プロフィール詳細

プロジェクトを作成

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Dr. Ben M.に依頼

New Zealand

Freelance Structural Engineering Researcher | Expert in Applied Machine Learning and Data Engineering

プロフィール概要

I am a structural engineering researcher with specialised expertise in reinforced concrete behaviour, durability, and performance assessment, supported by strong capabilities in data analytics, data engineering, and applied machine learning. My work sits at the interface of experimental testing, computational modelling, and practical design support, helping clients convert complex structural data into defensible engineering insights. Recent role and ongoing research: I recently completed my postdoctoral research associate contract on the EU Horizon 2020 ReCreate project, a multinational initiative advancing circular construction through the complete structural reuse of precast concrete elements. Within the Dutch cluster, I led research across two work packages: 1. Reliability-based design guidance for reclaimed elements. I investigated the uncertainties associated with structural reuse and translated these into reliability-based adaptations to the Eurocode partial safety factors. This work produced practical recommendations that can be implemented directly in design checks and is expected to inform an upcoming reuse design manual. 2. Element database system for end-to-end reuse workflows. Designed and implemented an element database to digitise and preserve the full value chain of reclaimed precast elements (geometry, reinforcement, testing, QA, history, logistics, and documentation), with demonstrated interoperability with BIM and IFC workflows. PhD research and academic track record: My doctoral research focused on the deterioration assessment of ageing bridge structures in high-seismic regions, with a particular emphasis on chloride-induced corrosion. This work combined large-scale testing of artificially corroded reinforced concrete piers with applied machine learning, analytical studies, and 3D numerical modelling. The research produced six publications, all published in Q1 journals, including an ASCE Journal of Materials in Civil Engineering Article Editor’s Choice recognition. If you are looking for a collaborator who can combine rigorous engineering judgement with modern data analysis methods and communicate the outcomes clearly to technical and non-technical stakeholders, I am ready to support your project!

専門分野

• ★ Structural Engineering
• ★ Earthquake Engineering
• ★ Data Analysis
• ☆ Civil Engineering
• ☆ Predictive Modeling & Analytics
• ☆ Data Collection
• ☆ Machine Learning
• ☆ Statistics
• ☆ Applied Statistics
• ☆ Regression Analysis
• ☆ Multivariate Analysis
• ☆ Bayesian Statistics

サービス

• Writing
• Research
• Data & AI

Writing Technical Writing, General Proofreading & Editing

Research Scientific and Technical Research, Systematic Literature Review, Secondary Data Collection

Data & AI Predictive Modeling, Statistical Analysis, Data Visualization, Data Cleaning, Data Insights

職務経験

Eindhoven University of Technology

- 現在

Postdoctoral Researcher

Eindhoven University of Technology

10月 2023 - 10月 2025

学歴

PhD

University of Canterbury

2月 2019 - 12月 2023

Bachelor of Engineering (Honours) Civil and Natural Resources Engineering

University of Canterbury

2月 2015 - 12月 2018

認定資格

• PhD in Structural Engineering

  University of Canterbury

  12月 2023 - 現在

出版物

JOURNAL ARTICLE

Benjamin Matthews, Diego Allaix, Simon Wijte, Marcel Vullings (2026). Advancing non-destructive concrete compressive strength estimation: Large-Scale datasets and machine learning framework . NDT & E International.

