Construction Process Research Articles

Revitalization of urban industrial heritage from a perspective of spatial production theory: the case study of “Old market” project

ABSTRACT The renewal of urban industrial heritage brings many benefits, meanwhile facing challenges, and there is a lack of in-depth and specific case study analysis. Based on spatial production theory, this paper takes an iconic project as an example to explore its construction process and reveal the spatial and social changes it triggers. This case study investigates the changes in physical and social spaces resulting from the shift in the subjects of spatial production. By analyzing document data, spatial data, and field data, the following key findings and conclusions are obtained: 1) Capital led the regeneration process, transformed decaying factory buildings into consumer space, and activated the economic vitality of the area. 2) The construction of the physical space changed the spatial layout, structure and function that attracted the creative community and young visitors, changing daily lives of the space users. 3) However, the neglect of industrial culture and participation of nearby residents are obvious challenges. The results indicate that using spatial production as a theoretical analysis framework helps us fully understand the challenges faced in the renewal of urban industrial heritage projects. This study can provide a reference for small-scale urban heritage renewal projects, facilitating accurate decision-making.

Digitalization and tender evaluation in construction projects: a BIM-integrated MCDA approach supported by Macbeth

The tender evaluation process in the procurement for construction works, aligned with regulations such as the European Directive 2014/24/EU, faces various constraints, increasingly acknowledging the impact of digitalization, particularly through Building Information Modeling (BIM). Therefore, a thorough BIM-integrated multi-criteria decision analysis approach is vital to fairly select the best contractor for the job and ensure project success. Despite its significance, the literature remains scarce, particularly in addressing BIM-related and value-focused constraints for construction tender evaluation. This research introduces a comprehensive and value-focused method integrating BIM criteria through Multicriteria Decision Analysis (MCDA) supported by the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), which is applicable to both the public and private sectors, serving as a reference guide for contracting authorities to assess and select the best tender for BIM-based construction works contracts. For this purpose, the following methodology was adopted: 1) conducting an extensive literature review; 2) carrying out interviews with construction experts to identify BIM-integrated evaluation criteria and performance descriptors; 3) creating an adapted MCDA construction tender evaluation framework supported by MACBETH; 4) performing a real-world case study to demonstrate the proposed evaluation framework; 5) discussing its application and limitations, along with considerations for future research. The study demonstrated the practicality of the proposed framework through a case study, showing that a value-based approach can significantly improve project outcomes compared to traditional methods. While acknowledging the potential of the BIM methodology, the impact of BIM-specific criteria on contractor selection was found to be less significant than expected. In conclusion, this work addresses current limitations, such as the absence of BIM-integrated assessment guidelines in the European Directive 2014/24/EU and ISO 19650. It outlines a value-focused MCDA approach for evaluating tenders for construction works contracts, supported by MACBETH for structuring the decision problem and building the evaluation model. The contribution to the body of knowledge lies in providing a robust framework that enhances the evaluation process in BIM-based construction works contracts and offers practical insights for contracting authorities.

Advanced Predictive Modeling of Concrete Compressive Strength and Slump Characteristics: A Comparative Evaluation of BPNN, SVM, and RF Models Optimized via PSO

This study presents the development of predictive models for concrete performance, specifically targeting the compressive strength and slump value, utilizing the quantities of individual raw materials in the concrete mix design as input variables. Three distinct machine learning approaches—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were employed to establish the prediction models independently. In the model construction process, the Particle Swarm Optimization (PSO) algorithm was integrated with cross-validation to fine-tune the hyperparameters of each model, ensuring optimal performance. Following the completion of training and modeling, a comprehensive comparison of the predictive accuracy among the three models was conducted, with the aim of selecting the most suitable model for incorporation into an optimized objective function. The findings reveal that among the chosen machine learning techniques, BPNN exhibited superior predictive capabilities for the compressive strength of concrete. Specifically, in the validation set, BPNN achieved a high correlation coefficient (R) of 0.9531 between the predicted and actual outputs, accompanied by a low Root Mean Square Error (RMSE) of 4.2568 and a Mean Absolute Error (MAE) of 2.6627, indicating a precise and reliable prediction. Conversely, for the prediction of the concrete slump value, RF outperformed the other two models, demonstrating a correlation coefficient (R) of 0.8986, an RMSE of 9.4906, and an MAE of 5.5034 in the validation set. This underscores the effectiveness of RF in capturing the complexity and variability inherent in slump behavior. Overall, this research highlights the potential of integrating advanced machine learning algorithms with optimization techniques for enhancing the accuracy and efficiency of concrete performance predictions. The identified optimal models, BPNN for compressive strength and RF for slump, can serve as valuable tools for engineers and researchers in the field of construction materials, facilitating the design of concrete mixes tailored to specific performance requirements.

