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Showing papers in "Journal of building engineering in 2021"


Journal ArticleDOI
TL;DR: A review of management strategies for building energy management systems for improving energy efficiency is presented and different management strategies are investigated in non-residential and residential buildings.
Abstract: Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings' energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.

230 citations


Journal ArticleDOI
TL;DR: The historical development and recent advances in the application of machine learning to the area of building structural design and performance assessment are reviewed and the challenges of bringing machine learning into structural engineering practice are identified.
Abstract: Machine learning models have been shown to be useful for predicting and assessing structural performance, identifying structural condition and informing preemptive and recovery decisions by extracting patterns from data collected via various sources and media. This paper presents a review of the historical development and recent advances in the application of machine learning to the area of building structural design and performance assessment. To this end, an overview of machine learning theory and the most relevant algorithms is provided with the goal of identifying problems suitable for machine learning and the appropriate models to use. The machine learning applications in building structural design and performance assessment are then reviewed in four main categories: (1) predicting structural response and performance, (2) interpreting experimental data and formulating models to predict component-level structural properties, (3) information retrieval using images and written text and (4) recognizing patterns in structural health monitoring data. The challenges of bringing machine learning into structural engineering practice are identified, and future research opportunities are discussed.

163 citations


Journal ArticleDOI
TL;DR: In this paper, the authors comprehensively reviewed and analyzed digital twin (DT) concept, technologies, and application in the construction industry using a systematic review methodology while incorporating the science mapping method.
Abstract: The construction industry is faced with numerous challenges including low productivity, lack of research and development, and poor technology advancements . Advances in digital technologies such as digital twin (DT) has seen enormous utilisations in digitally advanced industries including the manufacturing and automotive industries. It presents an opportunity for the integration of the physical world to the digital world. DT technology has the potential to transform the construction industry and provide responses to some of its challenges. As a result, the concept of DT has attracted much attention and is developing at a rapid pace. The overarching aim of this study was to analyse the current state of DT applications in the construction industry. This study comprehensively reviews and analyses DT concept, technologies, and application in the construction industry using a systematic review methodology while incorporating the science mapping method. After a complete search of several databases and careful selection in line with the proposed criteria, 22 academic publications about DT application in the construction industry were identified and classified accordingly. The research analysed in detail the status, evolution of the concept, key technologies, and six areas of application in the lifecycle phases of a project: building information modeling , structural system integrity, facilities management, monitoring, logistics processes, and energy simulation. This research shows that there is a high potential for DT to enable solutions to the numerous challenges in the construction industry. Thus, this study raises the level of awareness and need for the application of DT in the construction industry.

155 citations


Journal ArticleDOI
TL;DR: A comprehensive assessment on the basis of recent studies has been conducted to point out the potential of PCM with the most appropriate techniques under different locations, considering the cooling/heating load reduction, energy-saving and thermal comfort gained.
Abstract: Building envelope is a key element in providing adequate energy and thermal comfort performance to buildings. In this regard, improvement solutions are implemented in recent studies that focus on new techniques and methods. The main techniques adopted in this context are discussed to identify modern and effective methods with a particular focus on phase change materials (PCMs). Incorporating PCMs with building construction materials is a booming technology, owing to their enhancement potential of storing and releasing heat during phase transition. This work highlights the importance of PCMs in building envelope, focusing on roof and external wall applications. PCM types, general and desired properties and application area are presented and discussed. Influential parameters, incorporation techniques and methods, main numerical tools, and modelling equations are used to describe the thermal behaviour of PCM. A comprehensive assessment on the basis of recent studies has been conducted to point out the potential of PCM with the most appropriate techniques under different locations. The main findings of PCM thermal performance have been described, considering the cooling/heating load reduction, energy-saving and thermal comfort gained along with several research hiatuses for future studies.

153 citations


Journal ArticleDOI
TL;DR: In this article, the state-of-the-art of the fine recycled concrete aggregates (fRCA), focusing on their physical and chemical properties, engineering properties and durability of concretes with fRCA, is discussed.
Abstract: This paper discusses the state-of-the-art of the fine recycled concrete aggregates (fRCA), focusing on their physical and chemical properties, engineering properties and durability of concretes with fRCA. Based on the systematic review of the published literature, it is impossible to deduce without any further research the guidelines and tools to introduce the widespread application of the fRCA in new concrete whilst keeping the cement contents at least the same or preferably lower. Namely, what is still missing is knowledge on key physico-chemical properties and their relation to the quality of the concrete mix and the concrete performance. This paper sets the foundations for better understanding the quality of fRCA obtained either from parent concrete specifically produced in the laboratory, with controlled crushing and sieving of the recycled aggregates or from field structures. By comparing properties of fRCA with properties of fine natural aggregates, the key limiting properties of fRCA are identified as the high water absorption of fRCA, moisture state of fRCA, agglomeration of particles and adhered mortar. As such, continuous quality of fRCA is hard to be obtained, even though they may be more continuous in terms of chemistry. Advanced characterization techniques and concrete technology tools are needed to account for limiting properties of fRCA in concrete mix design.

