Dimitrie Iordan & Florin Alexandru Stan, How are AI driven programs used in civil engineering and will those fully take over this industry in the near future? — International Journal of Sciences and Innovation Engineering, Volume 3, Issue 8, August 2026.
Pengzhen Lu, Shengyong Chen & Yujun Zheng, Artificial Intelligence in Civil Engineering — Mathematical Problems in Engineering, 2012.
A. D. Rafindadi, B. Kado, A. M. Gora, I. B. Dalha & M. M. Aliyu, Artificial Intelligence’s (AI) Role in Civil Engineering: A Brief Overview — 3rd International Civil Engineering Conference (ICEC 2025).

The three works should not be treated as equivalent evidence. Paper 2 is an established historical review; Paper 3 is a more recent literature review with an explicitly described selection protocol; Paper 1 addresses a future-oriented question about AI and industry replacement, but the full article could not be independently retrieved from the DOI supplied for this review. Accordingly, claims about Paper 1’s detailed methods, evidence and conclusions are not inferred beyond the supplied bibliographic information.

The central CLAC360 question is therefore not “Is AI important?” That proposition is already well supported.

The harder question is:

Does the available evidence justify the stronger proposition that AI will substantially replace, or “take over,” civil engineering in the near future?

The evidence examined here does not justify that conclusion. It supports a more complicated proposition: AI is becoming a significant engineering capability, particularly in prediction, optimization, computer vision, monitoring, information processing and decision support. But civil engineering remains a socio-technical profession involving physical uncertainty, legal responsibility, safety-critical judgment, field conditions, incomplete information, interdisciplinary coordination and accountability. Current evidence indicates augmentation and task automation rather than wholesale professional replacement.

Research Paper Information Boxes

Paper 1 — Iordan & Stan

Item	CLAC360 Assessment
Title	How are AI driven programs used in civil engineering and will those fully take over this industry in the near future?
Authors	Dimitrie Iordan; Florin Alexandru Stan
Publication Date	August 2026, according to the supplied citation
Journal	International Journal of Sciences and Innovation Engineering
Volume / Issue	Vol. 3, Issue 8
Study Field	Artificial intelligence; civil engineering; engineering technology
Study Type	Review/discussion paper, based on the supplied bibliographic information; detailed classification is provisional because the full article was not independently retrievable
Research Question	How are AI-driven programs being used in civil engineering, and will AI fully take over the industry in the near future?
CLAC360 Rating	Provisional: 4.5/10 for professional evidentiary use
Evidence Strength	Weak / currently unverifiable for detailed methodological claims
Current Scientific Status	Plausible topic, but insufficiently verifiable as an evidence base from the accessible record

The journal identifies itself as a peer-reviewed, open-access multidisciplinary engineering journal and reports ISSN 3049-0251. Its official site states that it publishes research papers, reviews, short communications and case studies. The ISSN Portal independently confirms the journal’s ISSN and publication identity.

However, an important verification problem exists: the supplied DOI did not resolve successfully during this assessment, and the journal’s publicly accessible archive information retrieved during the search did not independently expose the supplied August 2026 article. The journal itself is verifiable; the specific article’s full text and detailed scientific record were not.

That distinction matters. A publication’s existence is not equivalent to independent verification of its scientific claims.

Paper 2 — Lu, Chen & Zheng

Item	CLAC360 Assessment
Title	Artificial Intelligence in Civil Engineering
Authors	Pengzhen Lu, Shengyong Chen, Yujun Zheng
Publication Date	5 December 2012
Journal	Mathematical Problems in Engineering
Volume	2012
Article ID	145974
Study Field	Artificial intelligence; civil engineering; computational intelligence
Study Type	Narrative literature review
Research Question	What AI methods had been developed and applied in civil engineering up to 2012, and what future directions were emerging?
CLAC360 Rating	7.4/10 as a historical scientific review; 5.9/10 as a current standalone technical reference
Evidence Strength	Moderate for historical state-of-the-field assessment; weak for current predictions
Current Scientific Status	Historically valuable; substantially incomplete as a description of contemporary AI

The publisher record confirms the title, authors, DOI, publication date and classification as a review article. The authors explicitly state that the paper summarizes literature on AI applications in civil engineering published prior to 2012.

This temporal boundary is essential to interpreting the paper fairly. It should not be criticized because it does not discuss transformers, large language models, modern computer vision, foundation models, generative AI or contemporary digital twins. Those technologies were not the state of the field in 2012.