Research on Temperature Control of Mass Concrete for Multi-Tower Cable-Stayed Bridge Cap during Construction

In the construction process of mass concrete structures, the large temperature gradient due to exothermic hydration makes the mass concrete highly susceptible to cracking. This paper carried out research on temperature control methods of mass concrete for the purpose of ensuring construction quality based on the construction of Fengyi cable-stayed bridge caps. Firstly, the temperature and stress change rule in the concrete pouring process of the caps was analyzed though the finite element method (FEM). Then, targeted-oriented comprehensive temperature control schemes were formulated according to the structural characteristics and construction environment of the cap, including the optimization of the material ratio, the arrangement of crack-resistant reinforcing steel, the design of a water pipe cooling scheme and reasonable maintenance. Finally, the whole bridge cap construction process using the optimized water pipe cooling solution was monitored, and the temperature gap between inside and outside the concrete satisfied the specification requirements rigorously. In the concrete demolding session, the concrete surface was smooth and no cracks were found, which indicates the temperature control scheme is reasonable and effective. The research results have reference significance for the pouring and temperature control of mass concrete for bridge caps.

COGNITIVE PROCESS IN COMPREHENSION OF SENTENCE PROPOSITIONS AMONG THE UNDERGRADUATE EFL STUDENTS

English acquired by EFL learners is categorized as interlanguage, which means that their English is developing towards native like competence. In Indonesian context, the EFL learners’ vocabulary and sentence structure mastery is insufficient. Comprehending main propositions or sentence meanings require cognitive process of both semantic and structural construction. Within such insufficient mastery of English, the researchers intend to uncover the cognitive process EFL learners applied to comprehend main propositions of complex sentences. For this purpose of the study, the researches obtained the main propositions that the learners comprehended from three complex sentences assigned to them to read. Soon after completing the comprehension, the learners were assigned to write their retrospective introspection (mental process they were experiencing during the comprehension). The two types of data were analyzed qualitatively. The research findings show that 65.24% of the EFL learners applied semantic construction, 25.24% used discourse analysis, and 9.52% applied syntactical construction. This indicated that in comprehending sentence meaning, 90.48% EFL learners relied more on words and discourse meanings rather than on sentence structure or on their combination.

METHODOLOGY OF BUILDING INFORMATION MODELING OF URBAN STREETS AND ROADS

Problem statement. New challenges in road construction require the application of modern and globally recognized best practices in the design and management of road infrastructure construction processes, involving the use of the latest Building Information Modeling (BIM) technologies. The implementation of BIM technologies necessitates conducting research to systematize information, forecast and evaluate outcomes, and develop a modeling methodology. The purpose of the article is to develop a methodology for Building Information Modeling of urban streets and roads. Research Results. The proposed methodology for Building Information Modeling is demonstrated using sections of the street and road network in the city of Dnipro. Digital terrain and site models were created in the InfraWorks software based on geospatial data from remote sensing. Conceptual models of urban streets and roads were developed. The conceptual models were exported to Civil 3D, where the terrain model was refined using ground survey data, analyzed, and used to develop route plans, longitudinal and cross profiles of streets and roads. The detailed and coordinated road design solutions in Civil 3D were exported to InfraWorks to build an integrated road infrastructure. An analysis of the spatial smoothness of routes and sight distances on roads was conducted using the building information model. Models of an overpass and bridge developed in Revit were exported to the road infrastructure model. Traffic management measures were modeled. Using the “Traffic Simulation” tools in InfraWorks, vehicle traffic flows on roads and interchanges of the studied street and road network were simulated, taking into account the actual traffic intensity. The building information models were checked for structural deficiencies using Navisworks software. Conclusion. The proposed methodology for designing urban streets and roads using BIM technology involves the step-by-step development of a digital model utilizing software for the automated design of road infrastructure with the required level of detail at each stage of the design process.

