Developments in the Built Environment最新文献

{"title":"Corrigendum to “Assessing classroom ventilation rates using CO2 data from a nationwide study of UK schools and identifying school-wide correlation factors” [Develop. Built Environ. 19 100520]","authors":"Samuel G.A. Wood, Alice E.E. Handy, Katherine Roberts, Henry C. Burridge","doi":"10.1016/j.dibe.2024.100542","DOIUrl":"10.1016/j.dibe.2024.100542","url":null,"abstract":"","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100542"},"PeriodicalIF":6.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychological artificial intelligence: Designing algorithms to deal with the uncertainty of rework in construction","authors":"Peter E.D. Love ,&nbsp;Jane Matthews ,&nbsp;Weili Fang","doi":"10.1016/j.dibe.2024.100586","DOIUrl":"10.1016/j.dibe.2024.100586","url":null,"abstract":"<div><div>As construction organizations are confronted with uncertainty and imperfect information, they find accommodating the likelihood of rework in their projects challenging. Bayesian statistical models cannot be utilized to predict rework as objective, and even subjective probabilities are unknown. In uncertainty settings, algorithms such as smart heuristics – <em>simple task-specific decision strategies that function under specific conditions</em> – have been shown to achieve equal and better performance in problems of inference than machine learning models. However, algorithms to effectively deal with the uncertainty of rework in construction have yet to be developed. Hence, the motivation for this paper is to examine how psychological artificial intelligence, which applies insights from psychology (e.g., mental and social processes) to design algorithms, can be potentially used to develop smart heuristics that can cater to the uncertainty of rework in construction in varying conditions and contexts. To this end, the contributions of this paper are twofold as it: (1) brings to the fore a <em>new</em> line of inquiry to deal with not only the uncertainty of rework using psychological insights to design simple algorithms but also unexpected events in general; and (2) provides guidance to ensure the design of algorithms to deal with the uncertainty that reflects the actualities of practice.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100586"},"PeriodicalIF":6.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative study on carbon emissions of modified recycled asphalt mixture based on life cycle assessment method","authors":"Xiaohua Liu ,&nbsp;Lu Deng ,&nbsp;Henglong Zhang ,&nbsp;Jiangmiao Yu","doi":"10.1016/j.dibe.2024.100584","DOIUrl":"10.1016/j.dibe.2024.100584","url":null,"abstract":"<div><div>The recycled asphalt technology is considered to have environmental sustainability prospects due to the resource conservation of old material recycling. Nonetheless, how to quantitatively evaluate the environmental impact of the entire life cycle of recycled asphalt pavement (RAP) still needs to be sorted out. Based on the theory of life cycle management, through the Life-cycle assessment (LCA) method, this study establishes a quantitative assessment model for the environmental impact of recycled asphalt pavement. A quantitative assessment model is established for the full life cycle environmental impact of recycled asphalt pavement. The model can output a list of environmental impacts for each stage of the life cycle, and can also conduct characteristic impact assessments based on five major impact categories: energy consumption (EC), global warming potential (GWP), acidification potential (AP), human health hazards (HTP), and particulate matter emissions. The results indicate that the acquisition of raw materials is the dominant stage for the environmental impact of cold recycled asphalt pavement, with a proportion of over 50% for each major impact category. In the construction of highways, using recycled modified asphalt mixture can reduce the total emissions by 12,976 kg per kilometer. In addition, the life cycle inventory (LCI) analysis shows that the environmental impact of recycled asphalt pavement is mainly quantified by energy consumption and various pollutant emissions, such as CO2, CH4, SO2, CO, N2O, NMVOC, particulate matter, and asphalt smoke. The raw material extraction stage has been identified as the stage with the greatest environmental impact, making significant contributions in energy consumption, global warming potential, acidification potential, human health hazards, and particulate matter emissions. This indicates that utilizing cold recycling technology and increasing the use of recycled RAP materials are efficient ways to promote energy conservation, reduce emissions, and minimize the environmental impact of asphalt pavement throughout its lifecycle.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100584"},"PeriodicalIF":6.