The correct CLAC360 question is whether the paper accurately captured the AI paradigm that existed when it was written.

Paper 3 — Rafindadi et al.

Item	CLAC360 Assessment
Title	Artificial Intelligence’s (AI) Role in Civil Engineering: A Brief Overview
Authors	A. D. Rafindadi, B. Kado, A. M. Gora, I. B. Dalha, M. M. Aliyu
Publication Date	August 2025
Conference	3rd International Civil Engineering Conference (ICEC 2025), Federal University of Technology, Minna
Study Field	Artificial intelligence; civil engineering; construction; infrastructure
Study Type	Literature review / mini-review claiming a systematic literature-selection procedure
Research Question	What are the applications, developments, challenges and future directions of AI in civil engineering?
CLAC360 Rating	6.5/10 as a broad orientation review
Evidence Strength	Moderate for mapping applications; weak-to-moderate for stronger claims about transformation, cost savings and future dominance
Current Scientific Status	Supported but incomplete

The indexed full text states that the authors searched Web of Science and Scopus, initially identifying 70 and 89 records respectively, then evaluating 47 and 72 full texts, ultimately selecting 53 studies plus seven manually identified documents for a final corpus of 60 studies.

That gives Paper 3 considerably more methodological information than Paper 1 and a more explicit selection framework than Paper 2.

The Three Studies in One Minute

Paper 1

The paper asks an unusually consequential question: AI may be transforming civil engineering, but will it actually take over the industry?

That question matters because civil engineering is not simply an information-processing occupation. It connects computational decisions to physical structures, public safety, construction operations, legal obligations and uncertain environments.

The principal weakness of the present assessment is that the complete article could not be independently retrieved from the supplied DOI. Consequently, CLAC360 cannot responsibly determine whether the paper’s evidence supports its conclusions.

Its question is important.

Its evidentiary status is presently uncertain.

Paper 2

Lu, Chen and Zheng surveyed the AI technologies being applied to civil engineering by 2012, including evolutionary computation, neural networks, fuzzy systems, expert systems, reasoning, classification and learning.

Its fundamental contribution was not to demonstrate that one AI algorithm outperformed a conventional engineering method in a controlled experiment.

It was to establish a map of an emerging research field. For 2012, that was useful. For 2026, it is historical rather than sufficient.

Paper 3

Rafindadi et al. attempt a broader and more contemporary mapping of AI applications across structural engineering, construction management, geotechnical engineering, transportation and environmental applications.

The review identifies genuine application areas, but its narrative sometimes moves too quickly from ‘AI can perform a task’ to ‘AI is transforming the discipline’ and occasionally toward ‘AI will become indispensable’.

The first statement can often be demonstrated. The second requires stronger empirical evidence. The third is a forecast. Those three propositions should not be treated as scientifically interchangeable.

What Are the Studies Actually Claiming?

Paper 1

The central implied hypothesis is essentially:

AI-driven programs are increasingly capable of performing important civil-engineering functions, and this raises the possibility that AI could eventually replace substantial portions of the civil-engineering industry.

The crucial distinction is between task substitution and professional substitution.

A system that detects cracks does not replace structural engineering.
A model that predicts traffic does not replace transportation engineering.
A generative design algorithm does not automatically replace design responsibility.
A scheduling model does not replace construction management.

The question therefore needs to be decomposed into measurable propositions:

Can AI perform specific civil-engineering tasks?
Can it perform them accurately?
Can it outperform conventional methods?
Can it generalize to new projects?
Can engineers understand and validate its outputs?
Can it operate reliably under unusual conditions?
Can it satisfy professional, legal and safety requirements?
Can organizations integrate it economically?
Can it assume responsibility for decisions?

Evidence for proposition 1 does not establish proposition 9. This distinction is fundamental.

Paper 2

Lu et al. primarily claim that AI provides useful computational alternatives for complex civil-engineering problems and summarize the techniques available at the time. Their claims are predominantly:

descriptive;
technological;
literature-based;
prospective.

They are not principally causal claims. The paper does not establish that AI will replace civil engineers. Indeed, its discussion is better understood as describing AI as a developing computational capability within engineering.

Paper 3

Paper 3 makes several categories of claims.

Descriptive claims

AI is being used for:

structural health monitoring;
damage detection;
prediction;
concrete-strength estimation;
construction planning;
cost estimation;
safety monitoring;
resource allocation;
geotechnical prediction;
transportation;
environmental monitoring.

