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This study aims to analyze the factors, evaluation techniques of the durability of existing railway engineering.

China has built a railway network of over 150,000 km. Ensuring the safety of the existing railway engineering is of great significance for maintaining normal railway operation order. However, railway engineering is a strip structure that crosses multiple complex environments. And railway engineering will withstand high-frequency impact loads from trains. The above factors have led to differences in the deterioration characteristics and maintenance strategies of railway engineering compared to conventional concrete structures. Therefore, it is very important to analyze the key factors that affect the durability of railway structures and propose technologies for durability evaluation.

The factors that affect the durability and reliability of railway engineering are mainly divided into three categories: material factors, environmental factors and load factors. Among them, material factors also include influencing factors, such as raw materials, mix proportions and so on. Environmental factors vary depending on the service environment of railway engineering, and the durability and deterioration of concrete have different failure mechanisms. Load factors include static load and train dynamic load. The on-site rapid detection methods for five common diseases in railway engineering are also proposed in this paper. These methods can quickly evaluate the durability of existing railway engineering concrete.

The research can provide some new evaluation techniques and methods for the durability of existing railway engineering.

This paper aims to present the integrated teaching activity carried out in the Studios of the Master of Science “Architecture-Building Architecture” held at the School of Architecture Urban Planning Construction Engineering of the “Politecnico di Milano,” Milan, Italy. The integrated teaching activity related to the structural disciplines was in Sgambi et al., 2019; here the structure of the MSc training and its disciplinary synergies will be presented. Indeed, this type of activity characterizes all the Studios of this Master of Science and involves all the disciplines that contribute to the development of an architectural and cultural heritage preservation project.

In the Studios of the aforementioned Master of Science, teachers of different subjects are involved, working together to guide the student in the development of an architectural project sustainable in all aspects. The fundamentals of each discipline are taught using the best suited teaching methodology and the application phase of each discipline is carried out directly on the students' projects in the form of “learning by making.” The students are thus stimulated to deepen their basic knowledge of each single discipline, making their design choices sustainable.

This experience, born in 2003 and still active, has also achieved good results in employment. Students train using the “learning by making” method to acquire proficiencies in various disciplines of design, giving them the ability to communicate competently with experts belonging to different construction sectors.

The approach illustrated in this paper does not represent the didactic experimentation of a single discipline, but it is typical of the study program of an entire Master of Science. Although this approach is entirely built on a “learning by making” and “active learning” philosophy, it maintains the teaching of the theoretical contents of disciplines at a significantly high level when compared with the contents of a frontal-taught theoretical course. The development of this structure required a strong commitment on the part of the teaching staff in their search for effective teaching methods in each individual discipline and aimed at the architectural project. The results obtained give an added value to the training of future architects.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

Housing provision and the neighbourhood's safety are significant social sustainability concerns. If structural issues are not well checked, housing provision and the neighbourhood's safety may become threatened, especially in Lagos State, Nigeria. Thus, this study aims to investigate the perceived root cause of collapsed buildings at the construction stage using two case studies, its effect on social sustainability aspects and suggested measures to mitigate future happening and enhance achieving social sustainability aspects goals.

The researchers collected data from Nigeria's built environment experts and eyewitnesses/employees of selected cases of collapsed buildings. The study adopted a phenomenology type of qualitative research design and analysed collated data via thematic analysis and achieved saturation. The analysed data created three themes.

Results reveal that inadequate heavy equipment and personnel incapacitated relevant government agencies are responsible for handling emergency and rescue during building projects collapse. Preliminary findings show developers' greed and systematic failures as the root cause of Nigeria's building project collapse (BPC). It categorised the root causes into three groups (developer's related-cause, design team related-cause and government entities related-cause). The study suggested measures to mitigate future happening. The emerged measures were grouped into a penalty, regulatory, byelaw act, technical and safety measures.

This study contributes to curbing the threat to social sustainability of housing provision in cities. It reveals the underlying perceived root cause of collapsed buildings in Nigeria's building industry. Also, it suggested feasible measures to mitigate BPC. These measures may be modified and adopted by other developing countries facing similar challenges.

Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.

The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.

The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.

The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.

The purpose of this paper is to analyze the potential for implementing Sustainable Development Goals (SDGs) into the civil engineering bachelor degree in the School of Civil Engineering at Universitat Politècnica de València (Spain).

All the 2019/2020 course syllabi were analyzed to diagnose at which extent each subject within the program curriculum contributes to achieving the different SDGs.

The results show a promising starting point as 75% of the courses address or have potential to address targets covering the 2030 Agenda. This paper also presents actions launched by the School of Civil Engineering to boost the SDGs into the civil engineering curriculum.

This paper presents a rigorous and systematic method that can be carried out in different bachelor degrees to find the subjects that have the potential to incorporate the SDGs into their program. This paper also presents actions launched by the Civil Engineering School to boost the SDGs into the civil engineering curriculum.