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Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Soil is an essential component of road construction and is used in the form of subgrade materials. It ensures the stability and durability of the road under adverse conditions; being one of the important parameters, poor judgment of the engineering properties of soil can lead to pavement failure. Geopathic stress (GS) is a subtle energy in the form of harmful electromagnetic radiation. This study aims to investigate the effect of GS on soil and concrete.

A total of 23 soil samples from stress zones and nonstress zones were tested for different engineering properties like water content, liquid limit, plastic limit, specific gravity and California bearing ratio. Two concrete panels were placed on GS zones, and their quality was monitored through nondestructive testing for a period of one year.

The result shows that the engineering properties of soil and pavement thickness are increasing in stress zones as compared with nonstress zones. For concrete panels, as time passes, the quality of the concrete gets reduced, which hints toward the detrimental effect of GS.

This research is a systematic, scientific, reliable study which evaluated subgrade characteristics thus determining the detrimental impact of the GS on soil and pavement thickness. On a concluding note, this study provides a detailed insight into the performance of the road segment when subjected to GS. Through this investigation, it is recommended that GS should be considered in the design of roads.

This paper aims to examine accessibility in the built environment and tries to determine the physical and attitudinal barriers affecting Persons with Visual Disabilities' (PWVDs) experience on the University of Jordan (UJ) campus.

This is a descriptive mixed-methods study, based on the following: data collection regarding PWVDs' services at UJ; semi-structured interviews with PWVDs and with some employees at UJ; observations, photographs and direct measurements during campus tours; accompanying one student with poor eyesight when navigating through UJ campus; then, analyzing data in light of the national code's accessibility checklist.

UJ campus suffers from many shortcomings regarding accessibility; these include an insufficient pedestrian environment, limited tactile paths, low illuminance levels in lecture halls and other inadequate services. Besides, there are many infringements on PWVDs' paths, due to either new expansions or unconscious behavior. Moreover, interpersonal barriers prevent PWVDs from using assistive equipment and accommodation. The study concludes that preserving pedestrians' rights, monitoring new expansions, renovating the UJ campus in accordance with national codes and international standards, improving PWVDs services and awareness-raising programs are needed to ensure accessibility for PWVDs.

New legislation has been recently passed regarding accessibility in Jordan, and – on the UJ campus – the first phase of tactile paving has been installed. This paper is believed to be the first of its kind to evaluate PWVDs' services following the new changes. The study's methodology might also be deemed useful to stakeholders when enabling the built environment.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.

This work aims to develop a web platform for inspecting roof structures for technical assistance supported by drones and artificial intelligence. The tools used were HTML, CSS and JavaScript languages; Firebase software for infrastructure; and Custom Vision for image processing.

This study adopted the design science research approach, and the main stages for the development of the web platform include (1) creation and validation of the roof inspection checklist, (2) validation of the use of Custom Vision as an image recognition tool, and (3) development of the web platform.

The results of automatic recognition showed a percentage of 77.08% accuracy in identifying pathologies in roof images obtained by drones for technical assistance.

This study contributed to developing a drone-integrated roof platform for visual data collection and artificial intelligence for automatic recognition of pathologies, enabling greater efficiency and agility in the collection, processing and analysis of results to guarantee the durability of the building.

The issue of energy efficiency is becoming increasingly prevalent globally due to factors such as the expansion of the population, economic growth and excessive consumption that is not sustainable in the long run. Additionally, healthcare facilities and hospitals are facing challenges as their operational costs continue to rise. The research aim is to develop strategic frameworks for managing green hospitals, towards energy efficiency and corporate governance in hospitals and healthcare facilities.

This research employs a qualitative case study approach, with a sample of ten hospitals examined through interviews with senior management, executives and healthcare facilities managers. Relevant data was also collected from literature and analysed through critical appraisal and content analysis. The research methodology is based on the use of grounded theory research methodologies to build theories from case studies.

The research developed three integrated conceptual strategic frameworks for managing hospitals and healthcare facilities towards energy efficiency, green hospital initiatives and corporate governance. The research also outlined the concepts of green hospitals and energy efficiency management systems and best practices based on the conclusions drawn from the investigated case studies.

The study is limited to the initiatives and experiences of the healthcare facilities studied in the Middle East and North Africa (MENA) region.

The research findings, conclusions, recommendations and proposed frameworks and concepts contribute significantly to the existing body of knowledge. This research also provides recommendations for hospital managers and policymakers on how to effectively implement and manage energy efficiency initiatives in healthcare facilities.

The present work focuses on evaluating the physical and mechanical characteristics of geopolymer concrete (GPC) by replacing the sodium silicate waste (SSW) in place of traditional river sand. The aim is to create eco-friendly concrete that mitigates the depletion of conventional river sand and conserves natural resources. Additionally, the study seeks to explore how the moisture content of filler materials affects the performance of GPC.

SSW obtained from the sodium silicate industry was used as filler material in the production of GPC, which was cured at ambient temperature. Instead of the typical conventional river sand, SSW was substituted at 25 and 50% of its weight. Three distinct moisture conditions were applied to both river sand and SSW. These conditions were classified as oven dry (OD), air dry (AD) and saturated surface dry (SSD).

As the proportion of SSW increased, there was a decrease in the slump of the GPC. The setting time was significantly affected by the higher percentage of SSW. The presence of angular-shaped SSW particles notably improved the compressive strength of GPC when replacing a portion of the river sand with SSW. When exposed to elevated temperatures, the performance of the GPC with SSW exhibited similar behavior to that of the mix containing conventional river sand, but it demonstrated a lower residual strength following exposure to elevated temperatures.

Exploring the possible utilization of SSW as a substitute for river sand in GPC, and its effects on the performance of the proposed mix. Analyzing, how varying moisture conditions affect the performance of GPC containing SSW. Evaluating the response of the GPC with SSW exposed to elevated temperatures in contrast to conventional river sand.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

One of the most contested and anticipated research issues is the acceptability of using recycled aggregates instead of fresh aggregates. This study aims to look at the possibility of replacing fresh aggregates with 15%, 30%, 60% and 100% recycled aggregates.

The research is divided into two stages. The compressive, split tensile, flexural and bond strength of the various mixes were examined in the first phase using untreated recycled concrete aggregates (RCA). The second phase entails chemically treating RCA with a 10% 0.1 M sodium metasilicate solution to evaluate differences in strength, indicating the success of the treatment performed. Microstructural experiments such as scanning electron microscopy and X-ray diffraction were also conducted to evaluate the formation of interfacial transition zone (ITZ) in treated and untreated RCA specimens.

The observed findings reveal a decrease in concrete strength with increasing RCA concentration; however, when treated RCA was used, the strengths increased significantly when compared to untreated samples. The findings also include curves indicating the correlation between compressive strength and other mechanical strength parameters for an optimum mix of concrete prepared with 30% RCA replacement.

The study through its novel approach, demonstrates the effect of pretreatment of RCA in the absence of any standardized chemical treatment methodology and presents significant potential in minimizing reliance on fresh aggregates used in concrete, lowering building costs and promoting the use of waste materials in construction.