Construction Engineering Research Articles

Construction engineering in China under green Transition: Policy research Topic clusters and development forecasts

China’s construction engineering market is gradually transitioning towards high-quality development, driven by policies such as green and low-carbon. Identifying research topics for China’s construction engineering policy and predicting future research trends are crucial for policymakers to make informed decisions and promote the transformation of the construction industry. This study combines Latent Dirichlet Allocation (LDA) and Autoregressive Integrated Moving Average Model (ARIMA) to analyze more than 6,500 relevant literature abstracts. The LDA model identified 11 key topics, with the five hottest topics conceptualized with trend prediction. ARIMA-based trend forecasts show that these topics will continue to grow over the next two years. Energy Consumption in Construction (Topic 2) is caused by the urgency of transforming China’s construction industry. The environmental impacts of green construction (Topic 6) are due to the sustainability potential of the green construction model. The relationship between urbanization, the economy and the environment (Topic 7) was prompted by the discussion that the relationship between them is still unclear. The use of recyclable materials in construction (Topic 8) is a result of resource depletion and environmental degradation. The relationship between urbanization and ecosystems (Topic 9) was prompted by the environmental problems associated with China’s high rate of urbanization. These findings reveal the focus and development trend of construction engineering transformation in China and provide feedback to policy makers and companies from the perspective of experts and scholars. The accuracy of the predictions can be further improved in the future through data updates and multi-modeling combinations.

Team-Based Questioning Battles in Construction and Building Engineering Educational Environments: A Useful Tool for Engaging Active Learning in the Classroom

This manuscript presents a comprehensive exploration of an innovative active learning approach implemented in a Master’s Degree in Construction Engineering program. The pedagogical intervention involved a series of competitive battles among student teams, wherein each team formulated and presented multiple-choice questions to their peers. The study aims to provide a detailed account of the implementation process, assess the effectiveness of this approach, and evaluate its impact on students’ learning and motivation. The assessment was conducted through triangulation of experiences, surveys, and interviews. The results provide key insights into how Team-Based Questioning Battles can foster highly competitive environments, enhancing the learning experience, understanding, and retention of concepts, all while motivating and engaging students.

ANALISIS PENGARUH OPERATING CASH FLOW, PROFITABILITAS, SALES GROWTH, FIRM SIZE, DAN LEVERAGE TERHADAP PREDIKSI KONDISI FINANCIAL DISTRESS

The phenomenon of PKPU (Application for Insolvency and Delayed Debt Payment) on some issuers in the IDX appears to indicate financial problems (financial distress). The research was conducted to test and analyze the impact of operating cash flow, profitability, sales growth, firm size, and leverage on the prediction of financial distress conditions in sub-sector companies heavy construction and civil engineering registered in the IDX in 2020-2022. The sampling techniques used are purposive samplings with samples obtained of 60 data. The quantitative method is used in this study through an associative approach using secondary data as well as using double linear analysis with the SPSS version 22 test tool. The results of this study show that the profitability variables with proxy return on asset have a significant impact on financial distress, while the operating cash flow, sales growth, firm size, and leverage variables of proxy debt to asset ratio have no influence on financial stress. The implications of this study are expected to inform investors to always do financial analysis first before making decisions in the heavy construction and civil engineering subsectors.

Comparative study on energy efficient green building with conventional building

Abstract Green Building concept is an environmental building practice followed by the construction engineers since the years 2000s. Green building concept is the imperative of time with respect to economic, social and environment aspects. Green building techniques can mainly classify under six categories, waste management, water efficiency, energy efficiency, location, configuration and green materials, where under each technique countless solutions can be developed based on the requirements and the proposed project. Hence, this paper targets to redesign Barka Marina Mall, Oman as a green building considering building configuration, renewable energy and green materials, with the help of Building Information Modelling (BIM) technology and Photovoltaic System software as simulation tools. Aim of the present study is to redesign the Barka Marina Mall, Oman as a green building by considering the building configuration, renewable energy and green materials, with the help of BIM and Photovoltaic System software and compared the results with the conventional building. The criteria considered for the comparative analysis are, annual carbon emission, annual energy use, monthly fuel consumption and monthly energy consumption between the conventional and the green building. Based on the comparative study it is noticed that applying the green building concept has a great potential to save the nation’s resources and the environment by reducing the consumption as well as the cost.