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the Printing Quality and Mechanical Properties of 3D-Printed Cement Composites with Date Syrup-Based Graphene Coated Sand Hybrid","authors":"Mohd Mukarram Ali ,&nbsp;Rashid.K.Abu Al-Rub ,&nbsp;Fawzi Banat ,&nbsp;Tae-Yeon Kim","doi":"10.1016/j.dibe.2024.100582","DOIUrl":"10.1016/j.dibe.2024.100582","url":null,"abstract":"<div><div>This study addresses the influence of adding date syrup-based graphene-coated sand hybrid (D-GSH) as a partial substitute for sand in 3D-printed cement composites, exploring its potential to enhance both fresh and hardened properties. Incorporating 0.5% D-GSH significantly improved the printing quality compared to the control mix, as evidenced by the reduction in the variance in thickness of double layers from 38% to 28%. In addition, buildability was enhanced by 116% with an open time of 27 min. The rheological properties showed reduced viscosity and shear stress against shear rates, thereby enhancing flowability. Moreover, the addition of 0.5% D-GSH increased the compressive strength, flexural strength, and elastic modulus by 62%, 118%, and 40%, respectively, after 28 days of curing, compared with a mix containing silica fume. Microstructural analysis revealed that D-GSH effectively fills gaps, bridges cracks, and bonds with the hydration products of cement matrix, thus improving the mechanical properties.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100582"},"PeriodicalIF":6.2,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the flexural capacity of RC beams under various loading rates through strengthening with ultra-high-performance fiber-reinforced concrete","authors":"Doo-Yeol Yoo ,&nbsp;Booki Chun ,&nbsp;Jinsoo Choi ,&nbsp;Kyung-Hwan Min ,&nbsp;Hyun-Oh Shin","doi":"10.1016/j.dibe.2024.100581","DOIUrl":"10.1016/j.dibe.2024.100581","url":null,"abstract":"<div><div>This study investigates the enhancement of impact resistance in reinforced concrete (RC) beams using ultra-high-performance fiber-reinforced concrete (UHPFRC). Three types of steel fibers and two fiber volume fractions (0.75% and 1.5%) were considered. UHPFRC-strengthened RC beams exhibited an increase in flexural strength by approximately 6% at a fiber volume fraction of 1.5% compared to plain RC beams, due to effective crack suppression. Steel fibers in the UHPFRC strengthening layer inhibited the deep propagation of cracks into the compressive zone, resulting in a more gradual decrease in the neutral axis depth of RC beams. Under impact loading, UHPFRC-strengthened beams showed up to 7% lower deflection, with straight steel fibers providing superior impact resistance. RC beams strengthened with UHPFRC including straight steel fibers demonstrated improved residual flexural strength at higher fiber volumes. This highlights UHPFRC's effectiveness in enhancing impact resistance of RC beams according to the fiber type and volume fraction.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100581"},"PeriodicalIF":6.2,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of latex / silica aerogel composites for thermal insulation applications","authors":"Samuel Pantaleo,&nbsp;Florent Gauvin,&nbsp;Katrin Schollbach,&nbsp;H.J.H. Brouwers","doi":"10.1016/j.dibe.2024.100576","DOIUrl":"10.1016/j.dibe.2024.100576","url":null,"abstract":"<div><div>Silica aerogel stands out as an exceptional thermal insulation material and is a great candidate for modern and energy-efficient buildings. However, silica aerogel also faces many challenges, mainly due to its expensive, unsustainable and difficult synthesis process, but also its poor structural properties. Consequently, the main research focus for silica aerogel is to mitigate its brittleness in order to pave the way for broader applications, especially in the building field. Therefore, this study focuses on the development of composite materials aiming at solving the abovementioned drawbacks of silica aerogel, by using environmentally friendly latexes and reinforcement that are easy to process. Results show the positive effect of this reinforcement, even at a small amount (5% volume), on the composite's properties, with thermal conductivity at least equivalent, or better, to either in development or already established insulation materials in the market.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100576"},"PeriodicalIF":6.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing cyber risks in construction projects: A machine learning-centric approach","authors":"Dongchi Yao ,&nbsp;Borja García de Soto","doi":"10.1016/j.dibe.2024.100570","DOIUrl":"10.1016/j.dibe.2024.100570","url":null,"abstract":"<div><div>The construction industry is undergoing digitalization, but it is increasingly vulnerable to cyber attacks due to its slow pace in developing effective cyber risk assessment tools. This study develops a Machine Learning (ML)-centric approach to assess common cyber risks for construction projects. This approach comprises three components: (1) For risk prediction, a simulated dataset is generated using Monte Carlo simulations, which is utilized for model training. A two-phase model development strategy is proposed to select the optimal model for each risk. (2) For risk factor analysis, ML feature analysis methods are adapted to identify risk factors that contribute significantly to risks of specific projects. (3) For the risk reduction strategy, a greedy optimization algorithm is proposed to efficiently address high-contributing risk factors. To demonstrate the applicability of the developed approach, a case study is conducted on a real construction project.