138 citations


Journal ArticleDOI
TL;DR: In this paper, the authors evaluated the performance of adding acai fibres in addition of up to 3.0% relative to cement mass and properly treated with NaOH solution.
Abstract: Acai (Euterpe oleracea Mart.) is a fruit from forests typical of South American countries, such as Brazil. The fruit is harvested from palm trees and later processed to produce several food and aesthetic products that bear considerable health benefits. The processing of acai generates substantial amounts of waste, such as natural fibres, which are generally disposed of in landfills. The objective of this work is to evaluate the technological performance of adding acai fibre (with additions of 1.5%, 3.0% and 5.0% relative to cement mass) in its natural condition and after surface treatment with NaOH in mortars based on cement and lime. Acai fibre was physically, chemically and microscopically characterised. The properties of consistency, water retention, incorporated air content, mechanical strength (compression and flexion), mass density (fresh and hardened state), capillary water absorption and durability (wetting and drying cycles) were analysed as well. Results show that acai fibres in additions of up to 3.0% relative to cement mass and properly treated with NaOH solution can be used as reinforcement mechanism for mortar applications.

122 citations


Journal ArticleDOI
TL;DR: In this paper, the authors performed a quantitative scientific evolution analysis of the application of the circular economy in the building sector to detect new trends and highlight the evolvement of this research topic.
Abstract: The building industry is responsible for considerable environmental impacts due to its consumption of resources and energy, and the production of wastes. Circular Economy (CE), a new paradigm can significantly improve the sustainability of this sector. This paper performs a quantitative scientific evolution analysis of the application of CE in the building sector to detect new trends and highlight the evolvement of this research topic. Around 7000 documents published 2005 to 2020 at Web of Science and Scopus were collected and analyzed. The bibliometric indicators, network citation, and multivariate statistical analysis were obtained using Bibliometrix R-package and VOSviewer. The co-occurrence analysis showed five keyword-clusters, in which the three main ones are: (i) energy and energy efficiency in buildings; (ii) recycling, waste management and alternative construction materials; (iii) sustainable development. The analysis showed that researchers pay close attention to “sustainability”, “energy efficiency”, “life cycle assessment”, “renewable energy”, and “recycling” in the past five years. This paper highlights that (i) the development and use of alternative construction materials; (ii) the development of circular business models; (iii) smart cities, Industry 4.0 and their relations with CE, are the current research hotspots that may be considered as potential future research topics.

111 citations


Journal ArticleDOI
TL;DR: In this paper, the authors present factors influencing the thermal conductivity coefficient of three main groups including conventional, alternative, and new advanced materials, including moisture content, temperature difference, and bulk density.
Abstract: Solving the matter of traditional energy consumption and finding the proper alternative resources are vital keys to a sustainable development policy. In recent years, many different thermal insulation materials have been developed for better energy efficiency and less environment damage. These products have confirmed their usefulness in buildings due to their benefits such as low density, high thermal resistance , and cost effectiveness. The efficiency of thermal insulation depends on their thermal conductivity and their ability to maintain their thermal characteristics over a period of time. This study presents factors influencing the thermal conductivity coefficient of three main groups including conventional, alternative, and new advanced materials. The most common factors are moisture content, temperature difference, and bulk density. Other factors are explained in some dependent studies such as airflow velocity , thickness, pressure, and material aging. The relationship between the thermal conductivity values with the mean temperature, moisture content, and density which were obtained from experimental investigation has also been summarized. Finally, uncertainty about the thermal conductivity value of some common insulation materials is also reviewed as the basis of selecting or designing the products used in building envelopes.

106 citations


Journal ArticleDOI
TL;DR: In this article, the effects of marine environment on the deterioration mechanism, performance, and durability of concrete materials and structures are systematically reviewed, and prospectives are proposed for practical applications on concrete under marine environment.
Abstract: Durability deterioration of cementitious concrete and reinforced concrete (RC) is critical to durability, safety, and sustainability of infrastructures, especially for offshore concrete structures under marine environment. In this paper, the effects of marine environment on the deterioration mechanism, performance, and durability of concrete materials and structures are systematically reviewed. For the deterioration mechanism, the effect of various chemicals in seawater and different marine exposure zones on the cementitious concrete and reinforced concrete is firstly analyzed and compared. At material level, this paper discusses the characterizations of cementitious concrete, including compressive strength, chloride diffusion, carbonation depth, and pore structure. On the other hand, the performance of cementitious concrete with the addition of supplementary cementitious materials was also compared when exposed to marine environment. At structure level, the durability of RC structures, including beams and slabs and other elements with corrosion protection under marine environment is evaluated. This paper also assesses some cases studies of RC structures after many years of exposure to marine environment. Furthermore, prospectives are proposed for practical applications on concrete under marine environment. The conclusions are of great benefit to the researchers and engineers in the concrete-related industry who aim to develop durable and sustainable concrete infrastructures under marine environment.

