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20 pages, 2292 KiB

Open AccessArticle

Special Length Priority Optimization Model: Minimizing Wall Rebar Usage and Cutting Waste

by Dong-Jin Kim, Lwun Poe Khant, Daniel Darma Widjaja and Sunkuk Kim

Buildings 2024, 14(1), 290; https://doi.org/10.3390/buildings14010290 - 21 Jan 2024

Cited by 1 | Viewed by 2262

Abstract

The production of steel rebar is an energy-intensive process that generates CO₂ emissions. In construction, waste is generated by cutting stock-length rebar to the required lengths. The reduction rate achieved in most previous studies was limited due to adherence to lap splice [...] Read more.

The production of steel rebar is an energy-intensive process that generates CO₂ emissions. In construction, waste is generated by cutting stock-length rebar to the required lengths. The reduction rate achieved in most previous studies was limited due to adherence to lap splice positions mandated by building codes and the use of stock-length rebar. A previous study demonstrated a significant reduction in rebar usage and cutting waste, approaching zero, upon optimizing the lap splice position, reducing the number of splices, and utilizing special-length rebar. However, the reference length used to determine the special-length rebar was not clearly optimized. This study proposes a special length priority optimization model to minimize wall rebar usage and waste by reducing the number of splices while simultaneously ensuring an optimal reference length. The proposed model was validated using a case study wall with a standard hook anchorage at the top of the wall reinforcement. The optimization model reduced rebar cutting waste to 0.18% and decreased rebar usage from the original design by 16.16%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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18 pages, 1761 KiB

Open AccessArticle

A Memetic Algorithm for the Solution of the Resource Leveling Problem

by Mehdi Iranagh, Rifat Sonmez, Tankut Atan, Furkan Uysal and Önder Halis Bettemir

Buildings 2023, 13(11), 2738; https://doi.org/10.3390/buildings13112738 - 30 Oct 2023

Cited by 1 | Viewed by 1601

Abstract

In this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. [...] Read more.

In this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. The main objective of the proposed algorithm is to integrate complementary strengths of different optimization methods and incorporate the individual learning as a separate process for achieving a successful optimization method for the RLP. The performance of the MA is compared with the state-of-the-art leveling methods. For small instances up to 30 activities, mixed-integer linear models are presented for two leveling metrics to provide a basis for performance evaluation. The computational results indicate that the new integrated framework of the MA outperforms the state-of-the-art leveling heuristics and meta-heuristics and provides a successful method for the RLP. The limitations of popular commercial project management software are also illustrated along with the improvements achieved by the MA to reveal potential contributions of the proposed integrated framework in practice. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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26 pages, 6586 KiB

Open AccessArticle

Weight Optimization of Discrete Truss Structures Using Quantum-Based HS Algorithm

by Seungjae Lee, Junhong Ha, Sudeok Shon and Donwoo Lee

Buildings 2023, 13(9), 2132; https://doi.org/10.3390/buildings13092132 - 22 Aug 2023

Cited by 1 | Viewed by 1105

Abstract

Recently, a new field that combines metaheuristic algorithms and quantum computing has been created and is being applied to optimization problems in various fields. However, the application of quantum computing-based metaheuristic algorithms to the optimization of structural engineering is insufficient. Therefore, in this [...] Read more.

Recently, a new field that combines metaheuristic algorithms and quantum computing has been created and is being applied to optimization problems in various fields. However, the application of quantum computing-based metaheuristic algorithms to the optimization of structural engineering is insufficient. Therefore, in this paper, we tried to optimize the weight of the truss structure using the QbHS (quantum-based harmony search) algorithm, which combines quantum computing and conventional HS (harmony search) algorithms. First, the convergence performance according to the parameter change of the QbHS algorithm was compared. The parameters selected for the comparison of convergence performance are QHMS, QHMCR, QPAR,

ϵ

, and

θ_{r}

. The selected parameters were compared using six benchmark functions, and the range for deriving the optimal convergence performance was found. In addition, weight optimization was performed by applying it to a truss structure with a discrete cross-sectional area. The QbHS algorithm derived a lower weight than the QEA (quantum-inspired evolutionary algorithm) and confirmed that the convergence performance was better. A new algorithm that combines quantum computing and metaheuristic algorithms is required for application to various engineering problems, and this effort is essential for the expansion of future algorithm development. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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27 pages, 4073 KiB