These claims are broadly consistent with the subsequent literature.

Performance claims

The paper repeatedly associates AI with:

greater accuracy;
efficiency;
cost reduction;
safety improvement;
sustainability;
optimization.

These claims require application-specific evidence. An AI model can be more accurate on a benchmark dataset without necessarily producing greater real-world engineering value.

Forecasting claims

The paper predicts continued expansion of AI and increasingly deep integration with IoT, cloud computing, robotics and augmented reality. It is plausible. But plausibility is not evidence of future realization.

Strongest implicit claim

The conclusion describes AI as becoming “indispensable” and capable of transforming civil engineering. That is considerably stronger than the evidence needed to demonstrate that individual AI applications work.

Research Design

Paper 1

Because the complete article could not be independently retrieved, CLAC360 cannot verify:

whether the work is systematic or narrative;
the databases searched;
inclusion/exclusion criteria;
number of sources;
evaluation criteria;
whether empirical case studies were independently assessed;
whether competing evidence was considered.

Therefore, assigning a more specific methodological classification would constitute speculation.

CLAC360 judgment: evidence-constrained rather than evidence-negative.

Paper 2

This is a narrative literature review. That design is appropriate for its stated historical purpose. It is not appropriate for answering questions such as:

“Does AI improve civil-engineering project outcomes by X%?”

or:

“Will AI replace civil engineers?”

A narrative review can identify themes, technologies and research directions. It cannot automatically establish causal effect sizes. The authors themselves frame the work as a synthesis of literature available prior to 2012.

Paper 3

Paper 3 provides a much more explicit review methodology.

It reports:

Web of Science searching;
Scopus searching;
keyword-based retrieval;
screening;
deduplication;
title/abstract assessment;
full-text evaluation;
two-reviewer assessment;
third-reviewer arbitration;
supplementary manual searching;
PRISMA 2020 reporting.

This is a meaningful methodological strength. However, “using PRISMA” does not automatically make a review systematic in the strongest scientific sense. PRISMA is primarily a reporting framework. It improves transparency; it does not guarantee that the search strategy, inclusion criteria, risk-of-bias assessment or synthesis methodology are scientifically optimal. That distinction is frequently lost in review papers.

Data and Sample

Paper 1

Not independently assessable from the accessible record. The absence of a retrievable full text prevents responsible evaluation of its evidence base.

Paper 2

The “sample” is published literature. The major limitation is historical coverage. The paper intentionally focuses on literature available before 2012. Therefore its sample is not representative of today’s AI literature. That is not a flaw in the original study design. It is a limitation that arises when a historical review is reused as a contemporary source.

Paper 3

The reported final corpus of 60 studies is respectable for a conference review, but several questions remain.

The authors state that:

Web of Science yielded 70 potential studies;
Scopus yielded 89;
47 and 72 full texts were retrieved/evaluated;
53 ultimately met the inclusion criteria;
seven additional documents were identified manually;
final corpus = 60.

This is transparent enough to reconstruct the broad selection pathway. However, the review would be stronger if it clearly reported, in a reproducible form:

the complete search strings;
exact search dates;
database field restrictions;
language restrictions;
exclusion reasons at each stage;
risk-of-bias assessment;
study-quality scoring;
effect-size extraction;
evidence grading.

Without those elements, the review is closer to a structured literature overview than a high-rigor evidence synthesis.

Methodology

Paper 1

The key methodological issue is not simply whether AI examples exist. It is whether the authors distinguish:

demonstration → validation → deployment → organizational adoption → professional substitution.

A study arguing about industry takeover must address all five levels. If it only demonstrates applications, the conclusion cannot logically reach industry replacement.

Paper 2

The methodology is appropriate for mapping a multidisciplinary field. Its strength is breadth. Its weakness is that many AI techniques discussed in 2012 were still experimental or specialized.

A literature review can report that researchers were using neural networks, fuzzy systems or evolutionary computation. That does not establish that these systems were reliable enough for widespread engineering deployment.

The review’s historical importance is therefore greater than its direct contemporary decision value.

Paper 3

The methodology is strongest when the paper describes what AI is being used for. It becomes weaker when it moves toward how much better AI is or how profoundly AI will transform the profession. For example, detecting surface cracks from imagery is a technically meaningful task.

But an engineering inspection system must also confront:

lighting;
occlusion;
surface contamination;
unusual crack morphology;
false positives;
false negatives;
sensor calibration;
dataset shift;
environmental conditions;
inspection standards;
engineer verification;
liability.

