3rd World Conference on Architecture and Civil Engineering

The increase of urban population in the World followed by the increasing use of information technology; has led to the creation of a new concept in the architecture - the concept of “Smart Cities”. The basic idea of smart cities is to improve the quality of life of inhabitants in various fields, such as economy, administration, health, traffic, education, …etc. and transportation is among one of those. Intelligent transportation systems lead to remarkable improvement in transportation system performance, such as reducing congestion and increasing safety with traveller convenience. Internet technology enables elements within the transportation system-vehicles, roads, traffic lights, message signs, …etc.- to become intelligent by embedding them with microchips and sensors and enabling them to communicate with each other through wireless technologies. This study explores the role of using Intelligent Transportation Systems (ITS) to solve transportation problems within the city of Amman. The issues of congestion in Amman arise from many issues among them: the lack of street capacity, lack of efficient public transportation services and dependence on private cars, facilities to accommodate the increase in means of transportation, absence of proper management based on modern transport and technical development. This paper discusses the challenges of applying intelligent transportation systems in Amman city, since it’s not really activated in a proper way to solve transportation problems, and to achieve the best of these systems successfully.

This research investigated the influence of raw bentonite as well as waste glass as alternatives to calcium carbonate and normal sand on the properties of alkali activated mortar. Raw bentonite has been used to replace calcium carbonate partially with ratios of 25, 50 and 75 % by mass. Waste glass aggregate has been utilized to replace sand at levels of 0, 75 and 100% by mass. Six mixes have been planned prepared and examined. Alkali activator type and content is the same for all mixes with silica modulus of 1.5. high temperature treatment of the casted samples has been applied up to 1000 ℃ in order to study the late calcination of raw bentonite of the performance of alkali activated mortar. Mechanical properties as well as microstructural characterization have been carried out. The obtained results revealed that replacing calcium carbonate with raw bentonite improves thermal stability of the mixture at high temperature. Additionally, compressive strength is improved and weight loss is decreased after exposing to 1000 ℃. The use of crushed glass as a fine aggregate in alkali activated mortar exhibited better performance than normal sand when high temperature exposure is applied due to sintering and solidification of the material.

The target of this paper is to investigate the behavior of post-tensioned (PT) slabs after applying openings at different locations of the simple slab under distributed load. Using RAM CONCEPT software, eighteen post-tension High Strength Reinforced Concrete (HSRC) slabs with openings under distributed load were numerically investigated. The studied parameters were the opening dimension, aspect ratio, and location. The flexural behavior of the tested specimens was evaluated based on stress transferred by the numerical slab, short- and longterm deflection, and distribution of PT strands. Considerations and results done in this parametric study are referenced according to ACI 318-14. By reviewing the available literature, it was found that there were no experimental results for slabs has the same condition of the investigated slabs which made it difficult to compare the numerical results obtained from RAM CONCEPT with any experimental results.

This paper advances the concept of “architectural cannibalization” as a radical yet pragmatic response to the environmental crises confronting the contemporary construction industry. Grounded in the anthropological study of cannibalism—a practice historically vilified as barbaric and instrumentalized within colonial and racial power structures—the research reconfigures cannibalization as both a metaphor and a material strategy for sustainable architecture. It posits that urban environments, akin to living organisms, must develop the capacity to “digest” their own material legacies by systematically deconstructing obsolete structures and reappropriating their components, with a particular emphasis on the reuse of concrete and stone fragments. This cyclical process directly addresses construction waste and resource scarcity, while simultaneously preserving the cultural and historical memory embedded within salvaged materials. Drawing on precedents such as spolia and cyclopean masonry, the study explores how these historical practices of material reuse can inform contemporary design methodologies. Moreover, it examines the integration of advanced digital technologies, including 3D scanning and robotic fabrication, to facilitate the optimization of heterogeneous, reclaimed materials within new architectural forms. By synthesizing historical techniques and technological innovation, the concept of architectural cannibalization contributes a viable framework for achieving a closed-loop material economy and advancing sustainable urban development.