Open AccessArticle

BIM-Based Automated Code Compliance Checking System in Malaysian Fire Safety Regulations: A User-Friendly Approach

by Aimi Sara Ismail, Kherun Nita Ali, Noorminshah A. Iahad, Mukhtar A. Kassem and Najib Taher Al-Ashwal

Buildings 2023, 13(6), 1404; https://doi.org/10.3390/buildings13061404 - 29 May 2023

Cited by 5 | Viewed by 3793

Abstract

Developing automated code compliance checking systems is becoming increasingly complex—to the extent of challenging the implementation of these systems. This paper addresses the need to develop an automated system that prioritises user accessibility. Accordingly, the study aims to develop a system through a [...] Read more.

Developing automated code compliance checking systems is becoming increasingly complex—to the extent of challenging the implementation of these systems. This paper addresses the need to develop an automated system that prioritises user accessibility. Accordingly, the study aims to develop a system through a semi-automated rule translation process and the utilisation of BIM models in native file format. A total of 256 fire safety clauses in Malaysian regulations were translated through logic-based approaches (classification technique, decomposition through semantic mark-up method, and interview method), which further assisted in identifying the necessary BIM properties. A visual programming language was then utilised to demonstrate the proof-of-concept prototype. The classification technique and semantic mark-up method were established and structured in this study by developing a framework and flowchart to provide specific guidelines for formalizing the clauses. The semi-automated translation process encouraged the participation of relevant regulatory experts and provided more user accessibility compared to existing studies. This study also offered more practicality for designers to employ the system by utilizing native BIM model data representation. High mean scores ranging from 4.09 to 4.96 were obtained for the validation process, which affirmed the feasibility of the BIM-based Automated System for Malaysian Code Compliance Checking (BIMSMACC) to assist designers. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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21 pages, 2083 KiB

Open AccessArticle

The Role of Renewable Energy as a ‘Green Growth’ Strategy for the Built Environment

by Ali A. Gorji and Igor Martek

Buildings 2023, 13(5), 1356; https://doi.org/10.3390/buildings13051356 - 22 May 2023

Cited by 7 | Viewed by 3198

Abstract

Green growth has emerged as a strategy for addressing environmental concerns while also promoting economic development. This study assesses the impact of renewable energy technologies and policies on green growth in the built environment. It investigates 20 developed and 20 developing countries from [...] Read more.

Green growth has emerged as a strategy for addressing environmental concerns while also promoting economic development. This study assesses the impact of renewable energy technologies and policies on green growth in the built environment. It investigates 20 developed and 20 developing countries from 2010 to 2021. Panel data estimators such as generalized least squares and generalized method of moments are employed. The results reveal that the contribution of renewable energy sectors to green growth varies between developed and developing countries. In developed countries, solar, wind, and biomass capacities have facilitated green growth, while hydroelectric capacities have not. By contrast, in developing countries, wind capacity has not been effective, while other sectors show a positive contribution. The study also confirms the criticality of judicious renewable energy policies in stimulating investment and technological innovation required for a sustainable built environment. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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18 pages, 4936 KiB

Open AccessArticle

Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models

by Jiajun Ren and Xianbin Sun

Buildings 2023, 13(5), 1242; https://doi.org/10.3390/buildings13051242 - 9 May 2023

Cited by 8 | Viewed by 2181

Abstract

The determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation [...] Read more.

The determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation with field tests. In recent years, artificial neural networks (ANN) have been successfully applied to various types of complex issues in geotechnical engineering, among which the back-propagation (BP) method is a relatively mature and widely used algorithm. However, it has inevitable shortcomings, resulting in large prediction errors and other issues. Based on this situation, this study was designed to accomplish two tasks: firstly, using the genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the BP network. On this basis, the two optimization algorithms were improved to enhance the performance of the two optimization algorithms. Then, an adaptive genetic algorithm (AGA) and adaptive particle swarm optimization (APSO) were used to optimize a BP neural network to predict the ultimate bearing capacity of the pile foundation. Secondly, to test the performance of the two optimization models, the predicted results were compared and analyzed in relation to the traditional BP model and other network models of the same type in the literature based on the three most common statistical indicators. The models were evaluated using three common evaluation metrics, namely the coefficient of determination (R²), value account for (VAF), and the root mean square error (RMSE), and the evaluation metrics for the test set were obtained as AGA-BP (0.9772, 97.8348, 0.0436) and APSO-BP (0.9854, 98.4732, 0.0332). The results show that compared with the predicted results of the BP model and other models, the test set of the AGA-BP model and APSO-BP model achieved higher accuracy, and the APSO-BP model achieved higher accuracy and reliability, which provides a new method for the prediction of the ultimate bearing capacity of pile foundations. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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23 pages, 1535 KiB

Open AccessArticle

Civil Engineering Standard Measurement Method Adoption Using a Structural Equation Modelling Approach

by Siti Asmiza Muzafar, Kherun Nita Ali, Mukhtar A. Kassem and Muhamad Azry Khoiry

Buildings 2023, 13(4), 963; https://doi.org/10.3390/buildings13040963 - 4 Apr 2023

Cited by 2 | Viewed by 4274

Abstract

The adoption of a standardized technique of measuring in civil construction projects is influenced both by the drivers and the strategies used, particularly in emerging nations such as Malaysia. So, the authors of this study used structural equation modeling and the PLS-SEM technique [...] Read more.

The adoption of a standardized technique of measuring in civil construction projects is influenced both by the drivers and the strategies used, particularly in emerging nations such as Malaysia. So, the authors of this study used structural equation modeling and the PLS-SEM technique to inquire into the connection between the driver and strategy elements of the adoption. Quantity surveyors at quantity surveying consultancy companies using the standard measurement technique were polled using a questionnaire. Using the PLS-SEM technique provided by the SmartPLS 3 software, a hierarchical model was created to determine the components and their impacts on the adoption of the measuring method. The results indicated that all classes considerably influence the adoption of the standard technique of assessment, but the barrier factors had the most impact. The adoption of a standardized technique of measuring was significantly impacted by the driver and strategy elements. The coefficient of determination (R-squared value) of 0.400 indicates that the dependent variable(s) can be explained by the predictor variable(s) in the model. Moreover, Q² is significantly different from zero, suggesting that endogenous latent components may be predicted by the conceptual model. Because of its high explanatory power, the created model has given a goodness-of-fit (GoF) index of 0.214. This means that the model adequately represents the link between the variables that affect measuring technique adoption and the effects of these factors. The first stage in determining what motivates people to utilize the most up-to-date standardized measurement approach in civil engineering construction projects is to develop a research model of the variables and to explain the connection between the driver and strategy factors on standard adoption. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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30 pages, 3447 KiB

Open AccessArticle

A BIA-Based Quantitative Framework for Built Physical Asset Criticality Analysis under Sustainability and Resilience

by Mohsen Aghabegloo, Kamran Rezaie, S. Ali Torabi and Seyed Mohammad Khalili

Buildings 2023, 13(1), 264; https://doi.org/10.3390/buildings13010264 - 16 Jan 2023

Cited by 9 | Viewed by 3746

Abstract

Asset-intensive industries, such as the construction industry, have experienced major catastrophes that have led to significant operational disruptions. Physical asset failure has been the primary cause of these disruptions. Therefore, implementing proper asset management plans, including continuity plans, is crucial for the business [...] Read more.