A high classification accuracy on a test dataset is therefore not equivalent to a validated engineering inspection process.

Statistical Analysis

Paper 1

No responsible statistical assessment is possible without the complete article.

Paper 2

The paper is a narrative review and therefore does not present a conventional statistical meta-analysis. The absence of pooled effect sizes is not inherently a defect because the paper was not presented as a quantitative meta-analysis.

However, this means readers should not extract numerical conclusions about AI effectiveness from the review as though they were pooled estimates.

Paper 3

Paper 3 does not appear to perform a formal quantitative meta-analysis. The synthesis is predominantly narrative. That creates an important limitation. Consider three hypothetical studies:

Model A achieves 95% accuracy on a controlled dataset.
Model B achieves 89%.
Model C achieves 82%.

These numbers cannot simply be averaged into a meaningful engineering conclusion. They may involve different:

datasets;
definitions;
failure modes;
baselines;
sample sizes;
validation procedures;
performance metrics.

A systematic review of AI therefore needs to distinguish application prevalence from performance evidence. Paper 3 is much stronger at the former than the latter.

Association, Causation, Mechanism and Generalizability

This distinction is particularly important for all three works.

Association

AI systems are associated with activities such as:

faster prediction;
automated monitoring;
optimization;
improved classification.

This is well supported at the application level.

Causation

The stronger statement:

“AI causes construction projects to become cheaper, safer and faster.”

requires comparative evidence.

That means ideally comparing projects or processes with and without AI while controlling for other influences. The reviewed papers generally do not establish such causal effects across the civil-engineering industry.

Mechanism

There are plausible mechanisms:

AI → faster data processing → earlier detection → earlier intervention → potentially reduced losses.

That mechanism is technically plausible. But every arrow requires validation in the relevant engineering environment.

Generalizability

This is the hardest problem. A model trained on:

one bridge type,
one climate,
one sensor configuration,
one concrete dataset,
one road network,
one construction company

does not automatically generalize to another. Civil engineering is highly context-dependent. Therefore:

Successful AI application ≠ universally transferable AI capability.

Evidence Strength

Work	Evidence Classification	CLAC360 Reasoning
Iordan & Stan	Weak / currently unverifiable	Important question, but full article and detailed evidence could not be independently verified
Lu et al. 2012	Moderate	Legitimate historical literature review, but necessarily bounded by pre-2012 evidence
Rafindadi et al. 2025	Moderate	More recent and methodologically structured, but predominantly narrative and heterogeneous evidence
Claim that AI is already replacing civil engineering as a profession	Unsupported	Current evidence does not establish industry-wide professional replacement
Claim that AI is increasingly useful for civil-engineering tasks	Strong	Supported by multiple application reviews and current professional evidence
Claim that AI will become increasingly integrated into civil engineering	Strongly plausible	Supported by research trends and industry adoption evidence
Claim that AI will fully take over civil engineering in the near future	Unsupported	No adequate empirical basis for such a sweeping forecast

What the Studies Get Right

Paper 1

Its greatest strength is the question itself. The “will AI take over?” framing forces attention toward the boundary between:

automation;
augmentation;
substitution;
professional responsibility.

That is a more useful question than simply listing AI applications.

Paper 2

Lu et al. correctly recognized that AI in civil engineering was not one technology. They identified a family of computational approaches, including evolutionary computation, neural networks, fuzzy systems, expert systems, reasoning, classification and learning. That conceptual framing remains valid.

Modern AI has changed enormously, but civil engineering still uses multiple computational paradigms rather than a single universal “AI method.”

The paper also correctly recognized applications across structural, bridge, geotechnical, underground, road and infrastructure-health domains.

Paper 3

The strongest contribution is breadth. The review demonstrates that AI is no longer confined to a narrow research niche.

Recent research independently confirms this broadening. A 2025 review covering 594 quantitative and 91 qualitative studies found particularly strong research activity around safety monitoring/control and construction-process management.

Another 2025 review synthesizing 106 peer-reviewed sources covered structural analysis, concrete, geotechnical engineering, hydraulic systems, transportation, construction management, BIM and sustainability.

Thus, Paper 3’s basic observation that AI has spread across civil-engineering domains is well supported.

Where the Studies Are Weak

Paper 1

The primary weakness for CLAC360 is verification. A critical review cannot assess:

statistical methodology;
literature-selection quality;
evidence hierarchy;
citation accuracy;
technical examples;
counterevidence;
conclusions

without the underlying document. That prevents a stronger scientific judgment.