Advancing non-destructive concrete compressive strength estimation @article{aa8cf16a80de4e0688d773d8f8aeff46, title = "Advancing non-destructive concrete compressive strength estimation: Large-Scale datasets and machine learning framework", abstract = "Non-destructive test (NDT) methods provide an indirect assessment of the compressive strength of in-situ concrete structures. While traditional static models effectively capture the behaviour of small-scale localised datasets, their accuracy diminishes when applied to larger, aggregated datasets, where increased variability in NDT measurements introduces greater uncertainty in predicting concrete compressive strength. This paper presents three exhaustive, largest-to-date NDT databases on the ultrasonic pulse velocity (UPV), rebound hammer (RH), and SonReb methods, comprising 16,531 test results from 115 studies. First, existing empirical models are evaluated against global dataset trends. New relationships are fitted to reflect the global behaviour of each NDT method, highlighting their innate limitations in capturing large-scale variability. A comprehensive three-phase machine learning (ML) program is then introduced, studying the effects of incomplete features with varying levels of missing data on model performance. Seven diverse ML models are included in Phase 1, while Phase 2 assesses different imputation strategies. Phase 3 integrates the top-performers with a Tree-Structured Parzen estimator (TPE) optimisation algorithm to refine hyperparameters and maximise performance. Across all phases, CatBoost regression emerged as the most robust predictive model due to the high proportion of categorical variables included within the databases. The TPE-CatBoost models achieved final R2 values of 0.928, 0.896, and 0.947 for UPV, RH, and SonReb, respectively. Finally, a Django-based web application was deployed on a cloud server (https://recreate-ndt.onrender.com/), allowing practitioners to generate real-time compressive strength predictions for new NDT results. These novel datasets and ML tools can power future innovation through more advanced data-driven modelling.", keywords = "Non-destructive, UPV, Rebound hammer, SonReb, Compressive strength, Machine learning, CatBoost, Non-destructive test", author = "Ben Matthews and Diego Allaix and Wijte, \{Simon N.M.\} and Marcel Vullings", year = "2026", month = mar, doi = "10.1016/j.ndteint.2025.103549", language = "English", volume = "158", journal = "NDT\&E International", issn = "0963-8695", publisher = "Elsevier", } . NDT&E International.

Modified Analytical Model for Shear Capacity of Corroded Columns @article{f47871c1a66b4ab4a7eb84c1bfb9a0d0, title = "Modified Analytical Model for Shear Capacity of Corroded Columns", abstract = "This paper proposes a series of empirical modifications to an existing three-step analytical model used to derive the cyclic shear capacity of circular reinforced concrete (RC) columns considering corrosive conditions. The results of 16 shear-critical RC columns, artificially corroded to various degrees and tested under quasistatic reversed cyclic loading, are used for model verification. The final model is proposed in a piecewise damage-state format relative to the measured damage of the steel reinforcement. New empirical decay coefficients are derived to determine the degraded material properties based on an extensive database of over 1380 corroded tensile tests. An additional database of 44 corroded RC circular piers is collected to assist in the modification of ductility-based parameters. Compared to the shear-critical test specimens, the model results indicate that the peak shear capacity can be predicted well across a range of deterioration severities (0 to 58.5\% average transverse mass loss), with a mean predictive ratio of ±8.60\%. As damage increases, the distribution of the corrosion relative to the location of the shear plane becomes a critical performance consideration, increasing predictive variance.", keywords = "analytical model, circular columns, corrosion, cyclic, reinforced concrete (RC), shear", author = "Benjamin Matthews and Alessandro Palermo and Allan Scott", note = "Publisher Copyright: {\textcopyright} 2025, American Concrete Institute. All rights reserved.", year = "2025", month = jul, day = "1", doi = "10.14359/51745466", language = "English", volume = "122", pages = "3--18", journal = "ACI Structural Journal", issn = "0889-3241", publisher = "American Concrete Institute", number = "4", } . ACI Structural Journal.

