project in such sectors requires a huge amount of expenses due to the unique requirements that oil and gas facilities required in such projects. Therefore, adopting an appropriate technological approach such as building information modeling (BIM) which is unfortunately not adopted yet in Iraq is essential to successfully deliver these projects. Thus, this paper aimed to introduce BIM to Iraq through Basra Oil Company (BOC) which is one of the biggest public oil and gas companies in Iraq.

The related literature of journals articles, conference proceedings and published reports have been reviewed. As a result, firstly: a hypothesis has been derived that is “If Basra Oil Company (BOC) adopts and applies BIM approach instead of the 2D approach currently used to manage its projects, the company can overcome several constraints in managing its projects that associated with such 2D traditional approach”; secondly: homogenous, consistence and reliable web-based questionnaire has been designed as its Cronbach’s alpha equal to 0.897 and 0.711 for BIM benefits and barriers, respectively. This questionnaire distributed to the BOC related professionals to test such hypothesis by investigating their readiness and accepting of BIM approach and to rank BIM barriers based on five-point Likert scale.

Based on the analysis using IBM SPSS Statistics 26 of 115 responses, almost 50% of the respondents had experience 11–15 years, while 22.6% had experience more than 15 years in oil and gas industry construction projects. Those participants were from diverse engineering majors that are: 4.3% Architectural Engineers, 31.3% Civil Engineers, 20% Mechanical Engineers, 22.6% Electrical Engineers and 21.7% from other engineering majors. The respondents’ departments demography was 16.5% of design department, 12.2% of construction department, 20.9% of Project Management Department, 12.2% of Maintenance department, 4.3% of HSE Department, 13% of Production Department and 20.9% of “Other Department.” The study resulted in 1: accepting BIM approach to be an alternative of current 2D-traditional approach used by the company to manage and construct its projects, since mean of collected data is (4.4332), Kruskal–Wallis H test significance values were 0.398 and 0.372; and ANOVA test significance values were 0.433 and 0.599 among Engineering Majors groups and Company’s Department groups, respectively. 2: Disclosed and sequenced BIM barriers in the company based on their criticality. 3: verifying reliably how BIM attributes are important to oil and gas construction projects in Iraq, 4: the company top management and company policies are the most critical potential factors to hinder or adopt and implement BIM in the company, 5: while cost is not seen a critical barrier to implement BIM in the oil and gas sector.

The limitation of this study is the excluding of decision makers of BOC, thus more profound future studies need to be conducted where top management and decision makers are involved, particularly the present study demonstrated that support of company top management is the most critical factor which can help the company to adopt (BIM).

The study concludes that BIM approach is valuable for managing projects in oil and gas sector in Iraq and identify the originality in output by using the research method. This noble study provides a leverage for enhanced research to adopt and implement building information modeling (BIM) in Iraq as the study originally demonstrates benefits and identifies the critical barriers in BIM implementation to push the boundaries toward adopt Digitalization and reduce CO2 emission in Iraqi oil and gas sector. The study can be used as evidence and platform to encourage professionals and practitioners to present more sophisticated tools of BIM in the oil and gas industry, especially for facility and operation management. These findings achieved via oil and gas experts, and it is first time to achieve such findings from a case study in Iraqi oil and gas sector.

Practitioners have reported a minimal and non-use of building information modelling (BIM), especially in small and medium-sized organisations and BIM infant construction industries. This development calls for a reappraisal of organisations’ strength in capabilities required for BIM uptake towards the target of global construction digitalisation. This study aims to assess the BIM Level 2 uptake capability of organisations in a BIM infant construction industry and identify the underlying interactions between the capability criteria.

The study used a multivariable analysis of fifteen descriptors identified from the people, process, policy, finance and technology domain. Data collection was done in the BIM infant construction industry in Nigeria. Verification of the descriptors and an evaluation of BIM uptake capability in organisations was done. Seventy-three responses were received within the selected context, and data analysis was done with mean weighting and exploratory factor analysis. Maximum Likelihood extraction and Direct Oblimin rotation were used.