Network analysis of contractors’ assessment factors

The effect of assessment management and selecting the appropriate contractor is growing interest in research because of ‎the change in preferences of contractors’ behaviour and the growth of competitiveness. The assessment of contractors is a continuous process that demands ongoing ‎monitoring to maintain high quality ranks. In this study, the factors behaviour of contractors' assessment for a construction engineering company and a method of topological analysis are examined to a) aid in the interpretation of existing relationships among all factors and b) find influencing factors of contractors’ assessment. The method begins with the network construction of factors considering some criteria or standards. We then propose a method of network analysis and describe the results of studies undertaken to apply the correlation network-based approach and discuss the implications of the methodology. As a result of this study, all influencing factors are involved in a category. The most influential factor is the specialized training of staff that can improve the contractors' productivity and competitiveness.

Innovative Applications and Benefit Analysis of Financial Informationization and Intelligence in the Construction Industry

This paper explores the current status of the application of financial informationization and intelligence in the construction industry, analyzing the impact on improving financial management efficiency, reducing operational costs, and enhancing decision-making support capabilities. Through a case study of Huaxin Construction Engineering Group Co., Ltd., this paper evaluates the actual benefits of intelligent technology in financial process reengineering, risk management, and strategic planning.

InerSens: A Block-Based Programming Platform for Learning Sensor Data Analytics in Construction Engineering Programs

InerSens: A Block-Based Programming Platform for Learning Sensor Data Analytics in Construction Engineering Programs

Instabilities in a two-dimensional granular fault gouge: Particle dynamics and stress fluctuations

Predicting stress fluctuations in granular media under steady-state shear loading is crucial for applications ranging from geophysical processes to construction engineering. Stress fluctuations emerge from particle rearrangement, usually enabled by frictional slip and force-chain buckling. Existing models used to predict stress fluctuations are largely phenomenological, often accounting for the force chain phenomena implicitly through the introduction of internal variables, or explicitly through assumptions of force chain mechanics. Improper consideration of particle mechanics or mesoscale effects can lead to inaccurate predictions of shear strength and instability, making it difficult to predict the onset of yielding, shear band formation, and other instabilities. Furthermore, while recent advancements in machine learning methods have established links between microscale behavior and macroscopic stress drops in granular fault gouges, their predictive capabilities are limited due to their black-box nature. To gain a deeper understanding of stress fluctuations, and ultimately predict them in a physics-informed manner, it is necessary to examine how system energetics change with stress fluctuations. In this paper, we analyze stress fluctuations in a 2D granular fault gouge loaded under quasistatic, steady-state shear. We track the flow of potential energy between force networks and understand how energy and force networks vary with stress rises and drops. We derive an analytical, dynamic force chain model from first principles to illustrate how interactions between force networks lead to the emergence of localized instability phenomena. Finally, we offer insights into how these localized instabilities ultimately enable shear stress fluctuations at the continuum scale.

Using Data Mining Applications to Analyze the Grades of Students in the Department of Construction and Civil Engineering: A Case Study at the Higher Institute of Science and Technology – Kufra

This study presents to analyze the grades of students in the department of construction and civil engineering in the department of construction and civil engineering, specializing in architectural drawing and surveying, from the Spring semester (2006-2007) until the Fall semester (2010-2011). The goal was to understand the factors influencing students' grades, as well as the lack of interest in the department due to the difficulty in obtaining grades of previous graduates. To address the issue of student data accumulation. The study also sought to explore various methods to achieve specific results and establish a digital repository of student data to support decision-makers in understanding student outcomes. The significance of the study lies in predicting and analyzing students' grades in the department of construction and civil engineering, as well as understanding students' lack of interest and identifying weaknesses in academic courses. The present study employed an analytical research methodology. The K-Means Clustering Algorithm was used for data mining to identify factors leading to academic underachievement. the study results showed that a repetition of subjects within the curriculum for architectural drawing and surveying for more than one academic semester or course. The study results also showed that the differences in students' grades between the architectural drawing and surveying branches, with the architectural drawing branch having higher grades at 6.65 compared to 5.56 for the surveying branch. Then the study recommended the necessity of building a database for the institute's students and conducting a balance between the subjects and courses for the students of the architectural drawing department, as well as conducting workshops to update the study materials.

