Machine Construction Research Articles

Don State Technical University Experience of Enhancing Vocational Training of the Specialists in Organisation of Construction

Introduction. Professional requirements set for the specialists in organisation of construction (job foremasters and foremen) grow from year to year along with the emergence of new building technologies, materials, machines and equipment. After implementation of the new professional standards in our country, such specialists are urged to acquire the additional knowledge, abilities and skills necessary for fulfilling their job functions and activities in compliance with their proficiency level. Therefore, searching the ways of enhancing the vocational training of the specialists in organisation of construction has become the objective of the authors’ research.Materials and Methods. To achieve this objective, the authors have developed a series of additional activities for teaching the specialists in organisation of construction, based on improving the competencies required in their profession. This series of activities includes the initial, intermediate and final stages of vocational training. The initial stage is implemented during the first three years of bachelor and long first degree (specialist) programmes within direction of study “Construction” and the remaining two stages – during the last years of university study or after graduation.Results. The article presents the Don State Technical University experience of enhancing vocational training of the specialists in organisation of construction. The article contains the information on integration into the educational process of the photo and video tests, construction machines simulators, business games (including the ones on erecting the building structures out of the crane operator’s field-of-view), thematic references on difficult (challenging) issues of construction production, glossary of construction slang, as well as the information on organisation of the student scientific circle “Student construction control”.Discussion and Conclusion. The experience of enhancing vocational training of the specialists in organisation of construction accumulated by the authors is successfully implemented at the Continuing Education Department and the Production Technology in Construction Chair of Don State Technical University. It can be recommended for implementation in other universities where students of the direction of study “Construction” are educated.

The Use of Artificial Intelligence in Educational Institutions: Social Consequences of Artificial Intelligence in Education

Artificial Intelligence (AI) technology was created to solve problems that are complex to be solved by humans, related to the construction of machines that understand, monitor, reason, predict, interact, learn, develop and work like humans. Thanks to the development AI has achieved in recent years, AI has surpassed its limits in the field of computer engineering and has begun to be effective in almost every field. AI has started to contribute to the management of information in education and directly to the education and training process, with its features such as learning, making predictions, solving complex problems, having experience and adapting to changing conditions. Systems inspired by AI have become very popular and have been applied in almost every field, especially in educational institutions. The biggest impact of this technology on education has been in the delivery of education. Technological developments are starting to change many sectors and the education sector is also keeping up with this change. AI is not just made to support learning. AI is used in all educational institutions (teacher duties, administrative works, teaching, school and classroom management). Research needs to be increased, especially in terms of the social impact that AI will have on people regarding the use of AI in education. Qualitative method was used in this study. The purpose of the article is to investigate the effect of AI on traditional education, to examine the point the traditional education has reached with AI and to analize the social consequences of AI in education.

Influence of abrasive waterjet pre-cutting slit on the performance of shield cutter cutting reinforced concrete

To improve the efficiency and safety of shield machines cutting reinforced concrete (RC) obstacles, using an abrasive waterjet (AWJ)-assisted shield cutter to cut RC is considered a feasible solution. To explore the effect of an AWJ on a shell cutter cutting RC, laboratory experiments and numerical simulation methods were used to compare the breaking performance and damage mechanism of a shell cutter cutting RC with and without a slit. The results showed that the waterjet pre-cutting slit could effectively reduce the cutter force of the shell cutter and promote the cutting of steel bars. The reduction rate of the cutter force by the waterjet slit increased and then decreased with an increase in the penetration degree. When the penetration degree was 1.5 mm, the waterjet cutting slit had the best reduction effect on the cutting force. An optimal critical value was found for the waterjet cutting slit of the steel bar; when the waterjet slit was larger than ½ of the diameter of the steel bar, the steel bar was prone to tensile fracture under the cutting action. The waterjet pre-cutting slit reduced the residual cross-sectional area and stress distribution of the steel bar, which promoted the bending and tensile fracture of the steel bar and thus improved the cutting efficiency. Furthermore, construction recommendations for the AWJ-assisted shield cutting of RC obstacles were identified. The findings presented in this paper can provide useful reference information for the design and construction of AWJ shield machines.

