The global economy's most significant sector, the construction sector, is key to accomplishing sustainability objectives. The building and infrastructure business is not typically thought of as being environmentally friendly, but this is likely to change as the ecosystem (the whole life cycle of all structures and infrastructure, from design and material manufacture to construction, use, and destruction) develops. Participants in the building sector cannot afford to ignore the worldwide trend of decarbonization. As the sector is responsible for more than 20% of all greenhouse gas emissions worldwide, construction companies have the opportunity to reduce their climate impact by decarbonizing their building operations. Participants in the construction sector must prioritise a strategic goal and collaborate with other ecosystem players, such as clients, architects, engineers, manufacturers, and financiers, in order to realize this potential. Here, we show what the industry can do right now and how stakeholders from all points along the value chain may work together to succeed. It is also important to keep in mind that if the low-interest-rate environment lasts and sizable stimulus packages are implemented, these developments may help deploy new sustainable infrastructure as well as infrastructure for adaptation and resilience, investments that would support the creation of jobs in the near future. In the meantime, it is possible that the need for international collaboration on this matter will increase in clarity and acceptance. This research study focuses on how construction affects greenhouse gas (GHG) emissions in the context of buildings, how the sector may decarbonize, and how businesses might profit from this process.

The preservation of The Great Mosque of Cordoba (Spain) as a carrier of Andalusian collective memory requires innovative approaches to assess and maintain its structural health. Numerical models are commonly employed to accurately estimate the building's static and dynamic behavior, with the accuracy hinging on precise input data, including the mechanical properties of the building material. While The Great Mosque's materials have been examined archaeologically, no prior mechanical characterization has been conducted. Due to the impracticality of extracting specimens from the building, non-destructive testing emerges as a viable alternative for material characterization. However, the existing literature lacks the strong correlations necessary to interpret the non-destructive testing results for this specific material.

This research centers on two commonly used non-destructive methods: direct ultrasound testing and rebound hammer. The former assesses elastic ultrasonic wave propagation velocity, while the latter reflects the stone's superficial strength through a rebound index, both of which can be potentially correlated with the stone's compressive properties. Laboratory tests were performed on natural stone provided by the primary material supplier for the Mosque's restoration and rehabilitation works. Over 100 cubic and prismatic specimens were cut from the stone and subjected to non-destructive and destructive tests, including density, compression, tensile, and bending tests. Correlations were established between the non-destructive test results and key material properties like compressive strength.

To investigate the stone's anisotropy, tests were conducted in multiple directions. Destructive testing indicated isotropy for the studied properties, yet ultrasound test results did not corroborate this finding. Sensitivity analysis regarding specimen dimensions showed an impact on the results' dispersion but not on average property values.

While further studies are necessary to delve deeper into this matter, the proposed methodology and correlations display promise for in situ characterization of natural stone in heritage buildings, particularly The Great Mosque of Córdoba.

Using innovation building technology as a case subject, this study aimed to be an exploration of the practices and techniques that are used in or as part of the framework in innovation in house construction. This study explored the existing literature on the subject and followed the proposed framework. The study methodology and the proposed conceptual framework were adopted from the house of quality and multicriteria decision-making procedure. Data were collected in two stages and were used to test or validate the framework. The two stages of this study were a questionnaire survey that targeted end users, namely contractors of innovation building technologies in the house construction industry. The second stage included interviews targeting the developers of innovation building technologies, who are also referred to as system holders. Regarding the findings of the study,the proposed conceptual framework may be used to measure innovation in house construction and may be used hand in hand with existing regulations in the house construction industry. On the other hand, the end system holders may use the proposed conceptual framework as a guide in innovation building technology projects. The value of this study lies in ensuring innovation building technologies maintain their original advantages and survive in the market.

