Abstract
Reducing carbon emissions is a major challenge for the building sector and is essential for environmental sustainability. Because existing approaches cannot handle complex, time-dependent data, they frequently fail to anticipate carbon prices accurately. Our method improves forecast accuracy by utilizing optimized LSTM networks and large data analytics, allowing for more efficient carbon cost reduction plans. This method improves real-time decision-making by incorporating temporal interdependence and overcoming constraints in existing models. Initially, historical data on energy, emissions, investments, and economics of the Chinese residential construction industry were given by the China-Building-Energy-and Emission-Database (CBEED) during 2000 to 2015. Z-score normalization and missing value relevant data were used in the data prepossessing process to normalize the data's scale. Recursive feature elimination with linear regression model (RFE-LR) is a wrapper technique that expands the set of features for the LSTM model by repeatedly removing the least significant features based on model performance. This reduces overfitting and increases computing performance. Features were gathered from huge quantities of data. Our proposal of the hybrid harmony search algorithm with red fox optimized (HHSARFO) to reduce carbon costs in the construction industry by utilizing long short term memory (LSTM) for hyper parameter optimization. The proposed improved performance over other similar models in terms of precision (99%), carbon emission (150ton) RMSE (26%), MAE (30%), and R2 (67%) indicates the resultant carbon costs reduction in the building industry. The concluded a great deal of promise for lowering carbon emissions, improving sustainability, and encouraging environmentally friendly practices in the building sector.
Key Words
big data analytics; carbon cost reduction; construction industry; hybrid harmony search algorithm with red fox optimized (HHSARFO); long short term memory (LSTM); recursive feature elimination with linear regression model (RFE-LR); Z-score normalization
Address
Hubei University of Technology School of Civil Engineering Architecture and Environment, Wuhan City, Hubei Province, 430070, China.
Abstract
A bridge's functionality and security are compromised by deck degradation. About 45,000 crossings in Ohio require inspection to guarantee that they are structurally sound. Predicting bridge corrosion 1 accurately and promptly is essential to averting accidents. The goal of this study was to create a precise simulation that could be used to forecast Ohio bridge decking characteristics. After thoroughly analyzing the existing research, it was discovered that earlier studies' methods to predict the deterioration of bridge decks were created using different characteristics and techniques. This research suggests combining Bayesian Neural Networks (BNN) with Seeker Optimization Algorithm (SOA) methods to improve the accuracy of bridge deck worsening and concrete strength predictions. This is because there is no certainty that the characteristics and algorithms used by previous researchers will produce precise findings for Ohio's bridges. Utilizing various feature-selection techniques, the structure's initial goal is to identify the "optima"l qualities that may be connected to deck deterioration circumstances, particularly concerning Ohio's bridges. Outcomes from the BNN-SOA techniques that employed the "optimal" characteristics as inputs had been compared with findings from identical deep learning methods that employed the "most frequent" characteristics used in earlier research to verify the structure. Whenever the "optimal" characteristics were used, the combination of DL methods was significantly better at predicting decking characteristics than individual models based on a data set sourced by the Ohio Department of Transportation (ODOT). The system can be effectively employed by other transportation authorities to estimate the degradation of bridge parts better and correctly, considering it was created utilizing ODOT information.
Key Words
accurate prediction; concrete strength; deep learning; large bridges; neural networks
Address
(1) Yuan Yang, Fu Zhang:
The 4th Engineering Company of China Railway12th Bureau Group Ltd., Co., Xi'an, 721000, Shaanxi, China;
(2) Yahe Tan:
Northeastern University, School of Resources and Civil Engineering. Shenyang, 110819, Liaoning, China;
(3) Liu Liu:
Department of Road and Bridge Engineering, Hebei Jiaotong Vocational and Technical College, Shijiazhuang, 050091, Hebei, China
(4) Liu Liu:
Hebei Transportation Construction Application Technology Collaborative Innovation Center, Shijiazhuang, 050091, Hebei, China;
(5) Liu Liu:
School of Civil Engineering, Shijiazhuang TieDao University, Shijiazhuang, 050043, Hebei, China.
Abstract
Traditional methods for landscape design using recycled waste materials, which rely primarily on manual selection and placement for integration and optimization, frequently encounter sustainability issues, which reduce the overall efficiency. To address these constraints, this study proposes a hybrid machine-learning technique that leverages AI-enhanced algorithms to improve the usage of recycled materials in landscape design. Based on total adaptive learning models, this strategy incorporates adaptive correction of material variations, followed by improvement utilizing AI-informed selection algorithms. AI technologies, particularly AI sensing techniques, are used to improve adaptive material integration, allowing for more incredible sustainable growth of landscape projects. The AI-enhanced technique allows for more precise utilization and analysis of recycled materials at the design level, considerably improving the sustainability of landscape projects. The experimental results show a significant increase in the effectiveness of landscape projects processed with these AI-enhanced techniques. The findings support the suggested method's efficacy, demonstrating its robust enhancing capabilities, efficiency, and practical applicability in sustainable landscape design. This advancement contributes significantly to improving the quality of landscape projects, particularly in sustainable development applications where precision and resource optimization are critical.