SAT587 Methylation-based Machine Learning Classifiers Discriminate Sellar Tumors By Lobe Origin Using Liquid Biopsy Or Surgical Specimens

Abstract Disclosure: G.A. Herrgott: None. K.P. Asmaro: None. M. Wells: None. K. Nelson: None. B. Thomas: None. L.A. Hasselbach: None. A. Transou: None. S. Cazacu: None. K.M. Tundo: None. S. Nadimidla: None. L. Scarpace: None. J. Barnholtz-Sloan: None. A.E. Sloan: None. W.R. Selman: None. A.C. deCarvalho: None. A. Mukherjee: None. A.M. Robin: None. I.Y. Lee: None. J. Craig: None. S. Kalkanis: None. J. Snyder: None. T. Walbert: None. J. Rock: None. H. Noushmehr: None. A.B. Castro: None. Background: The differential diagnosis of challenging sellar tumor cases can be inconclusive through imaging features and could benefit from noninvasive diagnostic approaches, such as liquid biopsy (LB). Similar to tissue, LB specimens carry tumor-specific DNA methylation signatures amenable to the construction of accurate machine learning models able to discriminate CNS tumors. We aimed to develop methylation-based classifiers which classify sellar tumors by lobe of origin, using either LB or tumor tissue specimens. Methodology: We analyzed the DNA methylome (EPIC array) of tumor tissue (T) and LB specimens from adult patients with tumors representing each of the three pituitary lobes (Anterior: T=177; LB=37; Intermediate: T= 7; LB: 10 and Posterior: T=44, LB=2 cases). Using the most variably methylated CpG probes derived from the unsupervised variance-based analyses across tumors from different lobes, we applied multi-class linear discriminant analysis to construct machine learning models to classify sellar tumor tissue and/or LB specimens. Results: We generated classifiers based on lobe-specific methylation signatures that were able to discriminate across sellar tumors either using tissue and/or LB specimens (500 and 600 CpGs, respectively) with observed accuracies of ∼99% across independent validation. DISCUSSION/CONCLUSION: Our findings suggest that methylation-based classifiers constitute an accurate diagnostic approach to discriminate sellar tumors according to the lobe origin, either pre-surgically through a blood draw or through surgical tumor specimens. These classifiers are objective approaches that could complement imaging and pathology reports for an accurate diagnosis of inconclusive cases, ultimately leading to optimal management of the patients with these diseases. Presentation: Saturday, June 17, 2023

Machine learning based on radiomics features combing B-mode transrectal ultrasound and contrast-enhanced ultrasound to improve peripheral zone prostate cancer detection

PurposeTo construct machine learning models based on radiomics features combing conventional transrectal ultrasound (B-mode) and contrast-enhanced ultrasound (CEUS) to improve prostate cancer (PCa) detection in peripheral zone (PZ).MethodsA prospective study of 166 men (72 benign, 94 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA (fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Time-intensity curves were obtained using SonoLiver software for all lesions in regions of interest. Four parameters were collected as risk factors: the maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (MTT). Radiomics features were extracted from the target lesions from B-mode and CEUS imaging. Multivariable logistic regression analysis was used to construct the model.ResultsA total of 3306 features were extracted from seven categories. Finally, 32 features were screened out from radiomics models. Five models were developed to predict PCa: the B-mode radiomics model (B model), CEUS radiomics model (CEUS model), B-CEUS combined radiomics model (B-CEUS model), risk factors model, and risk factors-radiomics combined model (combined model). Age, PSAD, tPSA, and RT were significant independent predictors in discriminating benign and malignant PZ lesions (P < 0.05). The risk factors model combing these four predictors showed better discrimination in the validation cohort (area under the curve [AUC], 0.84) than the radiomics images (AUC, 0.79 on B model; AUC, 0.78 on CEUS model; AUC, 0.83 on B-CEUS model), and the combined model (AUC: 0.89) achieved the greatest predictive efficacy.ConclusionThe prediction model including B-mode and CEUS radiomics signatures and risk factors represents a promising diagnostic tool for PCa detection in PZ, which may contribute to clinical decision-making.