Impact of Artificial Corrosion Technique under Variable Acceleration on Morphological Efficacy in Reinforced Concrete Elements @article{1e064f8001124133a3af35c435e04bb4, title = "Impact of Artificial Corrosion Technique under Variable Acceleration on Morphological Efficacy in Reinforced Concrete Elements", abstract = "The popularity of artificially accelerated methodologies for inducing corrosion in reinforced concrete (RC) elements has increased exponentially over recent decades due to their ability to achieve broad damage spectra within practical timespans. However, because of the time constraints often associated with experimentation, large volumes of data are obtained through excessively accelerated applications, potentially compromising the efficacy of the resulting corrosion byproducts, morphology, crack behavior, and system behavior. This paper experimentally studies the effects of the degree of acceleration on the corrosion morphology, sectional properties, crack distribution, and structural performance of laboratory-scale and large-scale RC elements. Two experimental phases are considered: a small laboratory-scale phase consisting of 24 RC cylinders and a large-scale phase involving eight circular RC columns tested under cyclic shear loading. Both phases investigate two variations of the impressed-current method for achieving artificial corrosion damage at varying current densities. The impressed-current method is divided into constant saturation and wet-dry phasing. Analyses are conducted from the local morphological scale to the global structural response and cyclic behavior of RC columns. The results emphasize that a maximum current density of 200 μA=cm2 should be implemented to ensure realistic corrosion morphologies and crack behavior. Wet-dry phasing effectively improves key sectional parameters associated with naturally occurring localized patterns, including radius of gyration, maximum eccentricity, and area pitting factor. Columns subjected to wet-dry phasing at severe levels demonstrated more significant reductions in ultimate deflection and peak shear capacity due to measurable increases in localized pitting corrosion. The final failure mechanism of columns with low amounts of corrosion was not impacted by technique or current density.", keywords = "Corrosion, Chlorides, crack pattern, sectional properties, morphology, cyclic shear, Sectional properties, Morphology, Crack pattern, Cyclic shear", author = "Ben Matthews and Alessandro Palermo and Allan Scott", year = "2025", month = feb, doi = "10.1061/JMCEE7.MTENG-18819", language = "English", volume = "37", journal = "Journal of Materials in Civil Engineering", issn = "0899-1561", publisher = "American Society of Civil Engineers", number = "2", } . Journal of Materials in Civil Engineering.

Benjamin Matthews, Alessandro Palermo, Allan Scott (2025). Impact of Artificial Corrosion Technique under Variable Acceleration on Morphological Efficacy in Reinforced Concrete Elements . Journal of Materials in Civil Engineering.

Database and optimized machine learning prediction of the deteriorated response of corroded reinforced concrete beams @article{2fad30bf41c847a6a2c92f1864db96ab, title = "Database and optimized machine learning prediction of the deteriorated response of corroded reinforced concrete beams", abstract = "This research introduces an extensive database aggregating 54 experimental programs with 804 test specimens and 45 input parameters, investigating the implications of chloride-induced corrosion on the deteriorated mechanical response of corroded reinforced concrete beams. Several machine learning models are explored to determine the highest performing predictor for five key response variables – the residual ultimate moment capacity, residual capacity factor, yield load, yield displacement, and the ultimate displacement. Three existing analytical approaches are included for comparison to verify the efficacy of the trained statistical models. The optimized machine learning models significantly outperformed conventional analytical methods and achieved high levels of predictive accuracy. Ensemble tree-based learning algorithms, namely gradient-boosting regression trees and random forests, consistently produced the best predictions. Finally, the top-performing models are aggregated into a Python-based application that allows users to input new data and predict the mechanical response of a corroded beam failing in bending.", keywords = "Beams, Corrosion, Machine learning, Optimization, Regression, Reinforced concrete", author = "Benjamin Matthews and Alessandro Palermo and Tom Logan and Allan Scott", year = "2024", month = oct, doi = "10.1016/j.dibe.2024.100527", language = "English", volume = "19", journal = "Developments in the Built Environment", issn = "2666-1659", publisher = "Elsevier", } . Developments in the Built Environment.

Benjamin Matthews, Alessandro Palermo, Tom Logan, Allan Scott (2024). Database and optimized machine learning prediction of the deteriorated response of corroded reinforced concrete beams . Developments in the Built Environment.