104 citations


Journal ArticleDOI
TL;DR: In this paper, a review of existing literature about alternative activators, produced from agricultural or industrial wastes, is presented, and some topics for future research on new activators are identified, aiming to stimulate more studies in this field.
Abstract: Alkali-activated materials are a new class of compounds that have been studied by several authors worldwide. Alkali-activated binders can be competitive alternatives to traditional Portland cement, especially concerning CO2 emissions into the environment. However, in order to obtain this advantage, it is essential to use activators that were produced cleanly and sustainably, which is not the case of commercial products such as NaOH or sodium silicate. This paper provides a brief discussion about the activators traditionally used, and a review of existing literature about alternative activators, produced from agricultural or industrial wastes. Factors such as molar ratios and preparation of the solution were highlighted. The mechanical behavior of the pastes and mortars was also assessed, showing that the performance of alternative activators can be similar or even better than the conventional ones. Finally, some topics for future research on new activators were identified, aiming to stimulate more studies in this field.

103 citations


Journal ArticleDOI
TL;DR: In this article, a comprehensive review of recent trends in incorporating biomass ashes from agricultural waste in Ordinary Portland cement (OPC) and geopolymer concrete is provided, where the material properties of different biomass ashes and their effect on fresh and hardened concrete properties (i.e., mechanical and durability properties) are reviewed.
Abstract: This paper aims to provide a comprehensive review of recent trends in incorporating biomass ashes from agricultural waste in Ordinary Portland cement (OPC) and geopolymer concrete. The material properties of different biomass ashes and their effect on fresh and hardened concrete properties (i.e., mechanical and durability properties) are reviewed. Partial replacement of OPC with byproducts, such as bamboo leaf ash, date palm ash, elephant leaf ash, banana leaf ash and plantain peel ash, rice straw ash, olive waste ash, wheat straw ash, and corn cob ash, escorts reduction in carbon dioxide (CO 2) emissions and global warming. It will also contribute to the effort of achieving zero-waste technology and sustainable development. This paper provides essential background information on the global status, composition, and ash preparation procedures of green and sustainable cementitious materials and then explores their potential applications. This review also highlights the areas requiring further research and indicates the possible negative impacts of utilizing these non-traditional supplementary cementitious materials (SCMs). The findings from this review confirm the feasibility of using biomass ashes as pozzolanic materials in cement concrete or as alternative activators in geopolymer concrete, with the required properties of building materials. Also, it is expected that this review will provide a better insight into biomass ashes incorporated in concrete for the benefit of academic/fundamental research and the construction industry.

Journal ArticleDOI
TL;DR: A new optimal method for home energy management system based on the internet of things based on ZigBee, based on a new improved version of the butterfly algorithm for increasing the convergence speed and user satisfaction is presented.
Abstract: This study presents a new optimal method for home energy management system based on the internet of things. The method is a multi-objective optimization method that considers two main purposes including energy consumption cost and user satisfaction. The method is designed under the environment of the smart grid. Generally, the impact of the users in the system efficiency in terms of energy cost saving is significant. This reason makes residential users participate in household appliances management. The optimization algorithm is based on a new improved version of the butterfly algorithm for increasing the convergence speed. IoT system is based on ZigBee which is known as the lowest consumption among different wireless technologies. The household employs based on a sample user scenario with different appliances. Using Multi-objective optimization gives fragmented energy consumption. The results of Multi-objective optimization are also compared with PSO-based and BOA-based algorithms to show the proposed method's effectiveness. Simulation results are compared by the normal home energy management system to declare the system efficiency.

Journal ArticleDOI
TL;DR: In this paper, a comparative study of different curing regimes, namely, standard curing (SC), internal curing (IC) with polyethylene glycol (PEG) and air curing (AC), used in ultra-high-performance concrete (UHPC) premixed with different types of nanomaterials was presented.
Abstract: This research presents a comparative study of different curing regimes, namely, standard curing (SC), internal curing (IC) with polyethylene glycol (PEG) and air curing (AC), used in ultrahigh-performance concrete (UHPC) premixed with different types of nanomaterials. Four types of nano waste materials were prepared, i.e. milled nano-metakaolin (NMK), nano waste glass (NWG) and nano rice husk ash (NRHA) and chemically prepared nano silica (NS). Several UHPC mixes with nanomaterial dosages (1%, 2% and 3%) were investigated. Compressive strength , ultrasonic pulse velocity , sulphate attack and microstructure were analysed. Results indicated the similarity between the performance of SC and IC in NS, NWG and NMK. Moreover, the addition of PEG exerted a negative effect on NRHA. Compressive strength increased by 17%, 24%, 14% and 13% under IC in NWG, NRH, NMK and NS, respectively. By contrast, sorptivity decreased by 84%, 60%, 48% and 60% in NS, NMK, NWG and NRHA under IC.