Asset-intensive industries, such as the construction industry, have experienced major catastrophes that have led to significant operational disruptions. Physical asset failure has been the primary cause of these disruptions. Therefore, implementing proper asset management plans, including continuity plans, is crucial for the business continuity of companies active in these industries. However, companies often face severe resource limitations when implementing these plans for all of their physical assets. Therefore, those critical physical assets that are vital for providing their key products should be identified. Moreover, sustainability and resilience are inseparable parts of organizations’ strategies, including strategic asset management plans. Therefore, any proposed ranking methodology for physical asset prioritization should encompass sustainability and resilience measures to ensure its practicality. This paper proposes a novel framework for physical asset criticality analysis based on the so-called business impact analysis to ensure the continuity of providing products/services through the continuity of physical assets. A hybrid fuzzy BWM-TOPSIS method is first applied to identify the key products. Then, a hybrid fuzzy DEMATEL-Bayesian network is applied based on proper sustainability and resilience factors to determine the critical physical assets, while interdependencies among these factors are well captured. The normalized expected asset criticality index is defined to guide managers in taking appropriate directions while developing asset management plans. A case study of a gas company is provided to show the applicability of the proposed decision model. The data needed for each step of the framework is gathered through experts’ judgments, historical data available on the sites, or quantitative risk assessment scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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37 pages, 83870 KiB

Open AccessArticle

On the Thermal Environmental Quality of Typical Urban Settlement Configurations

by Hamed Reza Heshmat Mohajer, Lan Ding, Dionysia Kolokotsa and Mattheos Santamouris

Buildings 2023, 13(1), 76; https://doi.org/10.3390/buildings13010076 - 28 Dec 2022

Cited by 3 | Viewed by 2205

Abstract

Urban overheating and energy imbalances are severe environmental concerns. The role of urban sprawl patterns in the formation of Heat Island has recently absorbed the researchers’ interest. The research focuses on metropolitan areas with a range of urban typologies. However, there still is [...] Read more.

Urban overheating and energy imbalances are severe environmental concerns. The role of urban sprawl patterns in the formation of Heat Island has recently absorbed the researchers’ interest. The research focuses on metropolitan areas with a range of urban typologies. However, there still is a knowledge gap in how UHI responds to different urban typologies. The interaction between urban configurations and heat island characteristics is explored in Sydney. A combination of terrestrial surveys and modelling techniques was implemented, and results were extracted based on simulation results. The Urban Taskforce Australia suggested the applied categorization methods that follow Stewart and Oke’s Local Climate Zones (LCZs) scheme. We assessed eleven urban designs on ambient air temperature, wind characteristics, heat intensity, and outdoor thermal comfort over three summer days. We correlated results to density and the built-up ratio in all configurations and found that the maximum configurational impact on the heat island reached 2.33 °C. Configurations with a built-up ratio between 0.37 to 0.5 present a sharp downward trend in the average wind speed value and indicate a minimum with a built-up ratio of 0.63. Wind maps present an increase in layouts with built-up ratios of 0.23 to 0.37, whereas they decreased with built-up ratios of higher than 0.43. The average temperature decrease in high-rise compact configurations was 1.12 °C per hour. This record is substantially higher than its open counterparts. The study showed the importance of urban configuration on thermal environmental quality. In addition, implementing appropriate urban design parameters is vital to mitigate heat islands and improve environmental thermal comfort in urban areas. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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20 pages, 4616 KiB

Open AccessArticle

Innovative Design and Execution Model for Improving Productivity of Interior Prefabricated Commercial Wall Assemblies

by Andrew Rener, Aslihan Karatas and Benjamin Videan

Buildings 2023, 13(1), 68; https://doi.org/10.3390/buildings13010068 - 28 Dec 2022

Cited by 3 | Viewed by 3128

Abstract

Field productivity of building trades is the focus of prefabricated construction practitioners as a path to greater profitability and competitiveness in the marketplace. Construction firms are struggling to meet the demand of the marketplace due to shortages of skilled workers and flat to [...] Read more.