Paper 2

Its major weakness today is obsolescence of scope, not necessarily poor scholarship. The 2012 review predates:

deep-learning expansion in engineering;
modern computer vision;
large-scale sensor ecosystems;
modern BIM integration;
digital twins at today’s maturity;
large language models;
generative AI;
foundation models;
contemporary autonomous robotics;
current AI governance concerns.

Therefore, it cannot serve as a comprehensive modern review.

Paper 3

Paper 3 has more substantive weaknesses.

1. Heterogeneous evidence

The reviewed literature spans fundamentally different AI tasks.

A crack detector, traffic predictor, concrete-strength model and construction scheduler cannot be treated as though they constitute one homogeneous evidence base.

2. Performance language is sometimes too broad

“More accurate,” “more efficient,” “safer” and “cost-effective” require defined baselines and measurable outcomes.

3. Benchmark-to-field gap

AI performance in controlled datasets does not automatically translate to field performance.

4. Forecasting exceeds evidence

The conclusion moves beyond describing current applications toward a strong vision of future transformation.

That is legitimate as professional forecasting, but it should not be presented as an empirical finding.

5. Limited treatment of engineering liability

The engineering problem is not merely:

Can the algorithm produce a correct answer?

It is also:

Who verifies the answer, who signs it, who is responsible if it is wrong, and under what professional framework may it be used?

Those questions deserve much more attention in any argument about AI replacing engineers.

What the Studies Do NOT Establish

This is the most important section of the entire comparison. The three papers do not establish that:

AI will replace civil engineers as a profession;
AI can independently assume engineering responsibility;
AI can reliably handle all unusual field conditions;
AI can replace engineering judgment under incomplete information;
AI outputs are inherently more accurate than conventional engineering methods;
AI necessarily reduces total project cost;
AI necessarily improves safety;
AI models generalize automatically between infrastructure projects;
AI can replace regulatory and professional accountability;
AI-generated engineering decisions can be accepted without human validation;
current AI adoption demonstrates imminent industry-wide automation.

These propositions require a much stronger evidence base.

The Strongest Counterevidence

The strongest counterevidence to the idea of imminent wholesale replacement is not philosophical. It is observed industry adoption.

The 2025 RICS Global Construction Monitor survey collected more than 2,200 professional responses worldwide. It found that regular AI use in specific construction processes remained below 12%, only 1.5% reported AI use across multiple processes, and less than 1% reported fully embedded organization-wide AI use.

The barriers were also highly practical:

lack of skilled personnel — 46%;
integration with existing systems — 37%;
data quality and availability — 30%;
high implementation cost — 29%;
unclear return on investment — 28%;
lack of standards and guidance — 25%.

These numbers do not prove that AI cannot eventually become dominant. They do demonstrate that “AI capability” and “AI deployment at industrial scale” are very different things.

A 2025 systematic review of AI implementation in construction similarly characterized deployment as a sociotechnical process, identifying organizational, technological, stakeholder and data dimensions rather than treating implementation as a purely algorithmic problem. That is highly relevant to the replacement question.

Reproducibility and Robustness

Paper 1

Cannot be adequately assessed from the independently accessible record.

Paper 2

Its literature synthesis is reproducible in principle but is historically bounded. Its conclusions should therefore be reproducible as a representation of the literature available at the time, not interpreted as a current evidence synthesis.

Paper 3

The reported use of Web of Science and Scopus, screening stages and multiple reviewers is positive. However, reproducibility would be stronger if the complete search strings, exclusion table, quality assessment and study-level extraction were available. The difference is important:

Transparent selection ≠ reproducible evidence synthesis.

What Has Changed Since Publication?

Since 2012

Almost everything about the computational environment has changed. Lu et al.’s basic insight remains valid: AI can provide computational approaches to difficult civil-engineering problems. But the technological center of gravity has shifted. The field now includes:

deep learning;
computer vision;
IoT-linked AI;
digital twins;
advanced predictive modeling;
robotics;
natural-language systems;
generative AI;
large language models;
hybrid AI;
explainable AI;
physics-informed learning.

Contemporary reviews now treat AI across substantially more civil-engineering domains than were emphasized in 2012.