Address
(1) Yuanyuan Yao, Yuansheng Huang, Xiujie Jiang, Yadong Zheng:
Art and Design College of Zhejiang Guangsha Vocational and Technical University of Construction, Jinhua, 322100, Zhejiang, China;
(2) Suhui Li:
School of Design, Shanghai Jiaotong University, Shanghai, 200240, China;
(3) Pengfei Shi:
Shangqiu Institute of Technology, Education and Art School, Shangqiu, 476000, Henan, China;
(4) Zhaochen Wang:
School of Economics and Management, Shangqiu Normal University, Shangqiu, 476000, Henan, China.
Abstract
Most widely utilized material in building sector, concrete is recognised as a pollutant to the environment and presents significant obstacles to sustainability in terms of energy use, greenhouse gas emissions, and resource depletion. Therefore, to increase the sustainability of concrete, efforts must be concentrated on reducing the material's negative environmental effects. this research proposes novel technique in computer aided system based concrete structure designing and manufacturing with their construction waste analysis using big data and machine learning model. The aim is to develop concrete structure design based on big data in manufacturing and their waste reduction is carried out using linear stochastic regression based Gaussian gradient vector machine. The sustainability index declines as cement as well as super-plasticizer content are increased in mixture design. Following design of sixteen sustainable mixture proportions, the most inexpensive, environmentally friendly, sustainable, least material-intensive mixtures are compared and presented according to their sustainability indices. The experimental analysis has been carried out in terms of computational cost, design efficiency, training accuracy, reliability, precision. According to the experimental findings, as the ratio of plastic aggregate increases, densities reduce by around 10% and workability rises by approximately 60%. As plastic substitution increases, compressive strength and split tensile strength drop by 14% and 34%.
Key Words
big data; concrete structure design; machine learning model; manufacturing; waste reduction
Address
(1) J. Laxmi Prasad:
Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad, 500043, India;
(2) J. Srikanth:
Department of Computer Science and Engineering (AI&ML), Marri Laxman Reddy Institute of Technology and Management, Dundigal, Telangana, 500043, India;
(3) G. Deena:
Department of Computer Science and Engineering, SRMIST, Ramapuram, Chennai, India;
(4) Pradeep Jangir:
Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai - 602 105, India;
(5) Pradeep Jangir:
Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan;
(6) K. Jamberi:
School of computer Science and Applications, REVA University, Bangaluru, 560064, Karnataka, India;
(7) Kalyanapu Srinivas:
Department of Computer Science and Engineering, Dhanekula Institute of Engineering & Technology, Ganguru, Vijayawada - 521139, A.P. India.
Abstract
Achieving sustainability in today's society is mostly dependent on energy efficiency. The viability of smart cities hinges on the availability of services and infrastructure that use less energy. The properties of different types of concrete, including geopolymer, fiber-reinforced, conventional, and recycled aggregate concrete, are predicted using machine learning techniques. From a recycling standpoint, using plastic waste in concrete may be the best option for the building sector. this research proposes novel technique in energy efficiency with concrete waste management using machine learning model based on sustainable construction application. In this research the concrete construction energy efficiency is carried out using discriminant extreme backward fuzzy genetic neural networks. Then the concrete waste management is carried out using support vector perceptron with concrete aggregate component analysis. the experimental analysis has been carried out for various concrete construction parameters in terms of sensitivity, efficiency co-efficient, accuracy, specificity, Coefficient of Determination (R2). The proposed model attained accuracy of 98%, Efficiency co-efficient of 95%, Sensitivity of 93%, SPECIFICITY of 89%, R2 of 96%.
Key Words
aggregate component analysis; concrete waste management; energy efficiency; machine learning model; sustainable construction
Address
(1) G.V. Rambabu:
Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad-500043, India;
(2) R. Ramya Swetha:
Department of Civil Engineering Institute of Aeronautical Engineering, Dindigul, Hyderabad, India;
(3) Pritee Parwekar:
Department of CSE, GIT, GITAM University, Hyderabad, India;
(4) Pradeep Jangir:
Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai - 602 105, India;
(5) Pradeep Jangir:
Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan;
(6) S. Amutha:Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, India;
(7) V. Sivaramaraju Vetukuri:
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist., A.P., India.