Experimental testing and predictive machine learning to determine the mechanical characteristics of corroded reinforcing steel @article{98f76f545a4f4500bd4c628444dda692, title = "Experimental testing and predictive machine learning to determine the mechanical characteristics of corroded reinforcing steel", abstract = "Chloride-induced deterioration of reinforcing steel bars has become a densely researched topic over the past several decades because of the severe ramifications to the structural reliability of aging infrastructure. The ever-growing volume of experimental and field data continually enables advances in the field through deeper micro-macro analyses and various modeling applications. The purpose of this paper is twofold. First, an experimental program is introduced, describing the tensile testing of 284 artificially corroded, 25 mm diameter deformed Grade500E reinforcing bars. Secondly, the mechanical characteristics of corroded bars are predicted through a collection of regression-based machine learning algorithms. Models are trained and tested on a database of 1387 tensile tests compiled from 25 other experimental programs available in the literature. The complete database includes 19 input parameters used to predict nine key mechanical properties of the corroded steel bars. Nine machine learning models were selected from a balanced assortment of algorithm typologies to determine the most appropriate methodology for each response variable. The adaptive-neuro fuzzy inference system (ANFIS) model was found to have the strongest individual predictive ability across all models. Meanwhile, ensemble tree-based learning algorithms categorically provided the most consistently high-performing models over the selected response variables.", keywords = "Corrosion, Reinforced concrete, Mechanical properties, Machine learning, Tensile testing", author = "Ben Matthews and Alessandro Palermo and Tom Logan and Allan Scott", year = "2024", month = aug, day = "9", doi = "10.1016/j.conbuildmat.2024.137023", language = "English", volume = "438", journal = "Construction and Building Materials", issn = "0950-0618", publisher = "Elsevier", } . Construction and Building Materials.

Benjamin Matthews, Alessandro Palermo, Tom Logan, Allan Scott (2024). Experimental testing and predictive machine learning to determine the mechanical characteristics of corroded reinforcing steel . Construction and Building Materials.

Benjamin Matthews, Alessandro Palermo, Allan Scott (2024). Cyclic shear testing of artificially corroded reinforced concrete short circular piers . Structures.

Cyclic shear testing of artificially corroded reinforced concrete short circular piers @article{f6afb5eb604240d0920d3d4aebfb0f93, title = "Cyclic shear testing of artificially corroded reinforced concrete short circular piers", abstract = "Chloride-induced deterioration of reinforced concrete (RC) structures is an increasingly vital topic among asset management and structural maintenance fields, as many RC bridges and other structures are reaching the end of their initial design lives. Deterioration of the transverse reinforcement in RC piers leads to the direct loss of shear and confinement-related mechanical properties as degradation advances with time. This research investigates the seismic response of fourteen short RC circular piers subjected to artificial chloride-induced corrosion. Each pier is designed to trigger a shear-dominated failure and artificially corroded at two current densities. A maximum average mass loss of 10.5\% and 43.0\% was measured in the longitudinal and spiral reinforcement, respectively. The influence of confinement effectiveness, spiral diameter, and aspect ratio on the rate of shear capacity degradation are included as test parameters in this study. Results indicate that shear deformation begins to govern performance at moderate to severe deterioration, which precedes the onset of brittle shear-dominated failure modes. Ultimate deflection is more adversely affected by increasing corrosion than all other measured properties, with a maximum observed reduction of 70.9\% compared to a maximum 37.5\% reduction observed in peak shear capacity. Several existing theoretical models are introduced and evaluated on their ability to predict the shear capacity of the experimental tests. Based on the results, those models directly evaluating the circular section without needing an equivalent section transformation offer the most accurate and reliable predictions.", keywords = "Brittle failure, Corrosion, chlorides, Cyclic loading, Reinforced concrete, Shear", author = "Ben Matthews and Alessandro Palermo and Allan Scott", year = "2024", month = may, doi = "10.1016/j.istruc.2024.106275", language = "English", volume = "63", journal = "Structures", issn = "2352-0124", publisher = "Elsevier", } . Structures.

Benjamin Matthews, Alessandro Palermo, Allan Scott (2023). Overview of the cyclic response of reinforced concrete members subjected to artificial chloride‐induced corrosion . Structural Concrete.