An integrated model of a prefabrication hub feasibility assessment: a Hong Kong case

Housing issues in densely populated, high-rise cities like Hong Kong place significant pressure on the local Housing Authority to devise effective housing strategies. In response to these challenges, prefabrication, promoting sustainable construction has gained traction, and addresses labour shortages, time constraints, safety concerns, and environmental impacts. However, cross-border logistics from Mainland China heighten supply chain disruptions and risks. Establishing a local prefabrication hub could mitigate these issues and streamline the construction process. There is a lack of research on standardized model solutions for prefabrication hub feasibility assessments, which is essential for objective project viability analysis. This article proposes an integrated model for assessing the feasibility of prefabrication hubs, considering financial, market performance, technical, social, and environmental aspects. The interrelationships among these five Key Performance Indicators (KPIs) were formulated as hypotheses. A questionnaire survey collected expert opinions from industry and academia on influential KPIs and their measurement items, followed by Structural Equation Modelling and interviews for hypothesis testing and validation. The assessment of market performance appears foundational, with ‘demand analysis for prefabricated components’ and ‘supply analysis’ as the most critical factors. These findings could serve as a basis for developing strategies for smoothly integrating prefabrication hubs into Hong Kong’s supply chain.

On the Formation of Auroral Spirals

AbstractThe paper contains a detailed analysis of the formation of an auroral spiral based on hitherto not published observations by the all‐sky camera in Kilpisjärvi, Northern Finland. We conclude that spirals appearing during a substorm form by a modification of the interface between tail and magnetosphere, the location of the generator current of the westward electrojet. Driven by the arriving flow bursts, this current is subject to ruptures by the appearance of a sequence of hook‐like structures. These structures can move eastward with speeds up to 3 km/s. The propagation is attributed to a constructive magnetic fracture process driven from behind by the power of the arriving flow bursts. Poleward bending and extension of a hook‐like structure, followed by a turning to the west and then equatorward, is the first step in spiral formation. It becomes the primary spiral arm, if a poleward arm grows out of weaker auroral structures, poleward and eastward of it. We suggest that the upward field‐aligned currents related to the bright spiral arms are largely balanced by adjacent downward currents. The electric fields associated with the connecting Pedersen currents are consistent with the counter‐clockwise motion. An important additional ingredient in the observed configuration is an eastward directed flow field, which is the generator of an additional upward current and possibly crucial for the spiral formation. Electric field data from literature throw confusing light on the propagation of a spiral, whether like a vessel in the ocean or by incorporating the magnetic flux ahead of it.

Digital Transformation in University Architecture: Optimizing Construction Processes and User Experience through CAMPUS 2.0 at Pontificia Universidad Javeriana

Digital Transformation in University Architecture: Optimizing Construction Processes and User Experience through CAMPUS 2.0 at Pontificia Universidad Javeriana

Fabrication Information Modeling for Closed-Loop Design and Quality Improvement in Additive Manufacturing for construction

Additive Manufacturing (AM) has emerged as a disruptive technology with the potential to revolutionize the construction industry by integrating digital design with automated manufacturing. This paper presents and extends Fabrication Information Modeling (FIM), a comprehensive framework tailored for automated manufacturing in construction. FIM facilitates the seamless integration of digital design concepts with automated manufacturing processes, enabling precise control over fabrication information and enhancing construction efficiency and quality. This paper demonstrates its potential to optimize construction processes through a detailed exploration of FIM’s capabilities, including data preparation, path planning, simulation integration, robot control, and data feedback. By enabling a circular data flow between digital modeling and manufacturing, FIM is able to bridge the gap between digital design and physical construction, revolutionizing how construction projects are conceived, planned, and executed. The paper concludes by highlighting the challenges and future research directions in advancing FIM-based construction systems, emphasizing its transformative potential in driving innovation and sustainability in the construction industry.

