The traditional methods of marking construction site layouts using manual techniques such as chalk lines are prone to human errors, resulting in discrepancies between blueprints and actual layouts. This has serious implications for project delivery, construction, costs and, eventually, project success. However, this issue can be resolved through autonomous robots and construction automation in line with Industry 4.0 and 5.0 goals. Construction automation enables workers to concentrate on the construction phase and not worry about manual site markups. This leads to an enhancement in their productivity. This study aims to improve the floor layout printing technique by introducing a framework that integrates building information modeling (BIM) and the Internet of Things (IoT), i.e., BIM–IoT and autonomous mobile robots (AMR). The development process focuses on three key components: a marking tool, an IoT-based AMR and BIM. The BIM-based tools extract and store coordinates on the cloud platform. The AMR, developed using ESP32 and connected to the Google Firestore cloud platform, leverages IoT technology to retrieve the data and draw site layout lines accordingly. Further, this research presents a prototype of an automated robot capable of accurately printing construction site layouts. A design science research (DSR) method is employed in this study that includes a comprehensive review of the existing literature and usage of AMRs in construction layout printing. Subsequently building upon the extant literature, an AMR is developed and experiments are conducted to evaluate the system’s performance. The experiment reveals that the system’s precision falls within a range of ±15 mm and its angle accuracy is within ±4 degrees. Integrating robotic automation, IoT and BIM technologies enhances the efficiency and precision of construction layout printing. The findings provide insights into the potential benefits of deploying AMRs in construction projects, reducing site layout errors and improving construction productivity. This study also adds to the body of knowledge around construction automation in line with Industry 4.0 and 5.0 endeavors.

Climate change and global warming have increased the frequency and intensity of natural hazards such as floods, landslides, and avalanches. These hazards not only have significant individual impacts but are also interconnected, often amplifying their destructive effects. Therefore, it is crucial to manage their consequences and ensure that communities and infrastructure are resilient enough to withstand these challenges. Given the limited research assessing the collective impact of natural hazards, particularly in Pakistan, this study investigates the effects of floods and landslides in the Kohistan District of northern Pakistan, an area which is highly vulnerable to such hazards yet minimally studied. Machine learning techniques, including the Analytical Hierarchy Process (AHP) and weighted overlay, along with geographic information systems (GISs) and remote sensing (RS), were employed to analyze the causative factors of these hazards. The resulting flood risk and landslide risk maps were then superimposed to produce an integrated dual-hazard risk assessment. The research findings serve as a foundation for policy-making, offering strategies to reduce risks for all stakeholders, implement adaptive measures for communities, and ensure that future developments are both resilient and sustainable.

A key factor in ensuring the stability and ductility of asphalt pavements is interlayer fatigue resistance. Interlayer bonding characteristics are one of the most significant elements influencing the lifespan of asphalt pavements. Poor bonding properties often lead to debonding, slippage cracking, and pavement deformation. The primary cause of interlayer slippage cracking is a lack of interface bonding between an asphalt overlay and underlayer, which is typically triggered by vehicle braking and turning. Emulsified asphalt, modified asphalt, and hot asphalt are just a few of the materials that are used as tack coats to address this issue. This paper examines five different bonding types between interlayers: a model with no tack coat, a model with SBS-modified hot asphalt, a model with SBS-modified asphalt emulsion, a model with an epoxy resin binder, and a model with SK-90 hot asphalt. This study evaluates the shear fatigue of asphalt pavement under a single wheel cycle load. A model is created using the Abaqus software to predict fatigue life while taking into account the various tack coat materials listed above. Considering the outcomes of this study, the best bonding type for asphalt pavement is SBS-modified hot asphalt. After selecting this material, various tack coat thicknesses were used until the optimum thickness of 6 mm was determined. The proposed model can withstand more load cycles and less rutting depth, which helps to prevent interlayer fatigue failure over the course of a pavement’s design life.

Unsafe behaviors at construction sites often originate from cognitive failures such as lapses in memory and attention. This study proposes a novel, hybrid framework to systematically identify and predict the key contributors of cognitive failures among construction workers. First, a detailed literature review was conducted to identify 30 candidate factors related to cognitive failures and unsafe behaviors at construction sites. Thereafter, 10 construction safety experts ranked these factors to prioritize the most influential variables. A questionnaire was then developed and field surveys were conducted across various construction sites. A total of 500 valid responses were collected from construction workers involved in residential, highway, and dam projects in Pakistan. The collected data was first analyzed using conventional statistical analysis techniques like correlation analysis followed by multiple linear and binary logistic regression to estimate factor effects on cognitive failure outcomes. Thereafter, machine-learning models (including support vector machine, random forest, and gradient boosting) were implemented to enable a more robust prediction of cognitive failures. The findings consistently identified fatigue and stress as the strongest predictors of cognitive failures. These results extend unsafe behavior frameworks by highlighting the significant factors influencing cognitive failures. Moreover, the findings also imply the importance of targeted interventions, including fatigue management, structured training, and evidence-based stress reduction, to improve safety conditions at construction sites.