Study on hydration and heavy metal leaching of washed MSWI FA as a green cementitious material

Despite municipal solid waste incineration fly ash (MSWI FA) being classified as hazardous waste, there is a consensus on its significant potential for resource utilization. The high content of soluble chlorides in MSWI FA not only limits its co-disposal in cement kilns but also hinders its application in the field of construction materials. The research aimed to investigate the viability of MSWI FA as a binding material in the construction engineering field. Through examining the impact of liquid/solid ratio and pH levels on the chemical composition of MSWI FA during the washing procedure, the study delved into the mechanisms affecting hydration and the characteristics of heavy metal contamination before and after washing pretreatment. The results indicate the feasibility of developing MSWI FA into construction engineering materials. The liquid/solid ratio is a significant factor affecting the removal of chlorides, with an optimal ratio of 30. pH plays a crucial role in the mineral composition during the water-washing process of MSWI FA. An acidic environment can remove a higher proportion of chlorides, while an alkaline environment can form more calcium salts. The compressive strength of pure MSWI FA at 28 days is 3.92 MPa, which can be increased by 0.99 MPa after washing. The washed MSWI FA not only exhibits heightened reactivity but also demonstrates reduced leaching concentrations of heavy metals, and its leaching toxicity was much lower than the safe landfill threshold. Therefore, MSWI FA, after washing, is a very promising alternative material for construction engineering.

Developing a Quantitative Methods Course for Civil and Construction Engineering Students

The ability to apply quantitative methods to gather, review, analyze and draw conclusions from data to support decision making is an important skillset in the engineering profession. We developed a new three-credit hour undergraduate course to develop a deeper understanding and appreciation for basic quantitative methods for construction engineering students. The course’s main objective is to provide basic skills in quantitative methods, by familiarizing students with the critical steps in an analytical approach to decision-making. The course applies a hands-on approach to problem solving and decision making using various engineering-themed problems that practicing engineers are likely to face. The specific methods explored include; constructing quantitative models to include break-even analysis, decision trees, linear optimization, PERT/CPM schedule development and crashing, as well as Monte Carlo Simulation and sensitivity analysis to generate and interpret problem solutions. Instruction emphasizes a hands-on approach and practical assignments to develop a logical framework for describing and solving problems. This new course emphasizes Excel software rather than hand calculations and introduces use of Excel Solver and other data analysis tools.

The crystal engineering of 3d-metal with two tetrazole-phenyl-carboxylate linkers and multiple post-synthetic metal exchange

Ligand with mixed-groups contributes to the construction of special structure motif with novel properties, yet it is challenging to understand and regulate the competitive coordination of different functional groups toward metal ions. Herein, the crystal engineering for MOF construction from two linear di-topic linkers of 4-(1H-tetrazol-5-yl)benzoic acid (H2tzba) and the longer 4′-(1H-tetrazol-5-yl)-[1,1′-biphenyl]-4-carboxylic acid (H2tzbba) featuring dual groups of phenyl-carboxylate and tetrazole, with three typical 3d metal ions (Mn2+, Fe2+ and Zn2+) were investigated. A series of five new MOFs, that are [Mn3(tzba)3(dmf)4]·11dmf (1, dmf = N,N-Dimethylformamide), [Mn(tzba)(H2O)] (2), [Zn2(tzba)2(dmf)]·0.5dmf (3), [Fe2(tzba)2(dmf)]·0.5dmf (4) and [Zn2(tzbbz)(gac)(dmf)2]·1.5dmf (5), were synthesized under solvothermal reactions. The modified solvent template and temperature effects lead to 3D framework of 1 (dmf solution under 433 K) with [Mn3(tz)3(COO)3] nodes and 1D hexagonal channels, while the 2 (water solution under 373 K) posssesses compact pillared-layer motif from [Mn2(tz)2] nodes. Under changed metal salt reactents but same solvent of dmf, the generated 3 and 4 have isoreticular and porous pillared-layer structure constituted of [M2(COO)2] nodes. The use of tzbba coupled with a crucial auxiliary ligand of glycollic acid, leads to 5 as porous pillared-layer MOFs with 1D rounded channels. The porous flexible skeleton of 1 constructed from [Mn3] node with four coordinated dmf facilitates post-synthetic metal exchange of the Mn2+ by radius similar metal ions following priority of Cu2+ < Zn2+ < Ni2+< Co2+, with maximum exchange percentage ranging from 17 % to 69 %. Time dependent ICP-AES analysis revealed the quite wide feasible solvents for post-synthetic metal exchange, as well as the fine control on substituting degree based on soaking temperature and time. Particularly, the compatible introduce of four different metal ions support the potential of preparing bi-, tri-, tetra- and penta- metal MOF crystals, that can not be obtained by traditional solvothermal reactions.