Concerns have been raised that connectionist approaches to design, particularly those using generative models, may result in stylistic homogenisation. This study aimed to examine whether a generative adversarial network (StyleGAN2-ADA) trained on heterogeneous landscape plan data would converge towards a singular aesthetic. A dataset of 2,000 diverse landscape plans was collected from online sources and used to train the model. The generative capacity of the model was evaluated by conducting latent walks— smooth interpolations between points in latent space—to produce new design variations. The results revealed a wide range of spatial formations, including grid-like, banded, and porous structures. These variations were found to reflect the diversity present in the training data rather than converge into a dominant style. The study demonstrates that connectionist design does not inherently produce a uniform formal language. On the contrary, it shows potential to support stylistic pluralism by encoding and generating diverse architectural expressions. These findings suggest that machine learning models, when trained on sufficiently varied data, can resist stylistic convergence and function as inclusive design tools. The approach offers broader implications for expanding formal diversity and rethinking authorship in computational design practices.

Effective subcontractor selection and efficient vendor management are critical elements of the successful execution of highway infrastructure projects. Inadequate subcontractor selection and poor management can adversely impact project outcomes in the form of budgetary excesses, timeline disruptions, and diminished quality standards. This study presents recommended methodologies while assessing subcontractor qualification to avoid any vendor mismanagement for the Department of Highways roadway construction projects, drawing upon scholarly literature, industry analyses, and documented project experiences. The paper identifies three primary evaluation metrics for subcontractor and vendor selections as historical performance, fiscal robustness, and safety compliance. The paper highlights the significance of rigorous subcontractor vetting procedures, subcontractor monitoring throughout the project, communication, and collaboration between the general contractor and the subcontractor. Additionally, paper explores challenges such as improper evaluation methods, lack of communication, and project scope misalignment between the general contractor and the subcontractor. The study will also include integrating technology tools. The aim of this study is to help project managers avoid conflicts with subcontractors, thereby improving project efficiency and avoiding schedule delay, ensuring project success.

Green roofs are part and parcel of promoting city sustainability since they prevent storm water runoff, reduce the urban heat island effect and increase biodiversity. However, they have an issue with their drainage being optimized in dynamic urban environments characterized by unpredictable weather conditions. The complexities cannot always be tackled through traditional drainage engineering that is based on static models and manual calculations. This paper explores what artificial intelligence (AI) and especially machine learning algorithms could contribute to the improvement of green roof drainage systems. The secondary quantitative data of various green building projects will be used to evaluate the potential of AI to increase water storage, minimize runoff and optimize drainage capacity. Comparative evaluation reveals that AI-fuelled strategies are outperforming traditional strategies in terms of the provision of high adaptability, real-time optimisation and eco-system friendliness. The paper looks into the prospect of combining AI and Internet-of-Things (IoT) sensors to support real-time data capture, thereby empowering adaptive and proactive drainage strategy. Key findings indicate that AI models, such as artificial neural networks and long short-term memory networks, surpass traditional models in predicting drainage performance across diverse conditions. The study concludes with recommendations to improve data quality, enhance AI model adaptability and promote the adoption of AI-driven solutions for green roof implementation, supporting climate-resilient and sustainable urban development.

One-part geopolymer concrete (OPGC) is gaining attention as a sustainable substitute for Portland cement, primarily due to its lower carbon footprint and the utilization of industrial by-products. This study examines the effect of varying activator-to-solid (a/s) ratios on the fresh and hardened properties of OPGC. Mixtures were produced using 30% magnesium slag (MS) and 70% ground granulated blast-furnace slag (GGBS) with a constant water-to-binder ratio of 0.47. Activator dosages ranged between 0.12 and 0.20. Fresh properties were assessed through slump and density tests, while hardened performance was evaluated using compressive strength and ultrasonic pulse velocity (UPV) at different curing ages. Results indicated that increasing activator content reduced slump, indicating lower workability due to higher viscosity. In terms of mechanical performance, compressive strength improved significantly with higher activator ratios, reaching up to 59.3 MPa at 90 days. However, excessively high dosages adversely affected workability. The optimum dosage was identified at a/s = 0.16, which provided a balance of good strength (46.07 MPa at 90 days) and acceptable fresh properties. These findings underline the importance of optimizing activator dosage to achieve durable and workable OPGC for sustainable construction.