Cybernetic Proportional System for a Hydraulic Cylinder Drive Using Proportional Seat-Type Valves

Hydraulic cylinders are well known as cheap and robust actuators for moving heavy loads, especially in harsh environments. For this reason, they are often used in construction machines such as excavators. Basically, the hydraulic cylinders are controlled by four-way proportional spool valves, resulting in poor energy efficiency due to resistance control. Furthermore, because all valve edges are located at the same spool, common proportional valves suffer from limited flexibility with regard to different loads. Independent metering (IM) is a well-known strategy for making proportional hydraulic drives more efficient and more flexible because each port of the actuator can be connected individually to pressure or tank by independent two-way proportional valves. In this paper, the IM concept is realized as an open-loop control for a hydraulic cylinder drive, which, in combination with a human operator, constitutes a so-called cybernetic proportional system (CPS). The piston velocity commanded by the operator is controlled by the compensation of the static characteristics of the proportional seat-type valves. Basic simulations show the benefits and also the problems of open-loop independent metering. Furthermore, measurements on one actuator of a real excavator regarding controllability and energy consumption are presented.

The State of the Art in Digital Construction of Clay Buildings: Reviews of Existing Practices and Recommendations for Future Development

Under the trend of digitization and global carbon reductions, clay construction has increasingly attracted attention due to advantages such as an excellent thermal performance, low carbon emissions, and high cost-effectiveness. In this article, an in-depth investigation is conducted into the potential, challenges, and future in the development of digital manufacturing technologies for clay architecture, especially 3D printing, robotic construction, and prefabrication systems. It is revealed through a review that digital clay construction is under rapid development and likely to provide a viable solution to achieving global carbon neutrality, which is conducive to addressing various regional issues. The key findings from this review are as follows. Firstly, as a flexible, precise, and low-carbon industrial solution, 3D printing lays a foundation for the extensive research on materials, equipment, and algorithm optimization. Secondly, the use of machine construction methods such as modern rammed earth technology and drone spraying is effective in improving efficiency and lowering costs. Lastly, the prefabricated building system shows its potential in renewing and developing the rammed earth architecture culture. These findings indicate a massive potential of digital clay construction to support sustainability efforts in the future.

Studies of adhesive and wear-resistant properties of chrome coatings for car parts and road construction equipment restoration

Introduction. The use of chrome plating as a method of restoring parts that work with waterjet wear is important for restoring the operability of hydraulic drive units and parts used in specialized vehicles and road construction equipment. The proposed method of chrome recovery has the necessary advantages, it is a sufficiently high performance and microhardness of the resulting chrome coatings. However, for implementation in production, additional studies of the reliability of the chrome coatings obtained, namely, adhesion strength and wear resistance, were required. This will make possible to draw conclusions about the prospects for further implementation and use for the restoration of hydraulic drive parts and other parts that work with waterjet wear.Materials and methods. To conduct the research, equipment meeting the requirements of technical conditions, using existing and developed devices and devices for obtaining chrome precipitation was used. For this purpose, the necessary devices and samples from the appropriate material to approximate the reliability of the obtained research results were made. Also the necessary improvements to digitize the data obtained on the friction machine were made.Results. The performed studies of adhesion strength have shown that the chrome coatings obtained from the new electrolyte composition will make possible to obtain adhesion strength reaching 270 MPa, which is sufficient for parts operating at high pressures (20 MPa) with the necessary margin of safety. Also the studies conducted on the wear resistance of chrome coatings let to conclude that the wear resistance of the chrome coatings obtained is 20-30% higher compared to the reference surface (the material of the spool manufacture).Discussion and conclusion. The studies of the obtained chrome coatings from the new electrolyte composition made possible to establish, which let to establish the regularities of the influence of the electrolyte composition and the modes of testing the wear resistance of the precipitation obtained compared with the existing universal chromium plating electrolyte. The results obtained confirmed the possibility of using a chrome coating from a new chromium plating electrolyte for the restoration of car parts and road construction machines operating under waterjet wear.