Since 2025

The central development has not been evidence that AI has replaced engineers. Instead, research has increasingly focused on how to integrate AI into existing engineering systems. Recent reviews emphasize:

data quality;
model transparency;
standardization;
digital twins;
explainability;
interoperability;
workforce capability;
organizational adoption.

A separate 2025 study of LLM adoption in the AEC industry found that performance expectations, ease of use, social influence and service reliability affect adoption intention, while training and organizational support remain important. This is evidence of technological adoption—not professional extinction.

Current Scientific Status

Paper 1

Plausible but uncertain. Its central question is legitimate, but the specific article cannot be given a stronger scientific status without independent access to the full work.

Paper 2

Historically well established; scientifically incomplete for contemporary use.

The paper remains useful for understanding how AI was conceptualized and applied in civil engineering around 2012. It should not be used as the principal source for a 2026 assessment of AI.

Paper 3

Supported but incomplete.

Its broad observation that AI has significant and expanding applications in civil engineering is consistent with contemporary literature. Its stronger transformation-oriented statements should be treated as forecasts and professional interpretation rather than established empirical findings.

CLAC360 Scientific Assessment

Dimension	Paper 1	Paper 2	Paper 3
Scientific validity	5/10*	7.5/10	7/10
Methodological rigor	4/10*	6.5/10	7/10
Evidence quality	4/10*	7/10	6.5/10
Causal inference	3/10*	N/A	4/10
Reproducibility	3/10*	6.5/10	6/10
External validity	4/10*	6/10	6.5/10
Practical relevance	7/10	7/10	8/10
Current relevance	8/10	4/10	8/10
Historical value	5/10	9/10	7/10
Overall CLAC360 rating	4.5/10*	7.4/10	6.5/10

*Paper 1’s component scores are verification-constrained, not a finding that the underlying article necessarily has poor methodology.

Why the ratings differ

The rating is not a measure of how much CLAC360 “likes” a paper.

It measures suitability for a defined purpose.

Paper 2 scores highest historically because it is a legitimate early review that successfully maps an emerging field.

Paper 3 is more useful today because it covers contemporary applications and uses a more explicit literature-selection methodology, but its broad narrative conclusions are stronger than the quantitative evidence permits in places.

Paper 1 receives a provisional low professional-evidence score because the actual article could not be independently retrieved and therefore cannot be subjected to the same methodological audit. It would be scientifically irresponsible to invent a higher rating from its title and bibliographic information alone.

Practical Implications

Engineering

The evidence supports AI as an engineering multiplier. High-value applications include:

condition assessment;
predictive maintenance;
image-based inspection;
forecasting;
optimization;
traffic prediction;
construction monitoring;
resource planning;
document analysis;
design-space exploration.

A 2024 critical review of AI in infrastructure construction found research concentrated particularly around safety monitoring/control and process management. The appropriate engineering model is therefore:

Engineer + validated AI + independent verification

rather than:

AI instead of engineer.

Industry

The evidence supports investment in AI where:

the task is repetitive;
sufficient data exist;
the output can be validated;
errors are detectable;
economic value is measurable;
integration costs are manageable.

The RICS evidence strongly suggests that organizational integration remains a major bottleneck.

Policy

Policies should focus on:

accountability;
data governance;
validation;
cybersecurity;
professional responsibility;
auditability;
interoperability;
procurement standards;
human oversight.

The policy mistake would be to regulate AI as though it were simply another software package. The other mistake would be to assume that AI should be prohibited because it is imperfect. Engineering already operates under uncertainty. The objective should be controlled uncertainty, not technological purity.

Technology

The strongest near-term architecture is likely to be hybrid.

For example:

Physics + engineering equations + domain data + machine learning + human validation

rather than a purely data-driven black box. Recent reviews explicitly identify physics-informed approaches, explainable AI, transfer learning and AI-enabled digital twins as important directions for overcoming current limitations.

Research

The research frontier should move beyond:

“Can AI predict X?”

toward:

“Under what conditions does AI improve engineering decisions compared with the best conventional alternative?”

That means future research needs:

external validation;
field datasets;
cross-project testing;
uncertainty quantification;
failure analysis;
benchmark datasets;
human-AI comparison;
cost-benefit analysis;
safety analysis;
long-term monitoring.

That would produce much more useful engineering evidence than another isolated model reporting a high accuracy score.

Who Should Read These Papers?

Paper 1

Best suited to:

students;
engineers interested in AI’s future;
technology strategists;
educators;
readers interested in professional transformation.

But it should not be used alone to support a major organizational decision about AI replacing engineering personnel until its evidence can be independently verified.

