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High-level labor waste is a major challenge in construction projects. This paper aims to identify, quantify and categorize labor waste in the context of Iranian housing construction projects.

This research uses a case study approach, with empirical data collected through direct observations and semi-structured interviews.

Having triangulated the findings from the literature review and empirical studies, a list of eight types of waste was derived for the thirteen observed laborers in ten case study projects. The empirical studies allowed the labor waste identified from the literature to be verified and refined by considering it in the context of the observed activities, and led to two new types of waste being identified which were not considered in the literature. Findings indicate that nearly 62% of laborers' time is spent on non-value-adding activities. It appeared that “unnecessary movement,” “waiting” and “indirect work” make up the highest labor waste.

This research focuses only on onsite resource flows in a housing construction site. It does not include offsite flows such as material delivery to site.

The findings have provided substantial evidence on type and amount of labor waste and provide a solid basis to stimulate construction actors to participate in reducing labor waste and improving productivity.

This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates.

A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, “vertical cracks,” “horizontal and vertical cracks” and “diagonal cracks,” subsequently, using “Matlab” to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates.

The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam’s optimization algorithm.

The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches.

A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.

The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection.

A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations.

The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.

The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources.

A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

The utilisation of emerging technologies for the inspection of bridges has remarkably increased. In particular, non-destructive testing (NDT) technologies are deemed a potential alternative for costly, labour-intensive, subjective and unsafe conventional bridge inspection regimes. This paper aims to develop a framework to overcome conventional inspection regimes' limitations by deploying multiple NDT technologies to carry out digital visual inspections of masonry railway bridges.

This research adopts an exploratory case study approach, and the empirical data is collected through exploratory workshops, interviews and document reviews. The framework is implemented and refined in five masonry bridges as part of the UK railway infrastructure. Four NDT technologies, namely, terrestrial laser scanner, infrared thermography, 360-degree imaging and unmanned aerial vehicles, are used in this study.

A digitally enhanced visual inspection framework is developed by using complementary optical methods. Compared to the conventional inspection regimes, the new approach requires fewer subjective interpretations due to the additional qualitative and quantitative analysis. Also, it is safer and needs fewer operators on site, as the actual inspection can be carried out remotely.

This research is a step towards digitalising the inspection of bridges, and it is of particular interest to transport agencies and bridge inspectors and can potentially result in revolutionising the bridge inspection regimes and guidelines.

The purpose of this paper is to introduce the use of critical success factors (CSFs) of stakeholder management as a possible solution to reduce disputes experienced because of legal and regulatory issues in public–private partnership (PPP) projects.

This paper’s epistemological positioning adopted positivism and deductive reasoning to investigate the dispute phenomena on PPP projects. A survey strategy was adopted using a structured questionnaire and closed-ended Likert scales to collate primary data. Questionnaires were distributed to South African construction professionals using both purposive and snowballing non-probability sampling techniques. Data was analysed using summary statistical analysis of the CSFs identified from literature.

This study revealed that among the 19 CSFs identified, five factors were highlighted that could contribute to the alleviation of disputes between stakeholders in PPP projects, namely, adequate project planning and control; effective leadership; appropriate strategies for the management of stakeholders; confirmation of clear goals and objectives of the project; and effective communication.

The strength of this study lies in the evaluation and use of CSFs of stakeholder management as a possible solution to minimise or even avoid disputes as a result of legal and regulatory issues in PPP projects. By integrating the CSFs, the legal and contractual misconceptions of the PPP initiative are clarified. Such work represents a novel contribution to procurement practice in South Africa and maybe to other countries internationally who are grappling with similar issues.

In this study, a critical literature review was utilized in order to provide a clear review of the relevant existing studies. The literature was analyzed using the meta-synthesis technique to evaluate and integrate the findings in a single context.

Digital transformation in construction requires employing a wide range of various technologies. There is significant progress of research in adopting technologies such as unmanned aerial vehicles (UAVs), also known as drones, and immersive technologies in the construction industry over the last two decades. The purpose of this research is to assess the current status of employing UAVs and immersive technologies toward digitalizing the construction industry and highlighting the potential applications of these technologies, either individually or in combination and integration with each other.

The key findings are: (1) UAVs in conjunction with 4D building information modeling (BIM) can be used to assess the project progress and compliance checking of geometric design models, (2) immersive technologies can be used to enable controlling construction projects remotely, applying/checking end users’ requirements, construction education and team collaboration.

A detailed discussion around the application of UAVs and immersive technologies is provided. This is expected to support gaining an in-depth understanding of the practical applications of these technologies in the industry.

The review contributes a needed common basis for capturing progress made in UAVs and immersive technologies to date and assessing their impact on construction projects. Moreover, this paper opens a new horizon for novice researchers who will conduct research toward digitalized construction.

This study aims to investigate numerically the problem of conjugate conduction and mixed convection heat transfer of a nanofluid in a rotational/stationary circular enclosure using a two-phase mixture model.

Hot and cold surfaces on the wall or inside the enclosure (heater and cooler) are maintained at constant temperature of T_h and T_c, respectively, whereas other parts are thermally insulated. To examine the effects of various parameters such as Richardson number (0.01 = Ri =100), thermal conductivity ratio of solid to base fluid (1 = K_r = 100), volume fraction of nanoparticle (0 = φ = 0.05), insertion of conductive covers (C.Cs) around the heater in a different shape (triangular, circular or square), segmentation and arrangement of the conductive blocks (C.Bs) and rotation direction of the enclosure on the flow structure and heat transfer rate, two-dimensional equations of mass, momentum and energy conservation, as well as volume fraction, are solved using finite volume method and Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm.

The results show that inserting C.C around heater can increase or decrease heat transfer rate, and it depends on thermal conductivity ratio of solid to pure fluid. Also, it is found that by the division of C.B and location of its portions in a horizontal configuration, heat transfer rate reduces. Moreover, it is observed that external heating and cooling of the enclosure causes enhancement of heat transfer relative to that of internal heating and cooling. Finally, results illustrate that under the condition that cylinders rotate in the same direction, the heat transfer rate increases as compared to those that rotate in the opposite direction. Hence rotation direction of cylinders can be used as a desired parameter for controlling heat transfer rate.

A comprehensive report of results for the problem of conjugate conduction and mixed convection heat transfer in a circular cylinder containing different shapes of C.C, conducting obstacle and heater and cooler has been presented. An efficient numerical technique has been developed to solve this problem. The achievements of this paper are purely original, and the numerical results were never published by any researcher.

Despite the expansion of women's entrepreneurial activities and its positive effects on the economic development of societies, women still face numerous difficulties in starting and running a business compared to men, especially in developing countries because of gender discrimination in the field. The cultural context in societies is a significant factor affecting the status of entrepreneurship among the Iranian women. Therefore, the present research is an attempt to identify the challenges affecting entrepreneurship among Iranian women. The results obtained from 30 semi-structured interviews with women entrepreneurs and women with entrepreneurial roles showed that sociocultural challenges faced by women entrepreneurs are classified into: “the society's perception of entrepreneurship among women,” “women's social security,” and “common family norms governing a society.”