A construction cost estimation framework using DNN and validation unit

Accurate construction cost estimation is crucial to completing projects within the planned timeframe and expenditure. The estimation process depends on multiple variables maintaining complex relationships between themselves and the target cost. As a result, an in-depth analysis from an experienced construction consultant is required to estimate construction costs accurately. Machine learning (ML) technology can learn from previous data, which is equivalent to human experience. Many project-specific ML models estimate the construction cost, which misses the generalizability. This paper addresses the gap and designs, develops, implements, and analyzes a deep learning (DL) based novel framework that maps 94.67% of the independent variables with a mean average percentage error (MAPE) of 11.60%. The proposed framework is not limited to any specific project. It estimates the construction cost of similar projects, further validated by an innovative estimator validation unit

Practice-based engineering design for next-generation of engineers: A CDIO-based approach

In recent years, practice-based learning has been establishing itself as a new norm in higher education: an enabler to foster knowledge, skills and innovative thinking in young learners. Conceive, design, implement and operate (CDIO), a well-established pedagogical methodology, offers many opportunities for education providers seeking to best achieve this practice-based learning within various educational environments. Case studies of engineering programs that made use of the CDIO model provide illustrations of how the ideas were put into effect in actual projects. This paper draws on a CDIO-based design case study where students were requested to solve a real engineering problem; in order to explore the great potential of such a teaching and learning paradigm in practice settings. Some first-year mechanical, biomedical and product design engineering students studying at the Canterbury Christ Church University were set a design brief by a Ford Motor Company tier supplier, to design a high security lock for commercial vehicles which works on both sliding and rear hinged slam doors. The project had twelve engineering groups, each with three or four students sharing responsibility for separate project design and engineering roles: including design sketches; computer-aided modelling; engineering material investigation; finite element analysis; computer-aided manufacturing; prototyping; project reporting and company presentation. In order to analyse the effect of incentives on the underlying motivation of learners, a cash prize was secured via the Engineers in Business Fellowship (EIBF) organisation, to be shared between the winners selected by the industrial partner after a detailed study of benefits, manufacturability and potential innovation. This paper documents the findings of collected qualitative and quantitative data as part of this project-based case study, and furthermore, reflects on the effectiveness of CDIO implementation on the depth of students’ knowledge and level of practical engineering learning. The objective here is to evaluate the individual and collaborative learning processes that occur among a group of students as they use CDIO active learning tactics. The analysis reported in this paper can serve as a foundation to illustrate how educators may better prepare their students for joining the workforce of the future, by using an active learning approach that provides more weight to practical than theoretical knowledge

Knowledge and agent-based system for decentralised scheduling in manufacturing

The aim of the research paper is to develop algorithms for manufacturers’ agents that would allow them to sequence their own operation plans and to develop a multi-agent infrastructure to allow operation pair agents to cooperatively adjust the timing of manufacturing operations. The scheduling problem consisted of jobs with fixed process plans and of manufacturers collectively offering the necessary operations for the jobs. Manufacturer agents sequenced and pair agents timed each operation as and when required. Timing an operation triggered a cascade of conflicts along the job process plan that other pair agents would pick up on and would take action accordingly. The conventional approach performs conflict resolution in series and manufacturer agents as well as pair agents wait until they are allowed to sequence and time the next operation. The limiting assumption behind that approach was systematically removed, and the proposed approach allowed manufacturers to perform operation scheduling in parallel, cutting down tenfold on the computation time. The multi-agent infrastructure consists of the Protégé knowledge base, the Pellet semantic reasoner and the Workflows and Agent Development Environment (WADE). The case studies used were the MT6, MT10 and LA19 job shop scheduling problems; and an industrial use case was provided to give context to the manufacturing environment investigated. Although there were benefits from the decentralised manufacturing system, we noted an optimality loss of 34% on the makespans. However, for scalability, our approach showed good promise

Towards decentralised job shop scheduling as a web service

This paper aims to investigate the fundamental requirements for a cloud-based scheduling service for manufacturing, notably manufacturer priority to scheduling service, resolution of schedule conflict, and error-proof data entry. A flow chart of an inference-based system for manufacturing scheduling is proposed and a prototype was designed using semantic web technologies. An adapted version of the Muth and Thompson 10 × 10 scheduling problem (MT10) was used as a case study and two manufacturing companies represented our use cases. Using Microsoft Project, levelled manufacturer operation plans were generated. Semantic rules were proposed for constraints calculation, scheduling and verification. Pellet semantic reasoner was used to apply those rules onto the case study. The results include two main findings. First, our system effectively detected conflicts when subjected to four types of disturbances. Secondly, suggestions of conflict resolutions were effective when implemented albeit they were not efficient. Consequently, our two hypotheses were accepted which gave merit for future works intended to develop scheduling as a web service. Future works will include three phases: (1) migration of our system to a graph database server, (2) a multi-agent system to automate conflict resolution and data entry, and (3) an optimisation mechanism for manufacturer prioritisation to scheduling services

The legacy of Verena Holmes: inspiring next generation of engineers

Verena Holmes was born in 1889 in Ashford, Kent, Verena became a pioneer for women in the industry as arguably the first female in the UK to have a full-time career as a professional mechanical, design and biomedical engineer. Verena was an advocate for widening participation in engineering and dedicated to the development of female engineers, she represented a breakthrough for equal rights in the early 20th century. As a creative and talented mechanical engineer, inventor and entrepreneur with own engineering business in Gillingham, Kent. In 1932, Verena Holmes filed a patent for poppet valve for fluid pressured systems, and in 2021 has provided the inspiration to students to conceive, design, implement and operate their own poppet valve. The poppet valve challenging first year biomedical, mechanical and product design engineering students to consider engineering materials, engineering manufacturing, standard components, fixes and fittings, and tolerances considerations into their poppet valve. This paper will provide qualitative analysis of the level of practical engineering learning, and the depth of student learning. Also, the quantitative analysis of the students’ evaluation of the learning opportunity to inspire, develop and stimulate them to be the next generation of engineers

Ontology-based decision tree model for prediction in a manufacturing network

This paper aims to create a predictive model, which will assist in the allocation of newly received orders in a manufacturing network. The manufacturing network, which is taken as a case study in this research, consists of more than 300 small manufacturing enterprises with a central company as the project managing integrator. The methodology presents the mapping of a PROSA (Product-Resource-Order-Staff Architecture) based ontology model on a decision tree, which was created with the Waikato Environment for Knowledge Analysis (WEKA) application. Furthermore, the methodology also demonstrates the formulation of the Semantic Web Rule Language (SWRL) rules from the WEKA decision tree with the help of MATLAB programming. The paper validated the result generated by the ontology model with the results of the decision tree model

Experimental study of PLA thermal behavior during fused filament fabrication

Fused filament fabrication (FFF) is an additive manufacturing technique that is used to produce prototypes and a gradually more important processing route to obtain final products. Due to the layer-by-layer deposition mechanism involved, bonding between adjacent layers is controlled by the thermal energy of the material being printed, which strongly depends on the temperature development of the filaments during the deposition sequence. This study reports experimental measurements of filament temperature during deposition. These temperature profiles were compared to the predictions made by a previously developed model. The two sets of data showed good agreement, particularly concerning the occurrence of reheating peaks when new filaments are deposited onto previously deposited ones. The developed experimental technique is shown to demonstrate its sensitivity to changing operating conditions, namely platform temperature and deposition velocity. The data generated can be valuable to predict more accurately the bond quality achieved in FFF parts