Paper 2

Especially useful for:

researchers entering AI-in-civil-engineering research;
historians of engineering computation;
graduate students;
readers studying the evolution of computational intelligence.

It remains a useful historical starting point.

Paper 3

Best suited to:

civil-engineering students;
researchers entering AI applications;
construction managers;
infrastructure researchers;
engineers seeking an overview of application areas;
technology strategists.

Its strongest value is orientation. It should not be treated as a quantitative proof that AI produces specific percentage improvements across civil engineering.

Who Should Not Rely on These Papers?

None of the three papers should be treated as sufficient evidence for:

approving an autonomous safety-critical engineering system;
removing professional engineering review;
eliminating engineering positions;
certifying AI-generated structural designs without qualified review;
establishing regulatory policy;
assigning a universal ROI to AI;
claiming that AI is already capable of replacing civil engineers.

For such decisions, primary validation studies, applicable engineering standards, professional regulations, project-specific testing and documented risk assessment are necessary.

Why This Research Matters

The subject aligns directly with a central theme:

The difference between computation and understanding.

Civil engineering has always used mathematical models to convert uncertain physical reality into manageable calculations. AI adds another layer. A conventional engineering model may say R=f(X), where an engineer defines the variables, equations and physical assumptions.

A machine-learning system may instead approximate, R̂ = f_θ(X), where the function is learned from data.

The second approach can be extraordinarily powerful. But it creates a new engineering question:

What happens when the future does not resemble the data used to train the model?

That is not merely an AI question. It is the classic engineering problem of extrapolation, uncertainty and model validity appearing in a new computational form. This is why the strongest future of AI in civil engineering is unlikely to be “engineers versus AI.” It is more likely to be:

engineers who understand AI versus engineers who merely use AI.

Final Verdict

Paper 1 — Iordan & Stan

Scientific value: Potentially useful as a contemporary discussion of AI and professional transformation.

Evidence strength: Weak / currently unverifiable at the article level.

Major limitation: The full article could not be independently retrieved from the supplied DOI, preventing a defensible audit of its methodology and evidence.

Current status: The question is highly relevant, but its conclusions should not be treated as established scientific evidence without examination of the complete paper.

Appropriate use: Discussion, education and hypothesis generation.

CLAC360 Rating: 4.5/10 — Limited for professional evidentiary use, provisional.

Paper 2 — Lu, Chen & Zheng

Scientific value: Significant historical review of early AI applications in civil engineering.

Evidence strength: Moderate.

Major limitation: Its literature boundary is essentially the pre-2012 AI landscape.

Current status: Historically valuable, but no longer sufficient as a contemporary review.

Appropriate use: Historical context, research orientation and understanding the evolution of AI in civil engineering.

CLAC360 Rating: 7.4/10 — Strong as a historical review; substantially less useful as a standalone current reference.

The publisher confirms that the paper’s objective was specifically to summarize AI developments and applications represented in the literature before 2012.

Paper 3 — Rafindadi et al.

Scientific value: Useful contemporary overview showing the breadth of AI applications in civil engineering.

Evidence strength: Moderate.

Major limitation: Heterogeneous literature is synthesized primarily narratively, while some broad statements about efficiency, cost, safety and future transformation go beyond what the underlying evidence can uniformly establish.

Current status: Supported but incomplete.

Appropriate use: Orientation, education, research-topic discovery and strategic discussion.

CLAC360 Rating: 6.5/10 — Mixed-to-strong overview, but not sufficient evidence for sweeping claims about AI replacing civil engineering.

Bottom Line

The three papers collectively tell an interesting story.

In 2012, AI in civil engineering was largely discussed as a collection of computational intelligence techniques.

By 2025, the field had expanded into computer vision, predictive analytics, construction management, structural monitoring, transportation, robotics, IoT and increasingly generative AI.

By 2026, the more important question is no longer whether AI can perform useful civil-engineering tasks.

It clearly can. The difficult question is whether those capabilities can cross the much larger boundary from task automation to professional substitution.

Current evidence does not show that they have. Contemporary reviews demonstrate broad research activity and expanding applications. Industry evidence, however, shows that large-scale organizational adoption remains limited, with skills, integration, data, cost and governance acting as major constraints.

The distinction can be stated simply:

AI can automate parts of civil engineering without automating civil engineering itself.

That distinction is not semantic.

Civil engineering is an end-to-end responsibility chain:

observe → model → predict → decide → design → construct → inspect → verify → accept → maintain → assume responsibility.