Field productivity of building trades is the focus of prefabricated construction practitioners as a path to greater profitability and competitiveness in the marketplace. Construction firms are struggling to meet the demand of the marketplace due to shortages of skilled workers and flat to declining productivity. Human capital and productivity challenges are affecting the ability to both acquire new work and complete the work under contract. This study focuses on the development of an innovative model that defines a process for the design, project site preconstruction planning phase, and fabrication of interior prefabricated wall components that improves onsite productivity. The developed model was tested and implemented in a case study of a single project comprised of four identical buildings located on a singular jobsite while utilizing both traditional and model approaches. The results verify that the productivity model developed in this study is capable of reducing on-site labor hours and, therefore, improving field productivity compared to traditional methods. The application of the model saved between 7–23% man-hours compared to the traditional methods and beat the estimate by 17%. Practitioners and researchers are both incentivized to explore, develop, and implement novel methodologies to address the human capital shortage that is facing the construction industry. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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18 pages, 6642 KiB

Open AccessArticle

A Novel Path Generation Approach for Robotic Spatial Printing of Branching Geometry

by Xinyu Shi, Yuan Liang, Tyson Keen Phillips, Haining Zhou, Da Wan, Weijiu Cui and Weijun Gao

Buildings 2022, 12(12), 2247; https://doi.org/10.3390/buildings12122247 - 16 Dec 2022

Cited by 1 | Viewed by 3059

Abstract

Although robotic spatial printing (RSP) has demonstrated a new way of fabricating building components with a good stiffness-to-weight ratio, the complexity of the applied geometries is still limited. Among them are branching geometries, which refer to the bio-inspired branching structures (BIBSs) in the [...] Read more.

Although robotic spatial printing (RSP) has demonstrated a new way of fabricating building components with a good stiffness-to-weight ratio, the complexity of the applied geometries is still limited. Among them are branching geometries, which refer to the bio-inspired branching structures (BIBSs) in the building industry. This paper presents a cutting-edge approach to tackle this bottleneck problem, in which we propose an automated printing path generation (APPG) approach for the RSP of branching geometries, including an original hierarchical framework of printing node permutations and a linear workflow that incorporates five core algorithms: the heat method, graph generation, graph traversal, curve adjustment, and lattice generation. Through the execution of this workflow, a lattice structure and its corresponding printing path can be generated. This work is validated by the simulation of three prototypes: two-branch geometry, multi-branch geometry, and multi-level-branch geometry. Printing expenses are compared with each of the related algorithms to validate the efficiency of this proposed approach. Along with the appropriate APPG solutions, an analytical tool for topological type is also presented in this paper. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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29 pages, 5847 KiB

Open AccessArticle

A Comparison of Different Machine Learning Algorithms in the Classification of Impervious Surfaces: Case Study of the Housing Estate Fort Bema in Warsaw (Poland)

by Janusz Sobieraj, Marcos Fernández and Dominik Metelski

Buildings 2022, 12(12), 2115; https://doi.org/10.3390/buildings12122115 - 1 Dec 2022

Cited by 4 | Viewed by 1870

Abstract

The aim of this study is to extract impervious surfaces and show their spatial distribution, using different machine learning algorithms. For this purpose, geoprocessing and remote sensing techniques were used and three classification methods for digital images were compared, namely Support Vector Machines [...] Read more.

The aim of this study is to extract impervious surfaces and show their spatial distribution, using different machine learning algorithms. For this purpose, geoprocessing and remote sensing techniques were used and three classification methods for digital images were compared, namely Support Vector Machines (SVM), Maximum Likelihood (ML) and Random Trees (RT) classifiers. The study area is one of the most prestigious and the largest housing estates in Warsaw (Poland), the Fort Bema housing complex, which is also an exemplary model for hydrological solutions. The study was prepared on the Geographic Information System platform (GIS) using aerial optical images, orthorectified and thus provided with a suitable coordinate system. The use of these data is therefore supported by the accuracy of the resulting infrared channel product with a pixel size of 0.25 m, making the results much more accurate compared to satellite imagery. The results of the SVM, ML and RT classifiers were compared using the confusion matrix, accuracy (Root Mean Square Error /RMSE/) and kappa index. This showed that the three algorithms were able to successfully discriminate between targets. Overall, the three classifiers had errors, but specifically for impervious surfaces, the highest accuracy was achieved with the SVM classifier (the highest percentage of overall accuracy), followed by ML and RT with 91.51%, 91.35% and 84.52% of the results, respectively. A comparison of the visual results and the confusion matrix shows that although visually the RT method appears to be the most detailed classification into pervious and impervious surfaces, the results were not always correct, e.g., water/shadow was detected as an impervious surface. The NDVI index was also mapped for the same spatial study area and its application in the evaluation of pervious surfaces was explained. The results obtained with the GIS platform, presented in this paper, provide a better understanding of how these advanced classifiers work, which in turn can provide insightful guidance for their selection and combination in real-world applications. The paper also provides an overview of the main works/studies dealing with impervious surface mapping, with different methods for their assessment (including the use of conventional remote sensing, NDVI, multisensory and cross-source data, ‘social sensing’ and classification methods such as SVM, ML and RT), as well as an overview of the research results. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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15 pages, 2494 KiB