West Java Pusdai Mosque has a very important role. Aside from being a place for congregational prayers, Pusdai Mosque is also a center for preaching and Islamic activities in West Java. Therefore, this mosque needs to create a comfortable atmosphere as a place of worship for Muslims. The comfort or solemnity of worship can be affected by the noise of the surrounding environment or the acoustics of the room. This study aims to analyze the acoustic quality of the room at Pusdai Mosque, which is influenced by several factors. This research was conducted by observing and simulating using Ecotect v5.50 software. The simulation was carried out by creating a 3D model by adding the absorption coefficient of the material and adding speakers. In addition, the research is also strengthened by a literature review of scientific articles. The simulation was carried out to determine the reverberation time and sound distribution produced by sound sources or speakers, which can indicate the acoustic quality of Pusdai Mosque. The acoustic quality of Pusdai Mosque is greatly influenced by the interior materials and the shape of the ceiling. Based on the results of the analysis, Pusdai Mosque has room acoustic defects. This is due to the large use of sound-reflecting materials and the form of ceiling, which is quite complex. This causes a lot of sound reflection to occur, causing the reverberation time to exceed the optimum limit for the frequency of 500 Hz (conversational space). This causes the speaker's voice to become an echo or hum. Therefore, Pusdai Mosque needs to improve the room acoustics to create comfort and solemnity in worship. Improvements can be achieved by adding sound-absorbing material.

The development of a comprehensive placemaking framework for cultural tourism in historic port cities is crucial for effective urban planning and the sustainable growth of tourism in these unique destinations. This paper aims to provide a comprehensive list of attributes and indicators that can guide decision-making processes in improving the cultural tourism experience within historic port cities. Through an extensive literature survey and expert input, a preliminary list of 16 attributes and 55 indicators that influence the placemaking for cultural tourism was compiled based on their frequency of citation in previous studies. These attributes were then categorized into four major dimensions that characterize the placemaking framework: (1) Physical; (2) Functional; (3) Social; and (4) Notional. A Fuzzy Delphi survey was conducted involving 12 selected experts, resulting in the identification of 43 significant indicators for the placemaking framework in historic port cities. Additionally, this study sheds light on important indicators that have been overlooked in previous research. The consolidated list of factors serves as a valuable resource for planners, local authorities, and government officials, enabling them to prioritize and implement relevant strategies according to the specific requirements of their historic port cities. Furthermore, this framework can serve as a reference for other destinations grappling with similar challenges in cultural tourism development.

Public–private partnerships (PPPs) have been very popular for the past thirty years in an effort to maximize the relative strengths of the public and private sectors, especially for infrastructure projects. The construction industry is a dynamic sector where the needs of clients change frequently. PPPs that were established a few years ago are not viable in today’s industry. There have been studies about the challenges and lessons learned from PPPs in construction projects all around the world, yet it is not certain how these challenges can be used to improve the performance of PPP projects. Building Information Modeling (BIM), as a 3D modeling and digitalization tool, has great potential to improve collaboration in PPP projects, which would improve the overall project performance with benefits such as reduced cost and duration. However, implementing this technology requires new steps rather than the well-known traditional approaches, especially when there are barriers affecting the success of BIM implementation in PPP projects. This study will analyze the drivers and barriers of BIM in PPP projects and aims to resolve the implementation dilemma by proposing a BIM Implementation Plan. The methodology will include conducting an extensive literature review and evaluating BIM adaptation at the company and project levels via PPP case studies. The results will show major drivers and barriers to utilizing BIM in PPPs and offer solutions to common barriers to address the challenges of PPP projects with regard to implementing BIM.

The Architecture, Engineering, and Construction (AEC) sector is observed to have a lower adoption rate of machine learning (ML) tools compared to other industries that share similar characteristics. A significant contributing factor to this lower adoption rate is the limited availability of data, as ML techniques rely on large datasets to train algorithms effectively. However, the construction process generates substantial data that provide a detailed characterisation of the project. This inclination towards generating abundant data in the Construction sector contradicts ML developers' prevailing challenge in sourcing sufficient data within the AEC industry.

In the specific case of Portuguese Construction Procurement, public construction projects are mandatorily submitted to online, open-source repositories. However, the consultation and extraction of procurement files is decentralised and not automated, making data agglomeration difficult and time-consuming.