Balkanization vs Europeanization: conceptual deconstruction

The article is dedicated to the deconstruction of the binary opposition between the concepts of “Balkanization” and “Europeanization”. The initial phase of deconstruction entails a critical examination of conventional conceptualizations that reflect power relations between Balkans and Europe. A particular emphasis is placed on the dominant perception of balkanization as a process of fragmentation and barbarization, which is a representation commonly depicted within Western strategic discourse. As an alternative, the authentic concept of balkanization, formulated by Yugoslav avant-guardists during the early twentieth century, is analysed. The avant-guard movement Zenitism proclaimed the slogan of the Balkanization of Europe and featured the figure of Barbarogenious-Decivilizer. This approach not only represents an inversion of the Western perspective on the Balkans, but also draws upon ideas of internationalism and class struggle, deriving inspiration from the experience of the Russian revolution. The concept of Europeanization is examined in both broad and narrow senses - as a notion of modernization and as transformations caused by the institutional influence of the European Union. The concept of europeanization, positioned as a positive project and vision of a better future, stands in contrast to the Balkan past ofthe region. With such as interpretation, the successful implementation ofthe europeanization agenda would entail the complete erasure of Balkan subjectivity and identity. Europeanization is conceptualized as a process of debalkanization. In the concluding phase of deconstruction, during the stage of“reassembly”, an alternative perception ofbalkanization is put forward. In accordance with the ideas of Zenitism and the political experience ofthe Balkan peoples' struggle for independence and the establishment of a common stated, balkanization is perceived as, on the one hand, resistance and rebellion; on the other hand, as a desire to overcome borders and internationalization. Accordingly, europeanization is perceived as a process of construction of homogeneous modern nation-states. It can be argued that fragmentation is indeed a consequence and essence of europeanization.

An adaptive photovoltaic power interval prediction based on multi-objective optimization

Photovoltaic (PV) power interval prediction can provide a variation range of prediction results, which is of great significance to promoting the optimization of power grid dispatching and maintaining the stability of the power system. However, the existing PV interval prediction fails to accurately reflect error fluctuations in their construction process due to dependency on data distribution assumptions and unreasonable estimation of prediction error standard deviations (PEStds). To address these issues, we propose an adaptive interval prediction method based on multi-objective optimization. Firstly, K-means clustering is used to segment similar days. Subsequently, Convolutional Neural Network-Gated Recurrent Unit-Attention (CNN-GRU-Attention) is utilized for point prediction. Furthermore, Grey Relation Analysis (GRA) is used to screen dates similar to the target date under the same weather type, enabling a more precise estimation of prediction error fluctuations. Ultimately, the estimated values are multiplied by non-fixed parameters to construct intervals, which avoids the dependency on data distribution assumptions. Among them, the optimal values for non-fixed parameters are obtained through multi-objective optimization considering interval coverage probability, interval width, and deviation. To verify the effectiveness of the proposed model, we conduct comparative experiments on two datasets with different resolutions. The results demonstrate that the proposed model offers more flexible and higher-quality intervals. Not only does this research improve the accuracy of PV power generation interval prediction, but it also helps to promote the development of smart grid technology and improve the adaptive ability of power systems facing dynamic environments and complex data, which has an important impact on the future energy management and power market.