Air pollutant removal performance using a BiLSTM-based demand-controlled ventilation method after tunnel blasting

Efficient tunnel ventilation is essential for ensuring construction safety and protecting personnel health during tunnel construction. This study proposes a demand-controlled ventilation (DCV) method on the basis of deep learning algorithm to both improve pollutant removal efficiency and reduce energy consumption. The DCV method utilizes a two-layer bidirectional long short-term memory algorithm (BiLSTM) to predict pollutant concentrations. The air volume is dynamically adjusted based on the gaseous pollutant removal requirements. The coefficient of ventilation performance (COVP) is proposed to evaluate the performance of two ventilation methods (DCV and constant air-volume ventilation (CAV)) through computational fluid dynamics (CFD) simulations. The results show that the DCV results in a lower maximum average CO concentration and higher removal efficiency in the heading area (372.3 mg/m3) than the CAV does (404.1 mg/m3). The fan's energy consumption of DCV is 64.6% lower than that of CAV during a 1000 s ventilation period. The COVPs for both methods exhibit temporal variation and achieves their maximums (2.25 for DCV and 0.741 for CAV) after reaching the constraint conditions (air volume threshold). The DCV method expedites pollutant elimination, reduces construction waiting period, and minimizes energy consumption, providing a novel application of a deep learning algorithm in construction engineering.

Construction engineering to transform the composite frame of YY London

30 South Colonnade was a 13-storey composite framed office building, completed in the early 1990s as part of the first phase of London’s Canary Wharf redevelopment. Between 2020 and 2023 it was refurbished to become YY London, a highly sustainable modern workspace. A key aspect of the project has been retention and reuse of much of the existing structure, dramatically reducing the new development’s embodied carbon impact. This paper describes the way the composite steel-framed building’s original structural configuration was developed to accommodate the move to on-screen share trading in the 1980s. It then outlines the reasons that change why change was needed for the 2020s. A key feature of the construction engineering was demolition that disconnected several bays of the original bracing from the surrounding slab, requiring staged construction of temporary stability, and complicated by an unusual decision by the original designers.UNSDG 12 Responsible Consumption and Production is stimulating architect, engineers, and clients to propose more extensive reuse of structures and their embodied carbon. This paper describes the construction engineering challenges that these schemes generate for contracting teams, and some of the solutions that can be implemented to preserve and reuse the embodied carbon of composite buildings.

Application of Building Engineering Technology in Old House Reconstruction Project

With the continuous improvement of China's urbanization level, more and more people have poured into the city, which has caused greater residential pressure to the city. And with the continuous rise of land prices, cities want to solve the problem of population living, they need to transform some old houses, increase the building area of the old houses, to meet more residential needs. This paper focuses on the transformation of old buildings. By analyzing the reasons for the transformation of old buildings, it summarizes the principles of the transformation of old buildings, and leads to various construction engineering technologies that can be actually applied in the process of the transformation of old buildings, so as to meet the basic needs of the transformation of old buildings through the rational application of construction engineering technologies.