Journal ArticleDOI
TL;DR: In this article, the performance of high performance concrete and ultra-high performance concrete (UHPC) was evaluated in comparison to normal strength concrete (NSC) using scanning electron microscope imaging and X-ray diffraction (XRD) analyses.
Abstract: Durability characteristics of high-performance concrete (HPC) and ultra-high performance concrete (UHPC) are evaluated in comparison to normal strength concrete (NSC). HPC and UHPC are cast using commonly available materials with no special heat treatment. Concrete resistivity, rapid chloride permeability, sorptivity, porosity, and resistance to chloride migration and carbonation of these three types of concrete are assessed. Microstructure and hydration products are investigated using scanning electron microscope (SEM) imaging and X-ray diffraction (XRD) analyses, respectively. Potential enhancement in the service life of reinforced concrete (RC) structures when concrete is replaced with HPC and UHPC is predicted using the time-to-corrosion model. Dense microstructures, high electrical resistance, negligible chloride permeability, low sorptivity, no carbonation ingress are observed in HPC and UHPC. The chloride diffusion coefficient was found to be at least three orders of magnitude lower in UHPC compared to NSC, which could delay the corrosion initiation of steel reinforcement. With such positive attributes, these concretes are expected to find more widespread application in concrete structures in harsh-climatic conditions. This paper provides additional data and analysis that could accelerate the adoption of these materials in practice.

Journal ArticleDOI
TL;DR: A new environment-people-building framework is constructed to help researchers explain the relationship between natural and social environmental factors, which will help engineers and policymakers identify applicable designs and operation measures to reduce building energy consumption.
Abstract: Currently, building energy consumption is increasing, thereby exacerbating various issues, including climate change and environmental pollution. However, current building energy consumption research is limited to a unilateral review regarding the impact of environmental factors from natural or social aspects. To broaden this research, we first used VOS Viewer software to analyze high-frequency keywords appearing in relevant studies to determine the specific environmental factors that affect building energy consumption. Then, according to the visualization results, we reviewed the internal influence mechanism of environmental factors that affect building energy consumption from both natural and social perspectives. Based on these results, we constructed a new environment-people-building framework to help researchers explain the relationship between natural and social environmental factors. The results show that interdisciplinary interactive research that combines the natural and social environments from a dual perspective will attract new research ideas. Further, the framework provides help in systematically clarifying the mechanism of different factors affecting building energy consumption, which will help engineers and policymakers identify applicable designs and operation measures to reduce building energy consumption.

Journal ArticleDOI
TL;DR: In this paper, the compressive strength of concrete mixtures with high volume fly ash (HVFA) has been evaluated and modeled for the LEED (Leadership for Energy and Environmental Design).
Abstract: Advances in technology and environmental issues allow the building industry to use ever more high-performance engineered materials. In this study, the hardness of concrete mixtures with high-volume fly ash (HVFA) has been evaluated and modeled for the LEED (Leadership for Energy and Environmental Design). High-performance building materials may have greater strength, ductility, external factor resistance, more environmentally sustainable construction, and lower cost than conventional building materials. To overcome the mentioned matter, this study aims to establish systematic multiscale models to predict the compressive strength of concrete mixes containing a high volume of fly ash (HVFA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested HVFA concrete mixes) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear, Nonlinear Regressions, Multi-logistic Regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of concrete, i.e. fly ash (class C and F) incorporation ratio (0–80% of cement's mass), water-to-binder ratio (0.27–0.58), and gravel, sand, cement contents and curing ages (3–365 days). According to the correlation coefficient (R) and the root mean square error, the compressive strength of HVFA concrete can be well predicted in terms of w/b, fly ash, cement, sand, and gravel densities, and curing time using various simulation techniques. Among the used approaches and based on the training data set, the model made based on the ANN, M5P-tree, and Non-linear regression models seem to be the most reliable models. The results of this study suggest that the M5Ptree-based model is performing better than other applied models using training and testing datasets. The maximum and minimum percentage of error between the actual test results and the outcome of the prediction using MLR, LR, M5P-tree, and ANN were 0.03–43%, 0.03–54%, 0.04–33%, and 0.03–41% respectively. Based on the outcomes from the models and statistical assessments such as coefficient of determination (R2), mean absolute error (MAE) and the root mean square error (RMSE), the models M5P-tree, ANN, and MLR respectively were predicted the compressive strength of the HVFA concrete very well with a high value of R2 and low values of MAE and RMSE based on the comparison with experimental data. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of HVFA concrete with this data set.