Open AccessArticle

Estimating the Bond Strength of FRP Bars Using a Hybrid Machine Learning Model

by Ran Li, Lulu Liu and Ming Cheng

Buildings 2022, 12(10), 1654; https://doi.org/10.3390/buildings12101654 - 11 Oct 2022

Cited by 6 | Viewed by 1714

Abstract

Although the use of fiber-reinforced plastic (FRP) rebars instead of mild steel can effectively avoid rebar corrosion, the bonding performance gets weakened. To accurately estimate the bond strength of FRP bars, this paper proposes a particle swarm optimization-based extreme learning machine model based [...] Read more.

Although the use of fiber-reinforced plastic (FRP) rebars instead of mild steel can effectively avoid rebar corrosion, the bonding performance gets weakened. To accurately estimate the bond strength of FRP bars, this paper proposes a particle swarm optimization-based extreme learning machine model based on 222 samples. The model used six variables including the bar position (P), bar surface condition (SC), bar diameter (D), concrete compressive strength (f_c), the ratio of the bar depth to the bar diameter (L/D), and the ratio of the concrete protective layer thickness to the bar diameter (C/D) as input features, and the relative importance of the input parameters was quantified using a sensitivity analysis. The results showed that the proposed model can effectively and accurately estimate the bond strength of the FRP bar with R² = 0.945 compared with the R² = 0.926 of the original ELM model, which shows that the model can be used as an auxiliary tool for the bond performance analysis of FRP bars. The results of the sensitivity analysis indicate that the parameter L/D is of the greatest importance to the output bond strength. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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18 pages, 2648 KiB

Open AccessArticle

Optimization of the Stacking Plans for Precast Concrete Slab Based on Assembly Sequence

by Yiquan Zou, Qin Gao and Shuqiang Wang

Buildings 2022, 12(10), 1538; https://doi.org/10.3390/buildings12101538 - 26 Sep 2022

Cited by 4 | Viewed by 4937

Abstract

Precast concrete (PC) slabs are widely used in the assembly of concrete residential buildings. The PC slabs are manufactured at the factory and then arranged in stacks for transport to the construction site for assembly. Currently, optimization of the stacking plans for PC [...] Read more.

Precast concrete (PC) slabs are widely used in the assembly of concrete residential buildings. The PC slabs are manufactured at the factory and then arranged in stacks for transport to the construction site for assembly. Currently, optimization of the stacking plans for PC slabs focuses on yard-space utilization and transportation efficiency and rarely considers the assembly sequence; secondary sequencing of prefabricated elements is required during construction to meet the lifting scheme, which leads to increased construction preparation time and risk of worker injury. To enable stacking crews to generate stacking plans rapidly and systematically to improve the on-site lifting efficiency of the components, this paper proposes a storage-location allocation model with two objectives: reduce secondary-sorting workload and increase stacking stability for PC slabs. At the same time, it must match the characteristics of the problem. To prevent the solution from falling into the local optimum during the evolution of the particle swarm optimization algorithm, we introduce an elitist learning strategy, which can improve the solutions when the group converges. Finally, we verify our allocation model and optimization algorithm through example simulations. The simulation results show that, compared with the traditional method, the stacking plans generated by this method have a lower secondary-sorting workload and higher stacking stability when using the same number of storage racks. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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17 pages, 3318 KiB

Open AccessArticle

Application of LightGBM Algorithm in the Initial Design of a Library in the Cold Area of China Based on Comprehensive Performance

by Yihuan Zhou, Wanjiang Wang, Ke Wang and Junkang Song

Buildings 2022, 12(9), 1309; https://doi.org/10.3390/buildings12091309 - 26 Aug 2022

Cited by 12 | Viewed by 3311

Abstract

The proper application of machine learning and genetic algorithms in the early stage of library design can obtain better all-around building performance. The all-around performance of the library, such as indoor temperature, solar radiation, indoor lighting, etc., must be fully considered in the [...] Read more.