Multi-Agent Decision-Making in Construction Engineering and Management: A Systematic Review

Construction engineering and management (CEM) involves multiple stakeholders, complex interest relationships, and conflicts. All stakeholders must comprehensively consider the interests relating to a project to make decisions. Appropriate multi-agent decision-making can effectively coordinate and integrate the needs or expectations of all stakeholders, which can reduce conflicts, improve the success probability of the project, maximize the overall returns on interest, and contribute to the project’s sustainability. Existing studies have begun to explore the associated theoretical framework and practical methods for multi-agent decision-making. However, early studies mainly focused on the conceptual theories of decision-making models and processes, such as stakeholder analysis, decision evaluation, and risk management. In recent years, increasing research has concerned the application of multi-agent decision-making in CEM. This paper is the first to use a comprehensive review approach to analyze multi-agent decision-making in CEM, providing an overall perspective. In this paper, 105 journal papers are identified and classified into four categories: (1) main concerns regarding multi-agent decision-making in CEM; (2) tools and methods of multi-agent decision-making in practice; (3) research tools and methods of multi-agent decision-making in CEM; (4) critical points on how to solve multi-agent decision-making problems. The findings of this study offer references for future trends in four regards, namely (1) knowledge management, (2) decision resilience, (3) the fusion of many techniques, and (4) technologies for future research.

Influence of cooling bed temperature on microstructural, mechanical properties and surface quality of high-strength seismic-resistant rebar

High-strength seismic-resistant rebar is essential in construction engineering, yet balancing its strength, plasticity, seismic resistance, and surface quality poses challenges. This research explores niobium-vanadium-nitrogen composite reinforced steel, examining how cooling bed temperature affects its microstructure, mechanical properties, and surface quality using advanced microscopy techniques. The research reveals that the steel's microstructure primarily consists of ferrite and pearlite. As cooling bed temperatures decrease, both the proportion and colony size of pearlite, along with ferrite size, show a declining trend. Specifically, reducing the cooling bed temperature from 1100 °C to 900 °C results in a decrease in yield strength, tensile strength, and tensile-to-yield strength ratio by 6 MPa, 27 MPa, and 0.03, respectively. Meanwhile, elongation after fracture, total elongation at maximum force, and strength-ductility balance improve by 3.8%, 1.5%, and 1.93%, respectively. At a cooling bed temperature of 1100 °C, significant blistering occurs on the steel surface. Significant blistering at 1100 °C vanishes at 1000 °C and 900 °C, with diminished silicon enrichment in the surface oxide scale. A cooling bed temperature of 1000 °C ± 20 °C is recommended for optimal performance. This study provides a temperature control strategy for the properties of high-strength seismic-resistant rebar rated at 650 MPa and above, contributing valuable insights to the field of advanced construction materials.

Application and Impact Assessment of Renewable Energy in Construction Projects

As a new type of environmentally friendly energy, renewable energy is widely used in various fields of society, and its purpose is to reduce the pollution of social production to the environment. For the research on the application effect of renewable energy, there are few cases and insufficient research depth. In order to solve the above problems, this paper proposes an improved regression analysis method, which refers to the construction engineering yearbook of 2020~2022 and the actual construction project as a case for analysis, and compares with SPSS, Mintab and other software to verify the effect of renewable energy application before and after. Among them, the complex data in renewable energy and construction projects are processed by binary classification, and the data with large field spans and low similarity are scaled to improve the unity of research content. The results of the study are realistic: renewable energy has an impact on energy consumption, cost and CO2 emissions in construction projects, and the reduction range is between 10~20%, and there is a positive proportional relationship. At the same time, the application of renewable energy is directly affected by energy policy and environmental policy, and the impact coefficient is between 0.3~0.5. This shows that the application potential of renewable energy in construction projects is huge, and the development sustainability of construction projects is improved.

The Application Research of BIM and IOT Technology integration in Construction Engineering

The Application Research of BIM and IOT Technology integration in Construction Engineering

Exploration of the Comprehensive Education Model Integrating Positions, Courses, Competitions, and Certifications: A Case Study of the Construction Engineering Technology Major

Exploration of the Comprehensive Education Model Integrating Positions, Courses, Competitions, and Certifications: A Case Study of the Construction Engineering Technology Major