Journal ArticleDOI
TL;DR: A critical review of available literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimisation was conducted in this paper, where the opportunities and challenges of AI applications were identified and presented in this study.
Abstract: The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labour shortages. Also, construction industry is one the least digitized industries in the world, which has made it difficult for it to tackle the problems it currently faces. An advanced digital technology, Artificial Intelligence (AI), is currently revolutionising industries such as manufacturing, retail, and telecommunications. The subfields of AI such as machine learning, knowledge-based systems, computer vision , robotics and optimisation have successfully been applied in other industries to achieve increased profitability , efficiency, safety and security. While acknowledging the benefits of AI applications, numerous challenges which are relevant to AI still exist in the construction industry. This study aims to unravel AI applications, examine AI techniques being used and identify opportunites and challenges for AI applications in the construction industry. A critical review of available literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimisation was conducted. Furthermore, the opportunities and challenges of AI applications in construction were identified and presented in this study. This study provides insights into key AI applications as it applies to construction-specific challenges, as well as the pathway to realise the acrueable benefits of AI in the construction industry.

Journal ArticleDOI
TL;DR: In this article, the physical and mechanical properties of a SBA-based geopolymer with various percentages of (PP)(PP) fibers were evaluated through the experiments and discussed in detail.
Abstract: Recently, the lightweight geopolymer production from wastes got adamant attention for sustainable and green building construction. But lower flexure and the tensile strength limit its wider application in the construction industry. This study was intended to prepare sugarcane bagasse ash (SBA) based geopolymer reinforced with (PP) (PP) fibers. The physical and mechanical properties of geopolymers with various percentages of (PP) (PP) fibers were evaluated through the experiments and discussed in detail. The addition of (PP) fibers resulted in enhanced flexural and tensile strength. Results assert that by limiting the content of (PP) fibers to 1%, not only improve in the flexural properties but also enhance the compressive strength by providing denser microstructure. This study concludes that the use of (SBA) composite reinforced with (PP) fibers can provide alternative ways to achieve sustainability by utilizing the wastes which mainly cause environmental degradation during landfilling.

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the influence of steam curing regimes on the properties of high-strength green concrete (HSGC) containing varying quantities of ultra-fine palm oil fuel ash (U-POFA) from 0, 20, 40% and 60% from the mass of Portland cement.
Abstract: This paper investigated the influence of steam curing regimes on the properties of high-strength green concrete (HSGC) containing varying quantities of ultra-fine palm oil fuel ash (U-POFA) from 0%, 20%, 40% and 60% from the mass of Portland cement. The HSGC specimens were steam cured at 50 °C, 65 °C, and 80 °C for 16 h in order to evaluate the effect of curing temperatures. Besides, the HSGC specimens were also cured at 80 °C for 6, 11 and 16 h in order to investigate the effect of curing period. The influence of different temperatures and periods of steam curing on the development of the compressive strength (CS) and microstructure of the HSGC was investigated at 1, 3, 7, 28, 90, 180 and 360 days. The results showed that replacing 20%, 40% and 60% of ordinary Portland cement (OPC) with U-POFA exhibited a decrease in the CS in early ages up to 7 days, whereas the long-term CS at 360 days improved by 5.4%, 10% and 9.2%, respectively in comparison to the control concrete mixture. It was also found that the application of steam curing regime at 80 °C for 16 h contributed towards increasing the strength of concrete by 193% at 1 day for HSGC containing 60% U-POFA when compared to normally cured specimen. The trends in CS development were complimented with microstructural analyses based on TGA, XRD and SEM/EDX. It was observed that steam curing has a significant influence on microstructures of matrix in early ages. However, it can be concluded that the partial replacement of U-POFA has positive impacts on the long-term properties of the HSGC at 360 days.

Journal ArticleDOI
TL;DR: In this paper, the micro carbon fiber (CF) was used to enhance the mechanical properties of fly ash geopolymer containing fine recycled concrete aggregate (RCA), where natural river sand was replaced with RCA at 0, 50, and 100% by volume.
Abstract: In this study, the micro carbon fiber (CF) was used to enhance the mechanical properties of fly ash geopolymer containing fine recycled concrete aggregate (RCA). Natural river sand was replaced with RCA at 0, 50, and 100% by volume. The CF was used as additive material by incorporating into the mixture at 0, 0.1, 0.2, and 0.3% by weight of fly ash. The results showed that the CF enhanced the mechanical properties of geopolymer containing RCA through the increased nucleation sites for geopolymerization reaction and the bridging effect of the fiber. For the mix with 100% RCA, the incorporation of 0.2% CF resulted geopolymer mortar with higher compressive and splitting tensile strengths . For the flexural strength and surface abrasion resistance , best results were obtained with the use of 50%RCA with significant improvement in both flexural strength and surface abrasion resistance. The incorporation of CF thus increases the use of recycled fine aggregate without resort to natural fine aggregate .