In this sense, this paper presents a data-scraping algorithm to scrape construction procurement repositories to develop an ML-ready dataset of training data for ML and Natural Language Processing (NLP) algorithms focused on the Construction sector's procurement phase. This tool automatically scrapes procurement repositories, developing a procurement file dataset comprising bills of quantities (BoQ) and project specifications.

In future studies, the dataset will be processed into a standardised format suitable for NLP BOQ task-matching algorithms. These matching algorithms will aim to automate construction budgeting for tender proposal purposes.

The current seismic regulations neglect the influence of multiple seismic events on the seismic response, which, asalready found in the literature, may strongly affect the seismic structural behavior. Symmetrical and asymmetrical reinforced concrete low-rise frames are investigated here via nonlinear time-history (NLTH) analysis under multiple seismic events, as well as under a respective single seismic event, for comparison purposes. The two horizontal directions, as well as the vertical one, of the ground excitation are considered in the NLTH analysis, assuming the nonlinear behaviour of reinforced concrete sections under strong loading. A simple ratio is defined to express the geometrical in-plan asymmetry of the 3D building frames. The nonlinear response results of the time-history analyses are appropriately plotted by using unitless parameters for an objective evaluation of the seismic behavior of the building frames. The response dimensionless results and plots are presented and discussed in view of the relative geometrical asymmetry of the 3D frames. The effect of the multiple seismic events, along with the effect of a simple geometrical symmetry or asymmetry, is identified and discussed in the presented plots of the seismic structural response. Thus, practical remarks are obtained regarding the role of the geometrical in-plan symmetry or asymmetry of frames for the improvement of the guidelines of the current seismic codes ir order to develop safer structures.

Modern tall and irregular buildings have become increasingly slender and have columns with high axial force, which can pose a serious risk to the seismic safety of these structures. However, existing experimental studies on slender reinforced concrete (RC) columns under high axial force are limited due to the restrictions of testing facilities. Most studies are based on small cross-section specimens bent in single curvatures and loaded monotonically. But these studies may not accurately reflect the realistic seismic behavior of full-scale double-curvature RC columns in buildings due to size effects. Therefore, the objective of this study is to provide new insights into the seismic performance of full-scale slender and large cross-section RC columns with various transverse reinforcement designs under a constant high axial load of up to 50% of the axial capacity. The full-scale specimens were tested in a double-curvature configuration under cyclic lateral displacement reversals and high axial loads. The results show that the tested slender columns experienced significant P-Δ moment magnification effects, with further drifts after yielding. This imposed a greater loading demand on the sections and destabilized the columns after peak loads. The robustly anchored transverse reinforcement improved seismic performance indicators, including strength retention and drift capacity, and reduced the P-Δ moment magnification experienced by slender columns, which enhances the stability index.

The utilization of tower cranes at construction sites entails inherent risks, notably the potential for loads to fall on individuals. To address these risks, laws in many countries explicitly prohibit individuals from occupying the vicinity directly beneath suspended loads, known as the fall zone. This study proposes a novel method for identifying the tower crane load fall zone and determining workers' locations relative to this zone. The dynamic nature of crane load fall zones has not been adequately addressed in previous studies, mainly due to the difficulties in detecting various types of crane loads. Past studies have heavily relied on detecting a load based on its color and shape, which inevitably limits the range of possible identifications. Thus, this study presents a method that recognizes crane loads based on their movement patterns and elevation, using stereo cameras and computer vision algorithms. In addition to the previously mentioned limitation, earlier studies were constrained by the assumptions made to measure workers and load fall zone locations in 2D image space, such as assuming that all construction site entities were at the same height. To address this issue, the YOLOv7 deep learning algorithm was employed to accurately detect workers, while stereo camera depth data were utilized to measure their positions in the 3D world coordinate system. The effectiveness of the proposed method was validated through tests in a simulated small-scale project. The results indicate that this method can recognize a diverse range of loads with a high level of accuracy, exceeding 90%, which is a substantial improvement over earlier studies that identified only a limited set of load types. Additionally, the proposed method outperforms prior approaches in terms of analysis speed, achieving 8 frames per second speed compared to a maximum of 1 frame per second in earlier research.