Tunable Fluorescence and Morphology of Aggregates Built from a Mechanically Bonded Amphiphilic Bistable [2]Rotaxane.

Advanced Organic Chemical Materials Co-constructed Mechanically bonded amphiphiles (MBAs), also known as mechanically interlocked molecules (MIMs), have emerged as an important kind of functional building block for the construction of artificial molecular machines and soft materials. Herein, a novel MBA, i. e., bistable [2]rotaxane H2 was designed and synthesized. In the solution state, H2 demonstrated pH and metal ion-responsive emissions due to the presence of a distance-dependent photoinduced electron transfer (PET) process and the fluorescence resonance energy transfer (FRET) process, respectively. Importantly, the amphiphilic feature of H2 has endowed it with unique self-assembly capability, and nanospheres were obtained in a mixed H2 O/CH3 CN solvent. Moreover, the morphology of H2 aggregates can be tuned from nanospheres to vesicles due to the pH-controlled shuttling motion-induced alternation of H2 amphiphilicity. Interestingly, larger spheres with novel pearl-chain-like structures from H2 were observed after adding stoichiometric Zn2+ . In particular, H2 shows pH-responsive emissions in its aggregation state, allowing the visualization of the shuttling movement by just naked eyes. It is assumed that the well-designed [2]rotaxane, and particularly the proposed concept of MBA shown here, will further enrich the families of MIMs, offering prospects for synthesizing more MIMs with novel assembly capabilities and bottom-up building dynamic smart materials with unprecedented functions.

Construction Instance Segmentation (CIS) Dataset for Deep Learning-Based Computer Vision

Deep learning-based computer vision (DLBCV) techniques have played an important role in intelligent construction. Image datasets are essential for developing DLBCV algorithms. However, a large-scale construction-specific dataset of major construction elements, such as precast components (PCs), PC delivery trucks, and workers not wearing safety helmets, remains absent. This paper presents the Construction Instance Segmentation (CIS) dataset, a new image dataset aimed at advancing state-of-the-art instance segmentation in the field of construction management. It contains 50,000 images with ten object categories belonging to construction workers, machines, and materials. Two rounds of algorithmic analysis have been conducted to refine and balance the dataset. Finally, a detailed statistical analysis of the dataset is presented.

European Green Deal: Improving the Efficiency of Using Planetary Hydraulic Machines

Research is devoted to the use of energy-efficient hydraulic motors in running modules for mechatronic systems of construction, road, municipal, agricultural, railway and other self-propelled machines; robots are used in various branches of the European economy and can contribute to the practical implementation of the philosophy of the “European Green Deal”. As a result of this research, design schemes and a mathematical apparatus have been developed that allow determining the rational force in the contact zone of the movable and fixed distributors. In order to determine the geometric parameters of the stepped surfaces of the movable and fixed distributors, which ensure the formation of an oil film in the contact zone, a structural-functional diagram of the mathematical model has been developed, which makes it possible to obtain a quantitative estimate of the increase in the productivity of the distribution system. The conducted studies have established that with rational design of the distribution system elements by reducing the clamping force by 3...5% in the distribution system, leakage through the end gap is reduced by 26%. Reduction of leakage in the contact zone allows an increase in productivity of the distribution system of 2.4% by increasing the volumetric efficiency (reducing losses).