Journal ArticleDOI
TL;DR: Four advanced computational frameworks including relevance vector machine (RVM), group method of data handling (GMDH), hybridization of adaptive neuro-fuzzy interface system (ANFIS) and biogeography-based optimisation (BBO) are proposed as novel approaches to predict the heating load (HL) and cooling load (CL) of residential buildings.
Abstract: Modelling the heating load (HL) and cooling load (CL) is the cornerstone of the designing of energy-efficient buildings, since it determines the heating and cooling equipment requirements needed to retain comfortable indoor air conditions. Advanced and specialised modelling tools for energy-efficient buildings may provide a reliable estimation of the effect of alternative building designs. However, implementing these tools can be a labour-intensive task, very time-consuming and dependent on user experiences. Hence, in this study, four advanced computational frameworks including relevance vector machine (RVM), group method of data handling (GMDH), hybridization of adaptive neuro-fuzzy interface system (ANFIS) and biogeography-based optimisation (BBO), i.e. ANFIS-BBO, and hybridization of ANFIS and improved particle swarm optimisation (IPSO), i.e. ANFIS-IPSO, are proposed as novel approaches to predict the heating load (HL) and cooling load (CL) of residential buildings. Obtained results from the proposed models are compared using several performance parameters. In addition, several visualisation methods including Taylor diagram, regression characteristic curve, a novel method called accuracy matrix and rank analysis are used to demonstrate the model with the best performance. Furthermore, Anderson–Darling’ Normality (A-D) test and Mann–Whitney U’ (M − W) tests are studied as non-parametric statistical test for further investigations of the models. Obtained results indicate the excellent ability of the applied models to map the non-linear relationships between the input and output variables. Result also identified RVM as the best predictive model among four proposed models. Finally, two equations are derived from the RVM model to address the HL and CL of residential buildings.

Journal ArticleDOI
TL;DR: The extreme gradient boosting model is shown to have the highest coefficient of determination and lowest mean square error estimate and the superior performance of this machine learning model is further underscored through comparison of its shear strength predictions with those of existing code provisions and empirical models.
Abstract: Flat slabs, despite their aesthetic qualities and widespread use in construction, are susceptible to brittle shear failure. In addition, although design provisions are available, they are often associated with high bias and variance. This study evaluates the efficiency of machine learning-based approaches in establishing accurate prediction models for the punching shear strength of flat slabs without transverse reinforcement. To this end, 380 experimental results from various literature are assembled in this study. In addition to linear regression, seven machine learning methods as ridge regression, support vector regression, decision tree, K-nearest neighbors, random forest, adaptive boosting, and extreme gradient boosting—are considered in this study to obtain the best prediction model for the punching shear strength of flat slabs. Based on random assignment of the data into training and test sets and a performance evaluation of the test set, the extreme gradient boosting model is shown to have the highest coefficient of determination and lowest mean square error estimate. The superior performance of this machine learning model is further underscored through comparison of its shear strength predictions with those of existing code provisions and empirical models. It is noted that the extreme gradient boosting model has a coefficient of determination of 0.98, and the associated coefficient of variation is 0.09. This study also employs the SHapley Additive exPlanation method to explain the importance and contribution of the factors that influence the punching shear strength in the extreme gradient boosting model.

Journal ArticleDOI
TL;DR: In this article, 60 cubic concrete specimens reinforced with a rebar in its center and three bond lengths (2, 4, and 6 times the diameter) were prepared using UHPC and NSC and two types of rebars (high-strength and normal strength).
Abstract: Bond is the interaction and force transfer between rebar and concrete, which directly affects the performance of concrete structures. On the other hand, innovations in the production of ultra-high performance concrete (UHPC) and its unique advantages, such as high strength, low permeability, and high resistance to chemical attacks, have increased the interest in using it in the building industry. While most of the recommended relationships in the standards are based on normal strength concrete (NSC), it is necessary to know more about UHPC behaviour. In this study, 60 cubic concrete specimens reinforced with a rebar in its centre and three bond lengths (2, 4, and 6 times the diameter) were prepared using UHPC and NSC and two types of rebars (high-strength and normal strength). A pull-out test was performed on all specimens. The results show that the concrete strength, the ratio of concrete cover to the diameter (c/d), bond length, rebar yield strength and geometry of the rebar play an important role in determining the failure mode. The use of a high-strength rebar (AIV) in comparison with normal strength (AIII) rebars, by increasing the possibility of applying a higher bond tension to concrete, provides the opportunity to use more concrete capacity. UHPC reduces the embedded length of rebars by increasing bond strength 5-fold relative to NSC. In NSC, increasing the bond length increases the maximum bond stress, while in UHPC, for increasing bond length, the maximum bond stress decreases. New relationships have been proposed to predict bond-slip behaviour.