The proper application of machine learning and genetic algorithms in the early stage of library design can obtain better all-around building performance. The all-around performance of the library, such as indoor temperature, solar radiation, indoor lighting, etc., must be fully considered in the initial design stage. Aiming at building performance optimization and based on the method of “generative design”, this paper constructs the library’s comprehensive performance evaluation workflow and rapid prediction combined with the LightGBM algorithm. A library in a cold region of China is taken as the research object to verify its application. In this study, 5000 scheme samples generated in the iterative genetic optimization process were taken as data sets. The LightGBM algorithm was used to classify and predict design schemes, with a precision of 0.78, recall rate of 0.93, and F₁-Score of 0.851. This method can help architects to fully exploit the optimization potential of the building’s all-around performance in the initial stage of library design and ensure the timely interaction and feedback between design decisions and performance evaluation. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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25 pages, 6227 KiB

Open AccessArticle

An Evolutionary Neuro-Fuzzy-Based Approach to Estimate the Compressive Strength of Eco-Friendly Concrete Containing Recycled Construction Wastes

by Ali Ashrafian, Naser Safaeian Hamzehkolaei, Ngakan Ketut Acwin Dwijendra and Maziar Yazdani

Buildings 2022, 12(8), 1280; https://doi.org/10.3390/buildings12081280 - 21 Aug 2022

Cited by 17 | Viewed by 2365

Abstract

There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D waste, especially in concretes [...] Read more.

There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D waste, especially in concretes used in construction, further environmental damage can be prevented. By using these concretes, energy consumption and environmental impacts of concrete production can be reduced. The behaviour of these types of concrete in laboratories has been extensively studied, but reliable methods for estimating their behaviour based on the available data are required. Consequently, this research proposes a hybrid intelligent system, Fuzzy Group Method of Data Handling (GMDH)–Horse herd Optimisation Algorithm (HOA), for predicting one of the most important parameters in concrete structure design, compressive strength. In order to avoid uncertainty in the modelling process, crisp input values were converted to Fuzzy values (Fuzzification). Next, using Fuzzy input variables, the group method of data handling is used to predict the compressive strength of recycled aggregate concrete. The HOA algorithm is one of the newest metaheuristic algorithms being used to optimise the Fuzzy GMDH structure. Several databases containing experimental mix design records containing mixture components are gathered from published documents for compressive strength to assess the accuracy and reliability of the proposed hybrid Fuzzy-based model. Compared to other original approaches, the proposed Fuzzy GMDH model with the HOA optimiser outperformed them in terms of accuracy. A Monte Carlo simulation is also employed for uncertainty analysis of the empirical, standalone, and hybridised models in order to demonstrate that the evolutionary Fuzzy-based approach has less uncertainty than the standalone methods when simulating compressive strength. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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27 pages, 52032 KiB

Open AccessArticle

Developing Heat Mitigation Strategies in the Urban Environment of Sydney, Australia

by Hamed Reza Heshmat Mohajer, Lan Ding and Mattheos Santamouris

Buildings 2022, 12(7), 903; https://doi.org/10.3390/buildings12070903 - 25 Jun 2022

Cited by 11 | Viewed by 3998

Abstract

Heat island effects raise the ambient air temperature in metropolitan areas by 4–5 degrees Celsius and can reach 10 degrees Celsius at their maximum. This phenomenon magnifies cities’ energy difficulties while reducing comfort. Mitigation strategies have been developed and recommended to deal with [...] Read more.