Selection of optimal sets of road construction machines in modern conditions

In this article, the authors consider the improvement of the methodology for substantiating the formation of the optimal structure of the machine park and sets of machines, taking into account the influence of specific conditions of work in various natural and climatic conditions and technological parameters of service facilities. The tasks of increasing the efficiency of the fleet of road construction vehicles, identifying the main goal and taking into account the secondary ones, which can act as limitations in the model of optimizing the fleet of cars. Construction of an algorithm for an automated system for the functioning of a fleet of cars. Keywords: productivity, efficiency, set of machines, object, formation, automated system

Advanced Control Systems for Axial Piston Pumps Enhancing Variable Mechanisms and Robust Piston Positioning

Axial piston pumps provide a number of benefits, including strong pressure resistance, high efficiency, high transmission power, a broad speed range, and a long lifespan. These characteristics have led to their widespread usage in naval applications, construction machines, and hydraulic machinery. On the other hand, axial piston pumps frequently display reduced operating speeds as well as instability because of their inherently nonlinear properties. In this study, a mathematical model of these changeable (variable) processes is developed using a mix of theoretical calculations and acquired data. An investigation of variable mechanism control in axial piston pumps is carried out centered on robust control methods, and the controller is constructed utilizing robust H∞ theory. In terms of resilience, control precision, and system reaction time, simulations show that the H controller surpasses the PID controller.

A data driven real-time perception method of rock condition in TBM construction

In tunnel boring machine (TBM) construction, the presence of collapsible rock mass (CRM) can lead to accidents such as collapse and jamming. This study presents a novel CRM early warning strategy based on real-time TBM rock fragmentation data to improve safety and efficiency in CRM conditions. The strategy includes a qualitative classification model and a quantitative probability model for CRM identification. The results indicate that the distribution dissimilarity index β effectively reflect the significance of variables across CRM and non-CRM datasets. Various parameters, including TPI, FPI, WR, and AF, show discriminatory ability between CRM and non-CRM samples. In particular, the CRM-weighted index, which combines the strengths of the individual indices, achieves a distributional dissimilarity index of 1.05, significantly higher than any of the individual indices. The qualitative classification model proves effective in identifying samples from collapse areas, demonstrating ability to identify samples located in adverse geological condition. The quantitative model shows that the probability of CRM is generally higher in adverse geological area samples, particularly in zones where collapse has occurred, with a CRM probability is approaching 1. The proposed strategy provides accurate early warnings to prevent collapse accidents and represents a practical approach to improving the safety and efficiency.

RoadAI: Reducing emissions in road construction

This challenge aims to reduce CO2 emissions from Norwegian construction machines by utilizing data-driven approaches.Skanska Norge AS provides a wealth of data from their construction sites, including GPS data, machine data, vibration data, and drone maps. The challenge focuses on using this data to optimize road construction processes for increased sustainability. Participants are tasked with developing feasible solutions to reduce idle time, optimize dump truck flow, minimize unnecessary driving, and improve driving styles. The evaluation criteria consider the novelty of the algorithms, feasibility of implementation, and the sustainable impact. The competition encourages innovative approaches to leverage data for sustainable construction practices.

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations.

Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling the catalytic function within zeolites under operating conditions. First-principles MD simulations refer to methods where the dynamics of the nuclei is followed in time by integrating the Newtonian equations of motion on a potential energy surface that is determined by solving the quantum-mechanical many-body problem for the electrons. Catalytic solids used in industrial applications show an intriguing high degree of complexity, with phenomena taking place at a broad range of length and time scales. Additionally, the state and function of a catalyst critically depend on the operating conditions, such as temperature, moisture, presence of water, etc. Herein we show by means of a series of exemplary cases how first-principles MD simulations are instrumental to unravel the catalyst complexity at the molecular scale. Examples show how the nature of reactive species at higher catalytic temperatures may drastically change compared to species at lower temperatures and how the nature of active sites may dynamically change upon exposure to water. To simulate rare events, first-principles MD simulations need to be used in combination with enhanced sampling techniques to efficiently sample low-probability regions of phase space. Using these techniques, it is shown how competitive pathways at operating conditions can be discovered and how broad transition state regions can be explored. Interestingly, such simulations can also be used to study hindered diffusion under operating conditions. The cases shown clearly illustrate how first-principles MD simulations reveal insights into the catalytic function at operating conditions, which could not be discovered using static or local approaches where only a few points are considered on the potential energy surface (PES). Despite these advantages, some major hurdles still exist to fully integrate first-principles MD methods in a standard computational catalytic workflow or to use the output of MD simulations as input for multiple length/time scale methods that aim to bridge to the reactor scale. First of all, methods are needed that allow us to evaluate the interatomic forces with quantum-mechanical accuracy, albeit at a much lower computational cost compared to currently used density functional theory (DFT) methods. The use of DFT limits the currently attainable length/time scales to hundreds of picoseconds and a few nanometers, which are much smaller than realistic catalyst particle dimensions and time scales encountered in the catalysis process. One solution could be to construct machine learning potentials (MLPs), where a numerical potential is derived from underlying quantum-mechanical data, which could be used in subsequent MD simulations. As such, much longer length and time scales could be reached; however, quite some research is still necessary to construct MLPs for the complex systems encountered in industrially used catalysts. Second, most currently used enhanced sampling techniques in catalysis make use of collective variables (CVs), which are mostly determined based on chemical intuition. To explore complex reactive networks with MD simulations, methods are needed that allow the automatic discovery of CVs or methods that do not rely on a priori definition of CVs. Recently, various data-driven methods have been proposed, which could be explored for complex catalytic systems. Lastly, first-principles MD methods are currently mostly used to investigate local reactive events. We hope that with the rise of data-driven methods and more efficient methods to describe the PES, first-principles MD methods will in the future also be able to describe longer length/time scale processes in catalysis. This might lead to a consistent dynamic description of all steps-diffusion, adsorption, and reaction-as they take place at the catalyst particle level.

Protocol Proposal for Data Collection and Processing in Productivity Assessment of Earthworks Using Audio-Visual and Location-Sensing Technology

During earthworks, monitoring and controlling the actual productivity of construction machines enables insight into the progress of tasks, calculation of expected duration and costs, favorable use and allocation of machines, and the application of appropriate decisions and corrective measures, which is of great interest to contractors. Excavators and tipper trucks are primarily used in earthworks. Manual collection of data from the construction site to assess the actual productivity of machines is today considered an outdated, time-consuming, and subjective method. Therefore, audio–visual and sensing technology devices are replacing manual data collection. The paper aims to propose an innovative protocol for optimal recording/tracking of the work of excavators and tipper trucks using audio–visual (a video camera or smartphone) and location-sensing technology (GPS). The protocol proposal enables a precise statement of the time cycle of excavators and tipper trucks and an estimate of their actual productivity. The application of the protocol proposal on the construction site demonstrated its practicality and functionality.

Development and validation of a radiomics-based prediction pipeline for the response to stereotactic radiosurgery therapy in brain metastases.

The first treatment strategy for brain metastases (BM) plays a pivotal role in the prognosis of patients. Among all strategies, stereotactic radiosurgery (SRS) is considered a promising therapy method. Therefore, we developed and validated a radiomics-based prediction pipeline to prospectively identify BM patients who are insensitive to SRS therapy, especially those who are at potential risk of progressive disease. A total of 337 BM patients (277, 30, and 30 in the training set, internal validation set, and external validation set, respectively) were enrolled in the study. 19,377 radiomics features (3 masks × 3 MRI sequences × 2153 features) extracted from 9 ROIs were filtered through LASSO and Max-Relevance and Min-Redundancy (mRMR) algorithms. The selected radiomics features were combined with 4 clinical features to construct a two-stage cascaded model for the prediction of BM patients' response to SRS therapy using SVM and an ensemble learning classifier. The performance of the model was evaluated by its accuracy, specificity, sensitivity, and AUC curve. Radiomics features were integrated with the clinical features of patients in our optimal model, which showed excellent discriminative performance in the training set (AUC: 0.95, 95% CI: 0.88-0.98). The model was also verified in the internal validation set and external validation set (AUC 0.93, 95% CI: 0.76-0.95 and AUC 0.90, 95% CI: 0.73-0.93, respectively). The proposed prediction pipeline could non-invasively predict the response to SRS therapy in patients with brain metastases thus assisting doctors to precisely designate individualized first treatment decisions. The proposed prediction pipeline combines the radiomics features of multi-modal MRI with clinical features to construct machine learning models that noninvasively predict the response of patients with brain metastases to stereotactic radiosurgery therapy, assisting neuro-oncologists to develop personalized first treatment plans. • The proposed prediction pipeline can non-invasively predict the response to SRS therapy. • The combination of multi-modality and multi-mask contributes significantly to the prediction. • The edema index also shows a certain predictive value.