Journal ArticleDOI
TL;DR: It is concluded that the multi-objective optimization approaches need a further systematic study, and further studies of sustainable concrete optimization are needed by comparing the different chemical composition and particle characteristics.
Abstract: A comprehensive review of the statistical experimental optimization problem concerning the mixture design of various cement-based materials is presented herein. This review summarizes and discusses over 80 applications of optimum design regarding the basic test information under response surface method (RSM), including influence factor and corresponding response, statistical method, and coefficient of determination. The statistical experimental design reported in previous studies has shown that RSM is a sequential procedure to provide a suitable approximation for the mixture optimization. Then, linear, quadratic and interactive relationships of the statistical model can be evaluated available. Especially, the multi-objective optimization issues with multiple or competing performance requirements for various cement-based materials have also been reported, by considering fluidity, strength development, environmental impact, cost and durability. Overall, the results from existing publications have demonstrated that statistical inference and analysis of variance (ANOVA) are suitable for mix proportion design and process optimization of cement-based materials. The W/B ratio and mixture components are the prevalent factors in experimental design optimization, and then the fluidity and strength as the most popularly used response. Thus, theoretical optimum mixture proportioning can be used to predict valuable fresh and hardened properties. Finally, a critical discussion of the selection of design strategy, independent factors and their responses, and the experimental region involved in statistical experimental design, is provided. Based on this review, we conclude that the multi-objective optimization approaches need a further systematic study, and further studies of sustainable concrete optimization are needed by comparing the different chemical composition and particle characteristics.

Journal ArticleDOI
TL;DR: This work leverages naturally inspired machine learning (NIML) algorithms to derive compact and one-stepped predictive expressions that can accurately predict the structural response of CFST columns.
Abstract: Concrete-filled steel tubular (CFST) columns are unique structural members that capitalize on the synergy between steel and concrete materials. Due to complexities arising from the interaction between steel tube and concrete filling, the analysis and design of CFST columns are both intricate and tedious. A closer examination to the provisions of American, European and Australian/New Zealand design guidelines shows how these building codes seem to diverge on a proper methodology to design CFST columns. This leverages naturally inspired machine learning (NIML) algorithms (namely genetic algorithms and gene expression programing) to derive compact and one-stepped predictive expressions that can accurately predict the structural response of CFST columns. These expressions were developed and validated using the results of 3103 available tests carried out on CFST columns over the past few years. The outcome of this work shows that the NIML-derived expressions have superior prediction capabilities than those in currently used design codes.

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TL;DR: A comprehensive review of the existing wind tunnel experiment and Computational Fluid Dynamics studies are conducted here to present the past and recent achievements on the response mitigation of tall buildings.
Abstract: This paper reviews the state-of-the-art and –practice on various methodologies developed to control the wind-induced vibration of tall buildings. Tall buildings experience wind-induced vibration in the along- and across-wind directions depending on the wind direction, building shape, height, and structural properties. It is possible to control the wind response of buildings through passive, active, and semi-active systems. Damping systems, which are widely used to reduce the structural vibrations, are reviewed, and their performance in alleviating the building vibration is discussed. It was found that the application of conventional dampers needs to be reassessed to ensure their efficiency in dissipating the energy, especially caused by wind loads. Specific attention has been given to the aerodynamic modification of building shapes considering their effectiveness and high popularity within the wind engineering community. A comprehensive review of the existing wind tunnel experiment and computational fluid dynamics (CFD) studies are conducted here to present the past and recent achievements on the response mitigation of tall buildings. A comparative study on the performance of different systems has been provided that can provide a point to commence from for future studies. The existing challenges and their solutions are explained, and suggestions for future studies are proposed. It is expected that the information provided in this paper will facilitate further research in the area of vibration mitigation of tall buildings under wind events.

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TL;DR: In this paper, the effect of the concentration of sodium hydroxide (NaOH) and substitution of GGBFS with pozzolans such as natural zeolite (NZ) and silica fume (SF) on the mechanical properties of GPC was investigated experimentally.
Abstract: Geopolymer concrete (GPC), usually produced via the activation of the cementitious nature of industrial by-products (IBPs) such as ground granulated blast furnace slag (GGBFS) and fly ash (FA), has potential to be used as a replacement for conventional portland concretes (CPCs). In this study, the effect of the concentration of sodium hydroxide (NaOH) and substitution of GGBFS with pozzolans such as natural zeolite (NZ) and silica fume (SF) on the mechanical properties of GPC was investigated experimentally. For this purpose, the compressive, flexural, and tensile strengths of various GPC mixes were measured. GPC mixes were prepared with various concentrations (i.e., 4, 6, and 8 M) of sodium hydroxide as well as GGBFS substitution (i.e., 5, 10, 15, 20, 25, and 30 wt%) with NZ and SF. Furthermore, the response surface method (RSM) was employed to achieve the optimum values of the design variables to maximize the compressive, flexural, and tensile strengths of pozzolanic GGBFS-based GPC. Overall, the results showed that with increasing NaOH concentration, the compressive strength was decreased while the maximum flexural and tensile strengths were obtained when the concentration of NaOH was 6 M compared with 4 and 8 M. In addition, the utilization of NZ improved the compressive, flexural, and tensile strengths of GGBFS-based GPC by about 4%, 6%, and 20%, respectively, at 10 wt% replacement. Moreover, the substitution of GGBFS with SF could improve the compressive, flexural, and tensile strengths of GPC up to 30%, 20%, and 25%, respectively, at 30 wt% substitution. Besides, the optimization results demonstrated that by substituting GGBFS with 60.29 kg (i.e., 15.9%) of NZ and using 5.28 M NaOH the optimal trinary conditions in terms of the compressive, flexural, and tensile strength values could be achieved. The optimal conditions could also be obtained by substituting GGBFS with 113.79 kg (i.e., 30.0%) of SF and using 6.19 M NaOH.