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100570"},"PeriodicalIF":6.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometrical and structural design development of an active-bending structure from natural fibre pultruded profiles: The LightPRO shell","authors":"Evgenia Spyridonos ,&nbsp;Yanan Guo ,&nbsp;Marta Gil Pérez ,&nbsp;Hanaa Dahy","doi":"10.1016/j.dibe.2024.100577","DOIUrl":"10.1016/j.dibe.2024.100577","url":null,"abstract":"<div><div>The utilisation of bio-based materials has significantly increased in recent years, driven by a growing awareness of environmentally friendly alternatives in the construction industry. This study introduces innovative natural fibre pultruded profiles for load-bearing applications in structural systems. By employing pultrusion technology with flax fibres and customised plant-based matrix, linear and unidirectional biocomposite profiles were developed. These profiles were used in the creation of LightPRO Shell, an active-bending structure combining biocomposite profiles with a membrane outer skin, demonstrating their mechanical properties and suitability for such applications. The paper focuses on the geometrical and structural design development of the structure employing computational design tools for optimisation, ensuring design parameters and performance requirements were met. The final structure, a 10-m span doubly curved gridshell, features a continuous perimeter beam and consists of 44 profiles ranging from 6 to 12.5 m, showcasing the potential of natural fibre biocomposites as sustainable alternatives in construction.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100577"},"PeriodicalIF":6.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and validation of a selective binding 3D printer for bio-based construction materials","authors":"Valentine Danché,&nbsp;Mamma Bouaouich,&nbsp;Abdeslam Benamara,&nbsp;Alexandre Pierre,&nbsp;Khadim Ndiaye,&nbsp;T.T. Ngo","doi":"10.1016/j.dibe.2024.100579","DOIUrl":"10.1016/j.dibe.2024.100579","url":null,"abstract":"<div><div>We aim to address that home-made design and assembly can be effective to investigate several sizes and natures of construction materials. This study offers a new design that accepts different powder and bio-based materials to enhance the functionality of construction elements. We developed a system in which a numerical controlled machine is coupled with a 6 axis articulated-arm to selectively bind a mineral powder bed with liquid’s droplets. A home-made frame has been designed and built to adapt the geometrical space that allows the displacement of the numerical controlled machine and the articulated arm. The powder deposition is carried out using a tank controlled by an Arduino board. In parallel, we propose a fluid deposition system using a pressurized tank and a valve jetting that allows the deposition of millimetric liquid droplet. 3D printing tests using cement particles, water retention agent, and a composite of cement with hemp aggregates were carried out to validate the prototype. We then highlight, as a proof of concept, that an innovative selective binding 3D printer could bring new composites or biobased materials in construction industry.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100579"},"PeriodicalIF":6.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the compositional effect of eco-friendly ultra-high performance concrete on dynamic strength based on stacking algorithm and explainable artificial intelligence","authors":"L.L. Wu ,&nbsp;D.L. Zou ,&nbsp;Y.F. Hao","doi":"10.1016/j.dibe.2024.100574","DOIUrl":"10.1016/j.dibe.2024.100574","url":null,"abstract":"<div><div>This study proposes a two-layer fusion model (stacking-CARF) to predict the dynamic compressive strength of eco-friendly ultra-high performance concrete (UHPC). Before building the prediction model, a well-balanced UHPC dynamic compression dataset is created using the anomaly detection algorithm. Subsequently, it is experimentally determined that the stacking-CARF model consisting of categorical boosting, random forest and linear regression outperforms other prominent ensemble learning and stacked models, and can be used as a robust strength prediction tool. Moreover, Explainable Artificial Intelligence is utilized to elucidate the intricate relationship between material proportions and dynamic compressive strength from both global and local perspectives, offering insights challenging to quantify using traditional methods. In particular, the interaction analysis affirms the role of reasonable replacement ratios between cement and supplementary cementitious materials in enhancing sustainability and cleaner production practices. Finally, a Python-based graphical user interface is developed to facilitate the implementation of the stacking-CARF model in engineering applications.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100574"},"PeriodicalIF":6.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}