OVERVIEW OF STRUCTURAL ENGINEERING EDUCATION REQUIRED FOR THE UNDERSTANDING OF VITRUVIUS’S TEN BOOKS ON ARCHITECTURE

The contents of many books, prima facie, can be known by their title. But within the context of the present day grammatical construction and images formed in the mind through some words, Vitruvius’s “Ten Books on Architecture”, written in 1st century, does not fall into this category. Apparently, the over 2000 year’s gap in knowledge and usage of precepts as well as educational attainment that formed the basis for the writing constituted unfamiliar background and terrain for the 21st century researchers and academia. Consequently many civil engineering developmental resources for research activities contained in the book, especially on concrete and structural materials, remain hidden from many researchers. The purpose of this work is to identify the depths of instructional and educational attainment, both in learning and practice that formed the background for writing and thus, for the understanding of the book, especially for engineers in the developing Nations. To accomplish this work, the Roman society of the time and her educational systems were studied from materials available from the public domain. Analysis of the materials obtained showed that a wide-range and multi-faceted education through learning and practice, were required to operate in the construction industry at the time; and that all construction work, including military and machine construction were termed Architecture and under the control of the Head of the Government. It is also concluded that this learning, now parceled into different disciplines, is available in the public domain for the understanding and application of precepts in the Ten Books on Architecture for a robust structural and civil engineering practice

System-wide construction waste and their connectivity to construction phases, impacting 5M factors and effects: a systematic review

PurposeThis review paper, using a systematic literature review (SLR) approach, aims to unravel the various system-wide waste in the construction industry and highlight the connectivity to construction phases, namely men, materials, machines, methods and measurement (5M) and impacting factors.Design/methodology/approachThis study used an SLR approach and examined articles published since the 2000s to explore the connectivity of system-wide waste to construction phases, 5M and impacting factors. The results are given in table forms and a causal loop diagram.FindingsResults show that the construction and demolition (CD) waste research carried out from various perspectives is standalone. The review identified ten types of system-wide waste with strong interlinks in the construction industry. The finding highlights connectivity between wastes other than material, labour and time and the wastes' impacting factors. Further, the review results highlighted the solid connectivity for construction phases, 5M, and impacting factors such as productivity (P), delay (D), accidents (A), resource utilisation (R) and cost(C).Research limitations/implicationsSLR methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, fruitful articles hiding in less popular journals may not be included in the well-known database that was searched. Researcher bias of the authors and other researchers that authored the articles referred to is a limitation. These limitations are acknowledged.Practical implicationsThis article unravels the construction system-wide waste and the waste's interlinks, which would aid industry understanding and focus on eliminating the waste. The article highlights the connectivity of system-wide wastes to 5M, which would help better understand the causes of the waste. Further, the paper discusses the connectivity of system-wide waste, 5M and P, D, A, R and C that would aid the organisation's overall performance. The practical and theoretical implications include a better understanding of waste types to help capture better data for waste reduction and productivity improvement. The operating managers could use the tracking of wastes to compare estimated and actual resources at every process stage. This article on system-wide waste, 5M and P, D, A, R and C, relationships and their effects can theorize that the construction industry is more likely to identify clear root causes of waste now than previously. The theoretical implications include enhanced understanding for academics on connectivity between waste, 5M and P, D, A, R and C that the academics can use and expand to provide new insights to existing knowledge.Originality/valueFor the first time, this article categorised and highlighted the ten types of waste in construction industries and the industries' connectivity to construction phases, 5M and impacting factors.