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TL;DR: A machine learning-based methodology for reliably predicting the seismic response and structural collapse classification of ductile reinforced concrete frame buildings under future earthquake events by accounting for component- and system-level modeling uncertainties is presented.
Abstract: Robust seismic vulnerability assessment for a building under expected earthquake ground motions necessitates explicit consideration of all-important sources of uncertainty in structural model idealization. This paper presents a machine learning-based methodology for reliably predicting the seismic response and structural collapse classification of ductile reinforced concrete frame buildings under future earthquake events by accounting for component- and system-level modeling uncertainties. The proposed methodology uses two different types of machine learning methods—regression-based and classification-based methods—to achieve the goal of this study. Machine learning techniques with boosting algorithms (i.e., adaptive boosting and extreme gradient boosting) are the best methods for both response prediction and collapse status classification of modern code-compliant reinforced concrete frame buildings. Finally, the effect of uncertain modeling parameters on the response and collapse identification is examined. The reinforced concrete beam modeling-related parameters (i.e., plastic deformation properties) of ductile, low-to mid-rise frame buildings are significant predictors of seismic response due to capacity design principles.

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TL;DR: This research presents a Deep Reinforcement Learning (DRL)-based heating controller to improve thermal comfort and minimize energy costs in smart buildings and observes that as the number of buildings and differences in their setpoint temperatures increase, decentralized control performs better than a centralized controller.
Abstract: Buildings account for roughly 40% of the total energy consumption in the world, out of which heating, ventilation, and air conditioning are the major contributors. Traditional heating controllers are inefficient due to lack of adaptability to dynamic conditions such as changing user preferences and outside temperature patterns. Therefore, it is necessary to design energy-efficient controllers that can improvise occupant thermal comfort (deviation from setpoint temperature) while reducing energy consumption. This research presents a Deep Reinforcement Learning (DRL)-based heating controller to improve thermal comfort and minimize energy costs in smart buildings. We perform extensive simulation experiments using real-world outside temperature data. The results show that the DRL-based smart controller outperforms a traditional thermostat controller by improving thermal comfort between 15% and 30% and reducing energy costs between 5% and 12% in the simulated environment. A second set of experiments is then performed for the case of multiple buildings, each having its own heating equipment. The performance is compared when the buildings are controlled centrally (using a single DRL-based controller) versus decentralized control, where each heater is controlled independently and has its own DRL-based controller. We observe that as the number of buildings and differences in their setpoint temperatures increase, decentralized control performs better than a centralized controller. The results have practical implications for heating control, especially in areas with multiple buildings such as residential complexes with multiple houses.

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TL;DR: In this article, the development of fiber reinforced geopolymer composite (FRGC) being relatively new, a comprehensive database is developed based on past research work and pinpoint research gaps for further study and analysis.
Abstract: Conventional Portland cement-based composites are inherently weak under tensile stresses, due to its high brittleness quotient, and the problem gets further aggravated in geopolymer composites due to pozzolanic effect of precursors like fly ash, GGBFS, etc. Fiber reinforcement in conventional Portland cement concrete have been adopted, for quite some time, to remodel its character from brittle to ductile or quasi-ductile along with significant enhancement in mechanical as well as durability characteristics. With the global emphasis on partial or full replacement of Portland cement-based products in the construction industry and with the advent of “geopolymer” composite as potential replacement, efforts have been made to use fiber reinforcement in geopolymer composites to enhance its performance and service life. The development of fiber reinforced geopolymer composite (FRGC) being relatively new, the paper envisages to contribute to overall understanding and assessment of fiber utility in geopolymer materials. Against this background, a comprehensive database is developed based on past research work and pin-point research gaps for further study and analysis. Analytical assessment of past research reveals that FRGCs possess immense potential as a viable substitute for Portland cement-based composites with a scope for providing better mechanical, durability and structural performances, besides being more environmentally friendly. Further research is required to streamline its database, codes and practical design standards with different fibers, parameters and conditions.