Factor analysis revealed three factors that explained 53.28% of the total variance in the BIM Level 2 uptake capability of construction organisations. The factors are workforce capacity and continuous development, an affinity for innovation and strength in physical and operational facilities.

This study provides an overarching and insightful discussion on BIM uptake capability and construction digitalisation with evidence from a BIM-infant construction industry.

The findings of this study are a piece of valuable empirical evidence on Level 2 BIM uptake capability. This empirical situation analysis will inform the advocacy for the advancement of BIM and enhanced utilisation of building information. Evidence on the capability performance of the BIM infant industry has been revealed.

The outcome is expected to stir debate on the preparedness of organisations to further exploit the benefits of BIM in the BIM infant construction industry. Examination of the capability for a particular phase of BIM is scanty in the literature.

The construction industry is a primary contributor to the development of emerging economies such as the Kurdistan Region of Iraq. However, the sector is underperforming, and products are not meeting expectations. A lack of collaboration is considered a significant contributor to these issues. Various researchers have identified factors to improve collaborative approaches. However, there is still a lack of clear frameworks to help implement collaboration in the construction industry, especially in emerging economies. Therefore, this study aims to develop a framework to implement collaboration in the construction industry.

This article utilises a review of literature, questionnaire and interviews with experts in the construction industry in order to develop a framework to achieve collaboration in construction projects.

The research presents a framework that distributes the factors of collaboration over the project lifecycle stages in accordance with the Royal Institute of British Architects (RIBA) Plan of Work 2007. Each factor is divided into a set of enabling conditions which must be satisfied to ensure that the given specific factors are delivered. Additionally, the framework suggests appointing a collaboration champion at the beginning of the project to manage the process.

The research contributes to scarce literature about collaboration practices in the Kurdistan Region and in emerging economies in general.

Gourd fibres (GF) are a natural biodegradable fibre material with excellent mechanical properties and high tensile strength. The use of natural fibres in composite materials has gained popularity in recent years due to their various advantages, including renewability, low cost, low density and biodegradability. Gourd fibre is one such natural fibre that has been identified as a potential reinforcement material for composites. However, it has low surface energy and hydrophobic nature, which makes it difficult to bond with matrix materials such as polyester. To overcome this problem, chemically adapted gourd fibre has been proposed as a solution. Chemical treatment is one of the most widely used methods to improve the properties of natural fibres. This research evaluates the feasibility and effectiveness of incorporating chemically adapted gourd fibre into polyester composites for industrial fabrication. The purpose of this study is to examine the application of chemically modified GF in the production of polyester composite engineering materials.

This work aims to evaluate the effectiveness of chemically adapted gourd fibre in improving the adhesion of gourd fibre with polyester resin in composite fabrication by varying the GF from 5 to 20 wt.%. The study involves the preparation of chemically treated gourd fibre through surface modification using sodium hydroxide (NaOH), permanganate (KMnO₄) and acetic acid (CH₃COOH) coupling agents. The mechanical properties of the modified fibre and composites were investigated. It was then characterized using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) to determine the changes in surface morphology and functional groups.

FTIR characterization showed that NaOH treatment caused cellulose depolymerization and caused a significant increase in the hydroxyl and carboxyl groups, showing improved surface functional groups; KMnO₄ treatment oxidized the fibre surface and caused the formation of surface oxide groups; and acetic acid treatment induced changes that primarily affected the ester and hydroxyl groups. SEM study showed that NaOH treatment changed the surface morphology of the gourd fibre, introduced voids and reduced hydrophilic tendencies. The tensile strength of the modified gourd fibres increased progressively as the concentration of the modification chemicals increased compared to the untreated fibres.

This work presents the designed composite with density, mechanical properties and microstructure, showing remarkable improvements in the engineering properties. An 181.5% improvement in tensile strength and a 56.63% increase in flexural strength were got over that of the unreinforced polyester. The findings from this work will contribute to the understanding of the potential of chemically adapted gourd fibre as a reinforcement material for composites and provide insights into the development of sustainable composite materials.