Overview of the cyclic response of reinforced concrete members subjected to artificial chloride-induced corrosion @article{38ea495da270423b956ab86b10f6606d, title = "Overview of the cyclic response of reinforced concrete members subjected to artificial chloride-induced corrosion", abstract = "Reinforced concrete (RC) elements suffer continuous deterioration across their lifetime from in situ aggressive environmental factors. Chloride-induced deterioration of the steel reinforcement presents significant repercussions on the mechanical and seismic behavior of affected RC members. This paper introduces and summarizes experimental work carried out at the University of Canterbury, investigating the long-term implications of chloride-induced deterioration on various mechanical properties. The impact of chloride corrosion on bond-slip behavior, the cyclic flexural response of large-scale RC piers, and the shear resistance of short RC columns, with particular focus on the latter, is reported. Results indicate that corrosion damage presents more severe consequences to the displacement ductility of RC columns than the load-bearing capacity. Transverse reinforcement presents a much greater risk and vulnerability to deterioration, leading to a transition in failure mechanism from a ductile response to a brittle shear/shear-bond failure. Reduced confinement negatively impacts the stress–strain response of the confined concrete and bond-slip behavior in cracked members, where friction is relied upon as the primary load-transfer mechanism once mechanical interlock is lost through the generation of rust by-products and concrete splitting.", keywords = "Bond, Corrosion, Reinforced concrete, Shear, Ductility, corrosion, ductility, reinforced concrete, shear, bond", author = "Ben Matthews and Alessandro Palermo and Allan Scott", year = "2023", month = feb, doi = "10.1002/suco.202200365", language = "English", volume = "24", pages = "100--114", journal = "Structural Concrete", issn = "1464-4177", publisher = "Wiley-Blackwell", number = "1", } . Structural Concrete.

CONFERENCE PAPER

Treatment of Uncertainties in the Semi-Probabilistic Design of Precast Concrete Structures with Reclaimed Elements @inproceedings{139bc5e5490840bb973006d041cc78d5, title = "Treatment of Uncertainties in the Semi-Probabilistic Design of Precast Concrete Structures with Reclaimed Elements", abstract = "The construction industry contributes to a significant proportion of the global annual car-bon emissions. To aid in mitigating the negative carbon implications and extend the lifetime usability of concrete components through circular design, the ReCreate project aims to investigate the deconstruction of precast concrete buildings and the reuse of reclaimed structural elements in new designs. In conventional concrete design and code specifications such as EN1990:2002, full and semi-probabilistic methods are available to estimate the reliability of structures. In the case of reuse, however, materials have aged, developed, and plateaued over decades of service, altering the constitutive assumptions underlying many design methods. Essential resistance model considerations include the mechanical properties of the concrete and reinforcement steel, reinforcement layout, geometrical properties, degradation mechanisms, serviceability damage, and bond integrity. Varying levels of information from the original design will be available, including undocumented alterations performed during construction or renovation stages. Several variables, such as element geometry and material resistances, can be determined more precisely through post-deconstruction testing and inspections. To understand the implications of material variability in reclaimed precast concrete elements, this paper first provides an overview of existing probabilistic theory and material safety factors for conventional designs, with particular reference to Eurocode 2. An inventory of the relevant variables affecting the reliability of reused elements is presented and discussed. Finally, the reliability of non-destructive testing on reclaimed and in-service rein-forced concrete elements is discussed.", keywords = "Reliability, reuse, uncertainty, probabilistic design, partial factors, non-destructive testing", author = "Ben Matthews and Donkervoort, \{Jilke R.W.\} and Diego Allaix and Vullings, \{Marcel W.F.\} and Wijte, \{Simon N.M.\}", year = "2024", month = nov, day = "13", language = "English", isbn = "9782940643257", volume = "67", pages = "847--858", editor = "Rick Henry and Alessandro Palermo", booktitle = "20th fib Symposium Proceedings in Christchurch - 2024, New Zealand", note = "ReConStruct - Resilient Concrete Structures ; Conference date: 11-11-2024 Through 13-11-2024", } . 20th fib Symposium Proceedings in Christchurch - 2024, New Zealand.

DATA SET

Benjamin Matthews, Alessandro Palermo, Allan Scott(2024). Monotonic Tensile Testing of Corroded Reinforcing Steel Bars Database . Zenodo. Zenodo

Benjamin Matthews, Alessandro Palermo, Allan Scott(2023). Monotonic Flexural Testing of Corroded Reinforced Concrete Beams Database . Zenodo. Zenodo

DISSERTATION THESIS

Benjamin James Matthews(2023). Deteriorated capacity assessment of aging bridge structures in seismic areas complimented by artificial intelligence modeling . University of Canterbury