Leveraging Technology to Improve and Streamline Operations in the African Construction Sector: A Case-Based Approach

The advent of innovation in tools, materials and techniques has transformed the planning, management and execution of construction projects. Globally, the construction sector has been embracing digital technologies to enhance efficiency, reduce cost and ensure sustainability to overcome challenges and meet evolving demands in the industry. Using a case-based approach, this study shows instances from South Africa of how the construction process is being revolutionised through the deployment of cutting-edge technologies to improve and streamline operations. The approach provides an introductory background on how technology has affected construction with an extensive literature review providing clarifications and insights on construction activities, processes and key technological advancements, in addition to highlighting their importance. Findings from the cases revealed that the projects deployed one or more forms of project management software, such as RIB Software, Autodesk Revit, Primavera P6, Microsoft Project, Procore, and Aconex. It was further found that these deployments enhanced collaboration and communication, improved design coordination and clash detection, and enabled efficient resource allocation and project planning among stakeholders. The study concludes that construction activities play a key role in economic development. Sustainable construction practices would require the adoption and implementation of state-of-the-art technological solutions in the 4th Industrial Revolution that will aid the totality of construction management processes. As evidenced in the various projects, the effectiveness of construction management software in streamlining the construction process is a testament to the critical role of technological advancement as a driver of sustainability.

Dynamic spatial–temporal conflict quantification of construction machinery for pouring blocks in arch dams

PurposeThe spatial–temporal conflicts in the construction process of concrete arch dams are related to the construction quality and duration, especially for pouring blocks with a continuous high-strength and high-density construction process. Furthermore, the complicated construction technology and limited space resources aggravate the spatial–temporal conflicts in the process of space resource allocation and utilization, directly affecting the pouring quality and progress of concrete. To promote the high-strength, quality-preserving and rapid construction of dams and to clarify the explosion moment and influence degree of the spatial–temporal conflicts of construction machinery during the pouring process, a quantification method and algorithm for a “Conflict Bubble” (CB) between construction machines is proposed based on the “Time–Space Microelement” (TSM).Design/methodology/approachFirst, the concept of a CB is proposed, which is defined as the spatial overlap of different entities in the movement process. The subsidiary space of the entity is divided into three layered spaces: the physical space, safe space and efficiency space from the inside to the outside. Second, the processes of “creation,” “transition” and “disappearance” of the CB at different levels with the movement of the entity are defined as the evolution of the spatial–temporal state of the entity. The mapping relationship between the spatial variation and the running time of the layered space during the movement process is defined as “Time–Space” (TS), which is intended to be processed by a microelement.FindingsThe quantification method and algorithm of the CB between construction machinery are proposed based on the TSM, which realizes the quantification of the physical collision accident rate, security risk rate and efficiency loss rate of the construction machinery at any time point or time period. The risk rate of spatial–temporal conflicts in the construction process was calculated, and the outbreak condition of spatial–temporal conflict in the pouring process was simulated and rehearsed. The quantitative calculation results show that the physical collision accident rate, security risk rate and efficiency loss rate of construction machinery at any time point or time period can be quantified.Originality/valueThis study provides theoretical support for the quantitative evaluation and analysis of the spatial–temporal conflict risk in the pouring construction process. It also serves as a reference for the rational organization and scientific decision-making for pouring blocks and provides new ideas and methods for the safe and efficient construction and the scientific and refined management of dams.

Enhancing Recycled Concrete Performance by Using Chemical Activators

The need for sustainable and eco-friendly construction materials and processes is rising significantly. In these circumstances, recycling of concrete can be a lifesaver. Although, various recycling techniques have already been introduced. But none of them can fulfill the term recycle refers to. They mostly focus on merely reusing coarse aggregates (CA) or fine aggregates (FA). To come out of this, this paper is focused not only on the reuse of both coarse and fine materials (RCA, RFA) but also on introducing a new process in which those materials can be used in their old form, as recycling refers to. In this experiment, we use all kinds of materials recollected from recycled concrete (fine, coarse, recycled cement particles). Those materials are vetted into two parts. One is for recycling coarse aggregates and one is for rebuilding concrete using recovered fine, coarse, and cement particles. First, we discuss the collection and treatment system procedure of recycled demolished concrete. The second part will consist of various standardized tests for reuse and evaluation. The third part will describe the treatment needed for the materials for chemical (Using Na2SiO3, NaOH, and Na2CO3) reactivation. Finally, we will test the recycled material’s performance as concrete and establish an authentic process of recycling and reusing concrete materials. With that, we obtain the most optimum usage of recycled concrete aggregate mixing ratio Cement: CA (coarse aggregate): RCA (recycled coarse aggregate): FA (fine aggregate): RFA (recycled fine aggregate) is 1: 2: 2: 0.8: 1.2: 0.8 (With molar solution of Na2SiO3 and NaOH + KOH) and other ration of mixing with their respected properties (Compressive Strength).