AI is becoming increasingly capable across several of those stages. It has not demonstrated that it can independently own the entire chain. And that is the central scientific weakness behind the popular “AI will take over civil engineering” narrative. The strongest evidence today supports augmentation, selective automation and changing professional roles. It does not support a scientifically established forecast of near-term wholesale replacement of civil engineering professionals.

The likely future is therefore not:

Human engineer → replaced by AI

but:

Human engineer → AI-assisted engineer → increasingly automated engineering workflow → human responsibility retained where physical uncertainty, safety, judgment and accountability remain irreducible.

AI has already demonstrated that it can perform parts of civil engineering; the evidence has not demonstrated that it can independently replace the profession that remains responsible for deciding whether those computational outputs are physically, professionally and safely acceptable.

That is a much less sensational conclusion. It is also the conclusion the current evidence can actually support.

References

Lu, P., Chen, S., & Zheng, Y. (2012). Artificial Intelligence in Civil Engineering. Mathematical Problems in Engineering, 2012, Article 145974.
Foundational review of AI applications in civil engineering, including neural networks, fuzzy systems, evolutionary computation, expert systems, classification and learning.
Publisher / DOI page
Rafindadi, A. D., Kado, B., Gora, A. M., Dalha, I. B., & Aliyu, M. M. (2025). Artificial Intelligence’s (AI) Role in Civil Engineering: A Brief Overview. 3rd International Civil Engineering Conference (ICEC 2025).
Useful recent overview of AI applications and adoption challenges in civil engineering.
Available paper record
Iordan, D., & Stan, F. A. (2026). How are AI driven programs used in civil engineering and will those fully take over this industry in the near future? International Journal of Sciences and Innovation Engineering, 3(8).
One of the principal works critically examined in this CLAC360 review. Use the DOI supplied with the paper as the bibliographic reference, while noting in the essay that the paper’s full evidentiary basis should be independently verifiable before drawing strong conclusions.
Royal Institution of Chartered Surveyors (RICS). (2025). Artificial Intelligence in Construction.
Particularly important for testing claims about actual industry adoption, rather than AI capability alone. The report draws on responses from more than 2,200 professionals worldwide and reports that widespread organisational AI deployment remained limited in 2025.
RICS — Artificial Intelligence in Construction
Royal Institution of Chartered Surveyors (RICS). (2025). Responsible Use of AI in Surveying Practice.
Useful for the professional-practice dimension of the review: responsible use, oversight, professional responsibilities and governance of AI in the built environment. RICS identifies this as its first global standard addressing responsible AI use in surveying practice.
RICS — Responsible use of AI in surveying practice

AI in Civil Engineering: Can Artificial Intelligence Really Replace the Engineer?

What the Evidence Actually Says About AI, Engineering Practice, and the Possibility of Replacement

Table of Contents

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Scope and Editorial Note

Research Paper Information Boxes

Paper 1 — Iordan & Stan

Paper 2 — Lu, Chen & Zheng

Paper 3 — Rafindadi et al.

The Three Studies in One Minute

Paper 1

Paper 2

Paper 3

What Are the Studies Actually Claiming?

Paper 1

Paper 2

Paper 3

Descriptive claims

Performance claims

Forecasting claims

Strongest implicit claim

Research Design

Paper 1

Paper 2

Paper 3

Data and Sample

Paper 1

Paper 2

Paper 3

Methodology

Paper 1

Paper 2

Paper 3

Statistical Analysis

Paper 1

Paper 2

Paper 3

Association, Causation, Mechanism and Generalizability

Association

Causation

Mechanism

Generalizability

Evidence Strength

What the Studies Get Right

Paper 1

Paper 2

Paper 3

Where the Studies Are Weak

Paper 1

Paper 2

Paper 3

1. Heterogeneous evidence

2. Performance language is sometimes too broad

3. Benchmark-to-field gap

4. Forecasting exceeds evidence

5. Limited treatment of engineering liability

What the Studies Do NOT Establish

The Strongest Counterevidence

Reproducibility and Robustness

Paper 1

Paper 2

Paper 3

What Has Changed Since Publication?

Since 2012

Since 2025

Current Scientific Status

Paper 1

Paper 2

Paper 3

CLAC360 Scientific Assessment

Why the ratings differ

Practical Implications

Engineering

Industry

Policy

Technology

Research

Who Should Read These Papers?

Paper 1