Heat island effects raise the ambient air temperature in metropolitan areas by 4–5 degrees Celsius and can reach 10 degrees Celsius at their maximum. This phenomenon magnifies cities’ energy difficulties while reducing comfort. Mitigation strategies have been developed and recommended to deal with the issue. Methods to increase albedo and the utilisation of vegetation appear to be the most promising, with a reasonably high heat island reduction capacity. This paper examines the heat mitigation techniques and their effectiveness under Sydney’s climate conditions and compares strategies. We implement two perspectives, namely urban greening (green roofs, green pavements) and albedo (street, roof), and characterise urban surface structures, and Envi-met software is employed for our simulation method. Mitigation strategies show a cooling potential of 4.1 °C in temperature along this precinct during the heatwave period. Scenarios that increase high-albedo material on the road, pavements and rooftops and full mitigation show the maximum cooling potential. The mitigation strategies have higher predicted cooling potential on the peak ambient temperature, up to 1.18 °C, while having no or little impact on minimum ambient temperature. The outdoor thermal comfort based on PMV indices varies between a minimum of −0.33 in scenario seven in large layout areas to 3. However, the mitigation scenario presents more acceptable outdoor thermal comfort, but large layouts are predicted to have a hot condition. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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19 pages, 1488 KiB

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Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review

by Sergio Zabala-Vargas, María Jaimes-Quintanilla and Miguel Hernán Jimenez-Barrera

Buildings 2023, 13(12), 2944; https://doi.org/10.3390/buildings13122944 - 25 Nov 2023

Cited by 6 | Viewed by 6914

Abstract

The high volume of information produced by project management and its quality have become a challenge for organizations. Due to this, emerging technologies such as big data, data science and artificial intelligence (ETs) have become an alternative in the project life cycle. This [...] Read more.

The high volume of information produced by project management and its quality have become a challenge for organizations. Due to this, emerging technologies such as big data, data science and artificial intelligence (ETs) have become an alternative in the project life cycle. This article aims to present a systematic review of the literature on the use of these technologies in the architecture, engineering, and construction industry. A methodology of collection, purification, evaluation, bibliometric, and categorical analysis was used. A total of 224 articles were found, which, using the PRISMA method, finally generated 57 articles. The categorical analysis focused on determining the technologies used, the most common methodologies, the most-discussed project management areas, and the contributions to the AEC industry. The review found that there is international leadership by China, the United States, and the United Kingdom. The type of research most used is quantitative. The areas of knowledge where ETs are most used are Cost, Quality, Time, and Scope. Finally, among the most outstanding contributions are as follows: prediction in the development of projects, the identification of critical factors, the detailed identification of risks, the optimization of planning, the automation of tasks, and the increase in efficiency; all of these to facilitate management decision making. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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24 pages, 4605 KiB

Open AccessReview

Future City, Digital Twinning and the Urban Realm: A Systematic Literature Review

by Zaid O. Saeed, Francesco Mancini, Tanja Glusac and Parisa Izadpanahi

Buildings 2022, 12(5), 685; https://doi.org/10.3390/buildings12050685 - 20 May 2022

Cited by 24 | Viewed by 6141

Abstract

Digitalisation and the future city paradigm are becoming a trend in recent research and practices. Literature discusses digitalisation and its applications as the main gear in the transformation to the ideal future city vision. Yet, the concept of digitalisation is articulated in many [...] Read more.

Digitalisation and the future city paradigm are becoming a trend in recent research and practices. Literature discusses digitalisation and its applications as the main gear in the transformation to the ideal future city vision. Yet, the concept of digitalisation is articulated in many interpretations and presented in different applications in the built environment. One emerging application is digital twinning. Literature envisions the potential of digital twinning applications in the urban realm and discusses the cognitive city model and its implications on the future of our cities, its urban realm and the built environment in general. With the evolving themes on the ideal future city model, this systematic review tackles the following questions: what are the key motives and drivers of the future city paradigm; what is a city digital twin; and what are their expected applications. Additionally, how literature envisions the definition of the city users and their experience in the urban realm of the city of the future. This review article explores related literature on the themes of future city model, digital urban realm, digital twinning and city users. The main findings are: identifying key gears of the future city model in literature, exploring city digital twin conceptualization and applications and discussing concepts on the definition of city user and user experience in the city of the future. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Construction Industry)

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