Constructed encoded data based coded distributed DNN training for edge computing scenario

Deep learning in unmanned aerial vehicle (UAV) deployment encounters two problems: 1, One UAV may fail to store and execute large Deep Neural Network (DNN) model. 2, One UAV fails to accomplish real time services. One possible solution is the group of UAVs (nodes) collectively generate swarm intelligence in the form of edge computing scenario, namely in the distributed computing (DC) mode with clever arrangement, say Coded Distributed Computing (CDC). In CDC systems, the redundant computation introduced by linear coding can compensate stragglers. However, since linear property cannot pass the nonlinear activation function in Deep Neural Network (DNN) training, coding/decoding for CDC need to be applied layer by layer, which slows down the training. To avoid layer-by-layer coding/decoding, we propose a novel DNN training scheme based on constructing encoded data. This construction process lies before the training process (can be done before training without any impact on training efficiency). Based on both the original data and the newly constructed encoded data, the training phase can take advantage of the (n,k) property (Wait for the first k returned data) and hence improve the training speed. The training process does not require encoding/decoding operations, and hence significantly improves the training speed. Experimental results show that the training scheme based on constructed encoded data can achieve prediction accuracy approximating that of the centralized one and significantly reduce the latency compared to the layer-by-layer linear encoding and decoding scheme.

Impact on the environment and expected results of the highway construction process

Impact on the environment and expected results of the highway construction process

Time-Varying Stability Analysis of the Trenching Construction Process of Diaphragm Wall

The stability of underground diaphragm walls is crucial for ensuring the safety and integrity of trench excavations in geotechnical engineering. This study addresses this critical issue by proposing a novel destabilization mechanism based on a sliding body model specifically designed for diaphragm wall trenching operations. The research employs an analytical framework rooted in soil mechanics and plasticity theory, utilizing limit equilibrium analysis to develop a method for calculating the minimum required slurry density and corresponding safety factor for trench stability. The study compares two distinct approaches to slurry density computation, analyzing their sensitivity to various influencing factors. Theoretical findings are validated through multiple real-world engineering case studies. Comparative analysis demonstrates the superiority of the proposed method, particularly in assessing trench stability within clay layers. Key variables influencing the safety factor are identified, including trench length, slurry density, soil friction angle, and the relative height difference between slurry and groundwater levels. Results indicate that actual slurry densities observed in practice consistently fall within the bounds predicted by the theoretical calculations. This research contributes a valuable theoretical framework to the field of diaphragm wall construction, offering improved accuracy in stability assessments and potentially enhancing safety in geotechnical engineering projects.

Advanced Risk Assessment for Deep Excavation in Karst Regions Using Improved Dempster–Shafer and Dynamic Bayesian Networks

This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios.

Manufacturing of a 3D-printed electrode cost-effective based on carbon black and polylactic acid for detection of flunixin anti-inflammatory

This study presents the manufacturing and application of a 3D-printed sensor based on CB/PLA for detecting the anti-inflammatory drug flunixin (FXN). The proposed sensor features a simple design and accessible construction process with fast production (∼25 min), fast activation (∼400 s), low cost (∼0.044 USD), and stands out for its sensitivity (0.083 µA µmol l-1) and low detection limit (0.30 µmol l-1). Furthermore, the sensor presents allowed the determination of the FXN in real samples such as pharmaceutical formulations (tablet and injectable), tap and river water without pretreatment. The selectivity obtained was satisfactory in the presence of metal ions and organic compounds. The results showed that the use of 3D printing technology with fast surface treatment made it possible to study the anti-inflammatory behavior of FXN in a satisfactory way. Therefore, it is pertinent to use 3D technology in electrochemical analyses of potential antibiotics and other pharmaceuticals.