Abstract
All evidence-based waste management endeavour needs accurate data on construction waste creation, but because many developing nations have outdated recording systems, this data is still hard to come by. Around 50% of global carbon dioxide (CO2) emissions connected to energy use in buildings have historically come from this industry. Thus, in the global endeavour to decarbonise the energy system, it garners a great deal of attention. In order to anticipate CO2 emissions from buildings over the long term, this research introduces and compares several Machine Learning (ML)-based methods. This research proposes novel technique in concrete waste reduction based on energy efficiency analysis and carbon footprint modelling using machine learning algorithms. Here the concrete construction waste reduction with energy efficiency is carried out using Bayesian multilayer reinforcement neural networks. then the carbon footprint analysis in smart building construction using fuzzy Gaussian linear hidden markov vector model. the experimental analysis has been carried out based on various concrete composition and CO2 analysis in terms of MAPE (mean average energy efficiency error), detection accuracy, correlation coefficient values (R), root mean square error (RMSE), energy efficiency. Proposed method produced 98% detection accuracy, 97% correlation coefficient values, 95% energy efficiency, 68% RMSE, and 58% MAPE.
Key Words
carbon footprint; concrete waste reduction; energy efficiency analysis; machine learning algorithms; markov vector model
Address
Department of Information Technology, Terna Engineering College, Nerul, Navi Mumbai, India.
Abstract
Detecting and quantifying cracks in bituminous (asphalt) road surfaces plays a crucial role in maintaining road infrastructure integrity and enabling cost-effective maintenance strategies. However, traditional manual inspections are laborious, time-intensive, and susceptible to inconsistencies due to factors like human fatigue, varying expertise levels, and subjective assessments. To address these challenges, this research proposes CrackNet, an innovative deep learning framework that harnesses state-of-the-art computer vision and object detection techniques for accurate and computationally efficient automated crack detection in bituminous road imagery. CrackNet introduces a novel hybrid neural network architecture that seamlessly integrates a cutting-edge Vision Transformer backbone with multi-scale convolutional feature fusion modules. The Vision Transformer component excels at capturing long-range structural dependencies and global contextual information, while the multi-scale fusion modules adeptly combine fine-grained crack details across various spatial resolutions. This unique design enables CrackNet to holistically model intricate crack topologies while preserving localized characteristics and intricate details. To further bolster robustness and generalization capabilities across diverse real-world scenarios, CrackNet incorporates selfsupervised pre-training techniques that leverage unlabeled data and unsupervised pretext tasks. These strategies allow CrackNet to learn rich visual representations tailored specifically for crack detection. Additionally, an extensive data augmentation pipeline is employed, encompassing geometric, photometric, and adversarial transformations, to enhance model invariance to varying imaging conditions and environmental factors. The accuracy achieved by the newly proposed approach surpasses that of current state-of-the-art methodologies, reaching an impressive 97.8%.
Key Words
bituminous pavements; deep learning; multi-scale fusion; object detection; road crack detection; selfsupervised learning; vision transformers
Address
(1) K.A. Vinodhini:
Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College (An Autonomous Institution), India;
(2) K.R. Aswin Sidhaarth:
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India;
(3) K.A. Varun Kumar:
Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur - 603203, India.
Abstract
Waste of all kinds, including hazardous and non-hazardous waste, biodegradable and non-biodegradable waste, is disposed of in landfills. The separation of leachate and the use of covers to protect the ground from pollution are two of the most crucial factors that must be taken into account in a landfill. An adsorbent property is being investigated for recycled coarse aggregate (RCA), a byproduct of construction and demolition waste (C&D). Two different studies are conducted with various chemicals to determine the suitable adsorbent for RCA as filtrate material. The first study was conducted with 6 different chemicals which was observed for 20 days and the second was done for 45 days with the aid of adsorbents such sodium chloride, zeolite, and activated carbon at 3 g, 5 g and 7 g each which was shortlisted from the first study. Paper pulp, adsorbents, unfiltered leachate, and an RCA sample with a size range of 6 mm were used in a pilot experiment. It was observed that adding RCA to the mix enhanced its performance. The study's conclusions show that sodium chloride, zeolite, and activated carbon have all steadily improved over time. However, the sample's pH has moved from an acidic range to a basic range, and its color and turbidity show less responsiveness. Even if the color and turbidity show less responsiveness, this was detected to ascertain whether there have been any changes in the color or turbidity of the sample that has been taken, more investigation will be done in the future.
Abstract
In order to improve the oversight, security, and upkeep of smart city infrastructures, this article investigates the potential for combining IoT with structural health monitoring (SHM) systems and cutting-edge ML methods. The suggested solution overcomes the shortcomings of conventional monitoring methods by enhancing the real-time gathering and analysis of data on structural integrity through the use of sensors powered by the Internet of Things (IoT) and deep learning (DL) algorithms. The approach achieves over 90% accuracy in forecasting structural health post-seismic events, demonstrating high prediction accuracy with up to 93,500 data points analysed for seismic response models of reinforced concrete (RC) structures. Moreover, cloud computing allows for effective data storage and remote access, guaranteeing that steps are taken promptly to ensure the safety of urban infrastructure. These advancements lay the groundwork for smart city solutions that are scalable, efficient, and dependable; they improve sustainability and resilience by using cutting-edge SHM and IoT technology.
